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Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data ☆
Abstract
Recent studies have shown that the greenness index derived from digital camera imagery has high spatial and temporal resolution. These findings indicate that it can not only provide a reasonable characterization of canopy seasonal variation but also make it possible to optimize ecological models. To examine this possi-bility, we evaluated the application of digital camera imagery for monitoring winter wheat phenology and modeling gross primary production (GPP). By combining the data for the green cover fraction and for GPP, we first compared 2 different indices (the ratio greenness index (green-to-red ratio, G/R) and the relative greenness index (green to sum value, G%)) extracted from digital images obtained repeatedly over time and confirmed that G/R was best suited for tracking canopy status. Second, the key phenological stages were estimated using a time series of G/R values. The mean difference between the observed phenological dates and the dates determined from field data was 3.3 days in 2011 and 4 days in 2012, suggesting that digital camera imagery can provide high-quality ground phenological data. Furthermore, we attempted to use the data (greenness index and meteorological data in 2011) to optimize a light use efficiency (LUE) model and to use the optimal parameters to simulate the daily GPP in 2012. A high correlation (R 2 = 0.90) was found between the values of LUE-based GPP and eddy covariance (EC) tower-based GPP, showing that the greenness index and meteorological data can be used to predict the daily GPP. This finding provides a new method for interpolating GPP data and an approach to the estimation of the temporal and spatial distributions of photosynthetic productivity. In this study, we expanded the potential use of the greenness index derived from digital camera imagery by combining it with the LUE model in an analysis of well-managed cropland. The successful application of digital camera imagery will improve our knowledge of ecosystem processes at the temporal and spatial levels.
... Repeat images from unattended in situ digital cameras or webcams (Richardson et al. 2007;Ahrends et al. 2008;Ide and Oguma 2010;Migliavacca et al. 2011;Sonnentag et al. 2012;Mizunuma et al. 2013;Inoue et al. 2015;Toomey et al. 2015;Wingate et al. 2015) have been used to track vegetation phenology in a range of ecosystems at a fine temporal resolution (daily to sub-daily) averaged over a greater spatial extent (10-100's of metres) than traditional, manual, methods. Vegetation products, based on the relative reflectance of green to red and/or blue in these digital images, are at a temporal and spatial scale where they can be related directly to measured CO 2 fluxes (Botta et al. 2000;Knorr et al. 2010;Migliavacca et al. 2011;Saitoh et al. 2012;Mizunuma et al. 2013;Westergaard-Nielsen et al. 2013;Zhou et al. 2013;Toomey et al. 2015). However, this method is site specific without standard protocols (Ide and Oguma 2010;Migliavacca et al. 2011) and is not straightforward in isolated upland areas such as peatlands. ...
... GRVI (Tucker 1979) was selected for this study as it does not use the blue band and is therefore less sensitive to variation in illumination conditions. This is in keeping with Zhou et al. (2013) who found the ratio of green to red better suited to monitor winter wheat phenology than relative brightness of green. ...
... It is known that seasonal variation in physiological pigments as well as leaf area, both measurable by digital camera vegetation products, results in variation in photosynthesis (Muraoka and Koizumi 2005;Migliavacca et al. 2011;Saitoh et al. 2012;Mizunuma et al. 2013;Westergaard-Nielsen et al. 2013;Zhou et al. 2013;Toomey et al. 2015). This study investigated if MODIS vegetation products could also identify this seasonal variation. ...
In peatlands plant growth and senescence affect annual ecosystem carbon dioxide (CO2) exchange, and CO2 fluxes show considerable inter-annual variability. Phenology is a fundamental indicator of ecosystem carbon dynamics and can be measured from remote sensing systems, but the extent to which satellite products provide useful proxies of peatland vegetation phenology is not well known. Using MODIS vegetation products coupled with field observations of phenology from a basic camera system and measurements of Gross Primary Productivity (GPP) measured using a closed chamber method, we sought to establish the extent to which satellite observations capture phenological processes at a UK peatland site. Daily, true-colour digital images were captured with a time-lapse camera (Brinno TLC100) between 23-Apr-2013 and 29-Oct-2013 and converted into a Green-Red Vegetation Index (GRVI). These were compared with a range of MODIS vegetation products at various spatial resolutions. We found that vegetation products with finer spatial resolution (<500 m) more accurately captured spring green-up (e.g. Normalized Difference Vegetation Index 16-day product), whereas those with 8-day temporal resolution better captured whole-season dynamics. The 8-day Gross Primary Productivity (GPP8) and the fraction of absorbed photosynthetically active radiation (fPAR8) products had the strongest daily Pearson's correlations with camera-derived GRVI (r > 0.90). The camera-GRVI (P = 0.005, r = 0.98) and MODIS-GRVI (P = 0.041, r = 0.89) products showed the strongest significant correlations with gross primary productivity measured using static chambers in the field. This work demonstrates that freely available MODIS data captured up to 92% of the daily variation in phenology over an upland peatland. This approach shows great potential for capturing ecosystem carbon dynamics which underpin carbon trading schemes, a budding funding source for peatland restoration projects.
... In recent decades, the productivity of field crops has been found to be affected by the ongoing climatic changes [e.g., Lobell and Asner, 2003;Peng et al., 2004;Semenov, 2009;Wang et al., 2012], and the timings of seasonal crop activities have shifted with the changing climate as well [e.g., Craufurd and Wheeler, 2009;Wang et al., 2008;Sacks et al., 2010]. Monitoring the phenological changes in croplands could enhance our understanding of the adaptation of crops to their environments [Tao et al., 2009;Salazar-Gutierrez et al., 2013] and help regulate the dates for timely crop management [Mirjana and Vulić, 2005;Zhou et al., 2013]. Additionally, phenological information is critical to the development of dynamic crop simulations [e.g., He et al., 2012], and it improves the prediction of sustainability and quality of crop production [Saiyed et al., 2009]. ...
... In China, the cropping system for winter wheat usually includes a rotation of winter wheat and summer maize/cotton. Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013;Lu et al., 2014]. ...
... Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013;Lu et al., 2014]. After it is sown, winter wheat begins to grow until temperature drops in winter, when it enters a dormant stage. ...
Monitoring crop phenology has become a growing concern for food security. Crop phenology can be traditionally observed at plot scale in the field or recently at a much larger scale by satellites. In this study, we compared the spring phenology of winter wheat (Triticum sp.), quantified as the timing of start-of-spring-season (SOS), using 8 km resolution satellite data and ground observations at 112 agrometeorological stations across China from 1993 to 2008. We found that ground and satellite observations displayed opposing trends in winter wheat SOS. Ground observation exhibited a delayed onset of SOS at 86% of ground stations, whereas satellite data suggested an earlier arrival of SOS at 78% of stations. The meteorological SOS calculated from daily air temperature supported the earlier occurrence of SOS indicated by satellite data. Moreover, satellite data showed more agreement with meteorological data with respect to interannual SOS variations than did field phenology records. Given the dominant control of air temperature on winter wheat's spring phenology, satellite observation provides a reliable measure of the long-term trends and dynamics of SOS. Ground-observed SOS trends were impaired by data heterogeneity and limited spatial coverage. However, compared with ground observations, satellite-derived phenological timings are often lack of biological meanings. Therefore, integrating ground and satellite observations could enhance the monitoring of winter wheat SOS, which would increase the knowledge of vegetation's response to the changing climate and help to optimize timely crop management.
... In recent decades, the productivity of field crops has been found to be affected by the ongoing climatic changes [e.g., Lobell and Asner, 2003; Peng et al., 2004; Semenov, 2009; Wang et al., 2012], and the timings of seasonal crop activities have shifted with the changing climate as well [e.g., Craufurd and Wheeler, 2009; Wang et al., 2008; Sacks et al., 2010]. Monitoring the phenological changes in croplands could enhance our understanding of the adaptation of crops to their environments Salazar-Gutierrez et al., 2013] and help regulate the dates for timely crop management [Mirjana and Vulić, 2005; Zhou et al., 2013] . Additionally, phenological information is critical to the development of dynamic crop simulations [e.g., He et al., 2012], and it improves the prediction of sustainability and quality of crop production [Saiyed et al., 2009]. ...
... In China, the cropping system for winter wheat usually includes a rotation of winter wheat and summer maize/cotton. Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. ...
... Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. After it is sown, winter wheat begins to grow until temperature drops in winter, when it enters a dormant stage. ...
... In recent decades, the productivity of field crops has been found to be affected by the ongoing climatic changes [e.g., Lobell and Asner, 2003; Peng et al., 2004; Semenov, 2009; Wang et al., 2012], and the timings of seasonal crop activities have shifted with the changing climate as well [e.g., Craufurd and Wheeler, 2009; Wang et al., 2008; Sacks et al., 2010]. Monitoring the phenological changes in croplands could enhance our understanding of the adaptation of crops to their environments Salazar-Gutierrez et al., 2013] and help regulate the dates for timely crop management [Mirjana and Vulić, 2005; Zhou et al., 2013] . Additionally, phenological information is critical to the development of dynamic crop simulations [e.g., He et al., 2012], and it improves the prediction of sustainability and quality of crop production [Saiyed et al., 2009]. ...
... In China, the cropping system for winter wheat usually includes a rotation of winter wheat and summer maize/cotton. Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. ...
... Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. After it is sown, winter wheat begins to grow until temperature drops in winter, when it enters a dormant stage. ...
... In recent decades, the productivity of field crops has been found to be affected by the ongoing climatic changes [e.g., Lobell and Asner, 2003; Peng et al., 2004; Semenov, 2009; Wang et al., 2012], and the timings of seasonal crop activities have shifted with the changing climate as well [e.g., Craufurd and Wheeler, 2009; Wang et al., 2008; Sacks et al., 2010]. Monitoring the phenological changes in croplands could enhance our understanding of the adaptation of crops to their environments Salazar-Gutierrez et al., 2013] and help regulate the dates for timely crop management [Mirjana and Vulić, 2005; Zhou et al., 2013] . Additionally, phenological information is critical to the development of dynamic crop simulations [e.g., He et al., 2012], and it improves the prediction of sustainability and quality of crop production [Saiyed et al., 2009]. ...
... In China, the cropping system for winter wheat usually includes a rotation of winter wheat and summer maize/cotton. Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. ...
... Winter wheat is often sown in late September to early October and harvested early next summer [Zhou et al., 2013]. Winter wheat's entire life cycle spans approximately 230 to 260 days [Zhou et al., 2013; Lu et al., 2014]. After it is sown, winter wheat begins to grow until temperature drops in winter, when it enters a dormant stage. ...
... images (Nagai et al., 2011;Richardson et al., 2007;Sonnentag et al., 2012;Zhao et al., 2012;Zhou et al., 2013). ...
... Non-normalized RGB coordinates tend to present considerable variation between images, specially related to light variation in shaded and not shaded surfaces (Woebbecke et al., 1995). Zhou et al. (2013) tested non-normalized values for the RGB color channels (RGBDN) and the indices fail to capture any seasonal change in the development of the canopy of winter wheat cultivated area. ...
... The Excess green (2G − R − B) is a commonly applied contrast index to highlight the green information and complement phenological interpretation in several studies (e.g., Kurc and Benton (2010); Migliavacca et al. (2011);Nagai et al. (2011);Sonnentag et al. (2012)). The G/R, calculated on the basis of the difference of absorptive/reflective bands, corresponding to the vegetation canopy and soil surface, provides effective vegetative information for leaf color changes in Zhou et al. (2013). Other examples of contrast indices are R − G, G − B, and (G − B)/(R − G) (Woebbecke et al., 1995). ...
Plant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterizing plant species phenology.
... As a key phenological phase, the spring green-up date (GUD), defined as the start time of photosynthetic activity of vegetation, is mainly controlled by climatic factors (Badeck et al., 2004;Khare et al., 2017;Richardson et al., 2006). For agriculture crops, GUD specifically refers to the time when the new leaves begin to grow after the winter growthbreak (Zhou et al., 2013). With significant global warming, numerous studies have reported that crop GUD has showed an advancing trend over the past several decades (Estrella et al., 2007;Oteros et al., 2015). ...
... Traditionally, phenological information can be obtained through ground observation following predefined criteria (Boschetti et al., 2009;McMaster and Smika, 1998). Although the ground observation has the advantages of high precision and high time frequency, it is difficult to obtain the continuous phenological information in a large area due to its time-consuming and labor-intensive nature (Guo et al., 2016;Zhou et al., 2013). Remote sensing technology provides an alternative tool to investigate the spatial and temporal dynamics of phenology at regional or global scale (Chu et al., 2016;Cleland et al., 2007). ...
Satellite vegetation index (VI) time series data provide a feasible option for monitoring crop phenology at a large scale. However, there are limited researches that investigated the accuracy of different methods for crop phenology detection with various VIs over a large-scale region. In this study, we used four VIs, i.e. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Two-band Enhanced Vegetation Index (EVI2), and Normalized Difference Phenology Index (NDPI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, combined with six methods, i.e. relative threshold at 10%, 20% or 50% of the VI's amplitude (RT10, RT20 and RT50), maxima of the curvature change rate of the fitted logistic curve (CCRmax), maxima of the first derivation of the VI curve (βmax), and cross-correlogram spectral matching-phenology (CCSM-P), to detect winter wheat green-up dates (GUDs) for the period of 2009–2013 in the Huanghuai winter wheat region of China. The performance of the combinations of these methods and VIs was evaluated using ground-observed GUDs from agrometeorological stations with correlation coefficient (r), regression coefficient (a), root mean square error (RMSE) and bias. We further investigated the spatial trend of residuals from a linear model between satellite- and ground-observed GUDs. Results show that NDPI outperforms the other VIs with the highest consistency with ground data in the whole region. RT10, CCRmax and CCSM-P show higher accuracy in the northern region, while in the southern region, RT20 shows relatively higher accuracy in the case of poor performance of all six methods. However, the residuals of these six methods based on NDPI show significantly positive correlations with latitude in the whole region, suggesting an uneven spatial distribution of accuracy with a tendency of underestimating GUDs at the low latitude region and overestimating GUDs at the high latitude region when applying the same method to detect GUDs over a large-scale region. It is suggested to develop a new method or combine several methods to reduce the spatial incoherence of residuals.
... Monitoring GUD changes in cropland could not only enhance our understanding of crop response mechanism to environments, but could also help in devising adaptive management strategies. Based on ground observation and crop model, a substantial number of studies explored the spatial temporal variation of winter wheat GUD in NCP and its influence factors [19][20][21][22]. For example, Zhou et al. [19] successfully applied digital camera images to monitoring winter wheat GUD in order to optimize an ecological model in Yucheng station. ...
... Based on ground observation and crop model, a substantial number of studies explored the spatial temporal variation of winter wheat GUD in NCP and its influence factors [19][20][21][22]. For example, Zhou et al. [19] successfully applied digital camera images to monitoring winter wheat GUD in order to optimize an ecological model in Yucheng station. Xiao et al. [20] and Tao, et al. [21] demonstrated that the advance of winter wheat GUD in some stations was dependent upon temperature change. ...
Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (Tm), mean maximum (Tmax) and minimum temperature (Tmin) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the Tmax and Tmin effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both Tmax and Tmin should be considered to improve the performance of spring phenology models.
... Usually, measuring and observing crops on farmland directly records phenological information. The development of digital camera technology, the ecosystem phenology camera, and a new method for near-surface remote sensing, has shown great potential for monitoring phenological events [10]. However, the sparsely distributed observational stations with or without Sentinel-1 can operate in four modes-Interferometric Wide Swath (IW), Extra Wide Swath (EW), Wave (WV), and Stripmap (SM)-with different resolutions, extents, incidence angles, and polarizations. ...
... The results indicate that inflection points both in the σ d time series and in the slope curve could be correlated with phenological metrics. Compared with the previous studies using optical remote sensing data, there were fewer atmospheric effects (e.g., cloud, haze) on the C-band SAR data, which would lead to a better and steadier temporal resolution [10,48]. In addition, it should be noted that the phenological metrics based on the analysis of backscatter time series have shown the main changes in the canopy structure (e.g., vertical structure, vegetation coverage) which are different than those in previous research based on NDVI or other vegetation indices [49,50]. ...
Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.
... GPP is also the basis for ecosystem services such as food, fuel, and wood products [2]. The ability to accurately track the spatial and temporal variability of GPP is fundamental for understanding the biogeochemical dynamics of terrestrial ecosystems [3,4]. Therefore, it is critical for us to accurately estimate GPP and further understand the trends and variations of global and regional carbon uptake. ...
... where ε max (g C m −2 d −1 MJ −1 ) is the maximum light use efficiency, which is given in a Biome Parameter Look-up Table (BPLUT) for each land cover type in the PSN model. The TMIN scalar and VPD scalar are environmental stress factors of temperature (daily minimum temperature, Tmin, • C) and water (maximum daily vapor pressure deficit, VPD, Pa) and are parameterized according to Equations (3) and (4). ...
As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP MOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP EC) from a global network of eddy covariance towers (FluxNet), we compared GPP MOD and GPP EC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP MOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R 2), and root mean square errors (RMSE). We found that GPP MOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R 2 = 0.66 (CI, 0.63-0.69), RMSE = 2.46 (2.33-2.58) g Cm −2 day −1) for the three grassland biomes. GPP MOD had varied performances with R 2 values of 0.72 (0.68-0.75) (temperate), 0.64 (0.59-0.68) (alpine), and 0.40 (0.27-0.52) (tropical). Thus, GPP MOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP MOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (ε max) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in ε max , FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.
... It directly or indirectly affects carbon, water, and energy cycling. Traditionally, observers record field GUD information by monitoring the length of the new leaves reaching 1-2 cm after winter dormancy (Zhou et al., 2013). Although these field-observed GUD records generally have higher accuracy, they cannot cover entire region or globe. ...
... Field-observed GUD data for winter wheat, from representative agro-meteorological stations, were obtained from the Chinese Meteorological Administration (CMA). According to the CMA phenology observation guide, GUD for winter wheat is defined as the date when the leaves begin to turn green after winter dormancy, and the length of the new leaves reaches 1-2 cm (Zhou et al., 2013). However, in practice, these qualitative descriptions are difficult to apply in determining GUD, because different observers and observation frequencies (generally, once a day) can markedly affect the consistency of the field observations (Guo et al., 2016). ...
Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.
... For example (Degife et al., 2021;Rettie et al., 2022), projected a decline of more than a third in the production of maize and wheat in different parts of Ethiopia, which would have an impact on the country's efforts to achieve food self-sufficiency and ensure food security. In this regard, understanding the nature of the long-term phenological response across the landscape to climate change can contribute to the adjustment of the timing of agricultural activities (Zhou et al., 2013;Ruml and Vulic, 2005). Furthermore, spatial and temporal variations in phenology across the different land use and land cover (LULC) in different agroecological zones are essential to gain valuable insight into vegetation response to climate change (Hwang et al., 2011). ...
Land surface phenology (LSP) is a crucial indicator of climate change and its impact on ecosystems. Therefore,
this study was carried out to assess the spatiotemporal variations in LSP and its response to climate change across
the agroecological zones (AEZs) of the upper Gelana watershed in the northeastern highlands of Ethiopia. The
LSP metrics were derived from MODIS NDVI data using TIMESAT v3.3, and trends as well as correlations were
analyzed using the statistical programming language R. The results indicate that the dega AEZ exhibits an earlier
start of the season (SOS) and a longer length of the season (LOS) compared to the lower and upper weina dega
(LWD and UWD) AEZs. A delay in SOS and end of season (EOS) was observed in 71.3% and 82% of the study
area, respectively, while LOS increased in nearly half of the area. There is a positive correlation between SOS and
maximum temperature, and a negative correlation with belg season rainfall and drought indices in large parts of
the study area. Similarly, EOS exhibits a direct association with kiremt season maximum temperature, rainfall,
and drought indices. Furthermore, a shorter LOS is associated with a higher annual maximum temperature, while
a longer LOS is associated with the increasing trend in annual rainfall. These findings will help raise awareness
on climate change adaptation activities, including crop diversification, alteration of planting dates, soil conservation,
water harvesting and irrigation, particularly within rural communities of the study area that rely
heavily rely on rainfed agriculture.
... These models include the Carnegie-Ames-Stanford Approach (Potter et al., 1993), the MOD17 algorithm (Running et al., 2000), Vegetation Photosynthesis Model (Xiao et al., 2004), Eddy Covariance (EC)-LUE model (Yuan et al., 2007), two-leaf (TL)-LUE model , Multiple TL-LUE model (Xie and Li, 2020), and Radiation scalar TL-LUE model (Guan et al., 2021). The LUE model has been widely used to estimate the GPP of terrestrial ecosystems with various levels of accuracy Pei et al., 2020;Zhao et al., 2023;Zhou et al., 2013). The uncertainty in these models stems from variations in maximum LUE as well as the complexity of the compositions of vegetation . ...
Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as "observed GPP") retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R 2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m − 2 d − 1) and 0.83 (1.49 gC m − 2 d − 1), respectively, which increased (decreased) by 0.17-0.03 (0.58-0.12 gC m − 2 d − 1) and 0.11-0.02 (0.39-0.10 gC m − 2 d − 1) compared with the R 2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R 2 by 0.33 and decreasing RMSE by 0.97 gC m − 2 d − 1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.
... The calibrated constant values of key photosynthetic parameters, such as VCMX25, in this study, were more appropriate for the early and late growing stages when photosynthesis is weak, but they caused underestimations during the middle growing stage when photosynthesis is strong. Similar findings are reported in the research of Zhou et al. (2013), Lin et al. (2017) and Xu et al. (2024). There is a high demand for temporally varying critical photosynthetic parameter values to accurately track crop dynamic growth Zhang et al., 2020). ...
Detailed sowing and harvesting (S&H) information is crucial for climate-coupled crop models to accurately simulate dynamic crop growth. The timing of intra-annual crop growth not only reflects adaptation to climate change but also significantly influences terrestrial biophysical and biochemical processes, as well as the local climate. However, accurately providing sowing and harvesting dates in crop models is challenging due to the limited availability of S&H observations worldwide. In this study, we integrated a prognostic S&H scheme into the Noah-MP-Crop model to eliminate the need for prescribed S&H dates and optimised key crop-related parameters to better reproduce dynamic maize and soybean growth in the U.S. Corn Belt. Results indicated that the bias in estimating site-level S&H dates was within one week. The prognostic S&H schemes, along with optimised crop-related parameters, effectively captured maize and soybean growth at the site scale, as evidenced by leaf area index (LAI) and gross primary production (GPP) simulations. The determination coefficient (R 2) for GPP ranged from 0.88 to 0.91 for maize and from 0.70 to 0.82 for soybean at two flux stations. Moreover, the prognostic schemes exhibited better regional LAI and GPP simulations at the beginning and end of the growing season compared to those using state-level median S&H dates, with significant improvements in correlation coefficients ranging from 0 to 0.6, particularly in maize-dominated regions. However, the accuracy in reproducing latent heat flux and sensible heat flux was less satisfactory and showed little association with crop growth status. This work provides an alternative approach to obtaining crop sowing and harvesting information in the Noah-MP-Crop model and facilitates studies on interactions between dynamic crop growth and the climate system, particularly when coupled with the widely used Weather Research and Forecasting (WRF) model.
... Normalized Difference Vegetation Index NDVI (NIR − R)/(NIR + R) [27] Coloration Index CI (R − B)/R [32] Normalized Pigment Chlorophyll ratio Index NPCI (R − B)/(R + B) [33] Green Chlorophyll Vegetation Index GCVI NIR/G − 1 [34] Greenness Index GI G/R [35] Triangular Vegetation Index ...
Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R²) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.
... Because canopy color derived from phenocam imagery depends on both the leaf amount and the color of individual leaves, it reflects both physiological and structural characteristics of the canopy, such as leaf area index (LAI), leaf chlorophyll and carotenoid concentrations [12][13][14][15][16][17], which are key components of canopy photosynthesis and transpiration. Therefore, phenocams have been installed in many flux sites in various ecosystems, such as temperate and boreal forests, savannas, and grasslands, and time-series data of canopy greenness or redness have been used to analyze seasonal changes in ecosystem gas flux, especially for CO 2 , e.g., [9,10,[18][19][20][21]. ...
Understanding the relationship between leaf phenology and physiological properties has important implications for improving ecosystem models of biogeochemical cycling. However, previous studies have investigated such relationships only at the ecosystem level, limiting the biological interpretation and application of the observed relationships due to the complex vegetation structure of forest ecosystems. Additionally, studies focusing on transpiration are generally limited compared to those on photosynthesis. Thus, we investigated the relationship between stem sap flux density (SFD) and crown leaf phenology at the individual tree level using the heat dissipation method, unmanned aerial vehicle (UAV)-based observation, and ground-based visual observation across 17 species in a cool temperate forest in Japan, and assessed the potential of UAV-derived phenological metrics to track individual tree-level sap flow phenology. We computed five leaf phenological metrics (four from UAV imagery and one from ground observations) and evaluated the consistency of seasonality between the phenological metrics and SFD using Bayesian modelling. Although seasonal trajectories of the leaf phenological metrics differed markedly among the species, the daytime total SFD (SFDday) estimated by the phenological metrics was significantly correlated with the measured ones across the species, irrespective of the type of metric. Crown leaf cover derived from ground observations (CLCground) showed the highest ability to predict SFDday, suggesting that the seasonality of leaf amount rather than leaf color plays a predominant role in sap flow phenology in this ecosystem. Among the UAV metrics, Hue had a superior ability to predict SFDday compared with the other metrics because it showed seasonality similar to CLCground. However, all leaf phenological metrics showed earlier spring increases than did sap flow in more than half of the individuals. Our study revealed that UAV metrics could be used as predictors of sap flow phenology for deciduous species in cool, temperate forests. However, for a more accurate prediction, phenological metrics representing the spring development of sap flow must be explored.
... The results showed that VH/VV polarization coefficient ratio time series and slope curves strongly correlate with rice phenological indexes. Compared with the previous optical remote sensing image data, SAR data is less affected by the atmosphere, which ensuring the availability and applicability of the data (Zhou, L., et al., 2013;Pan, Y., et al., 2017). ...
As the core issues of agricultural remote sensing, the mapping of crop planting and dynamic monitoring of crop growth in the urban area have always been limited by complex climatic conditions that induced optical image shortages. Considering that the backscattering signals of SAR image data are sensitive to crop phenological period, the paper presents a fully convolutional neural network (FCN) model to realize the wide-area planting thematic mapping at the pixel level. Taking Fujin City of Heilongjiang province in China as a specific study area, we selected rice as the typical research object and obtained urban-scale rice planting thematic mapping using Sentinel-1A image data derived from April to October 2020. Based on the proposed FCN classification method, the rice extraction precision is up to 95.7%, and the kappa coefficient is 0.90. Comparative analysis indicated that the proposed model is superior to the traditional random forest classification model in extraction precision and kappa coefficient. Further analysis showed that the rice growth cycle in Fujin City was consistent with the time series of VH/VV polarization coefficient ratio and had prominent phenological cycle characteristics. Relevant research data and results proved that high-precision planting thematic mapping at an urban scale could be achieved based on time series SAR image data sets. And the proposed FCN classification model is helping to dynamic monitoring of crop growth and related phenological research.
... Recently, phenocam data have been coupled with meteorological indices to model GPP of terrestrial ecosystems, including grasslands (Migliavacca et al., 2011), wetlands (Knox et al., 2017;Westergaard-Nielsen et al., 2013), savannas (Moore et al., 2017), and agro-ecosystems (Sakamoto et al., 2012;Zhou et al., 2013). These studies have used derivatives of a light use efficiency model proposed by Monteith (1972) to calculate GPP, such as the model from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform (Running et al., 2004), which relate vegetation GPP to the amount of radiation absorbed by vegetation. ...
Although drylands cover >40% of the land surface, models of ecosystem gross primary productivity (GPP) generally have been designed for mesic temperate ecosystems. Arguably, GPP models often lack a good representation of vegetation phenology, particularly not estimating the ecosystem effects of the prolonged foliage senescence which may be common in drylands. To estimate daily GPP for a water‐limited Mediterranean shrubland, we propose a simple framework (GPPmod) using light use efficiency, a spectral vegetation index derived from digital cameras, and five meteorological variables, including an index of functional senescence of foliage (i.e., heat degree‐days). We tested the model with different combinations of meteorological variables but without senescence, using 1 year's data. The best formulation showed good agreement with GPP derived from eddy covariance (GPPEC; r² = 0.53, RMSE = 0.77). However, including the foliage senescence parameter significantly improved model performance (r² = 0.74, RMSE = 0.49), especially during the fall season. In the following year, we validated the parameters: The overall GPPmod and GPPEC comparison yielded an r² = 0.78. We postulate that models that mainly rely on meteorological variables or greenness indices could yield an overestimation of annual GPP between 24% and 90%, while models including the foliage senescence parameter reduced that bias by 10% to 34%. Our results highlight the importance of incorporating the phenology of foliage senescence in models regarding productivity in drylands or dry sclerophyll ecosystems.
... As a low-cost modeling method, PhenoCams could be applied widely across different vegetation types. Studies have shown that vegetation indices retrieved from PhenoCams possess great potential for GPP modeling and can be used as surrogates for the fraction of absorbed photosynthetically active radiation (fAPAR), displaying good agreement with flux measurements (Migliavacca et al., 2011;Saitoh et al., 2012;Nagai et al., 2013;Westergaard-Nielsen et al., 2013;Zhou et al., 2013;Toomey et al., 2015;Knox et al., 2017). The PhenoCam is not a calibrated instrument, however. ...
The accurate estimation of temporally-continuous gross primary production (GPP) is important for a mechanistic understanding of the global carbon budget, as well as the carbon exchange between land and atmosphere. Ground-based PhenoCams can provide near-surface observations of plant phenology with high temporal resolution and possess great potential for use in modeling the seasonal dynamics of GPP. However, due to the site-level empirical approaches for estimating the fraction of absorbed photosynthetically active radiation (fAPAR), a broad application of PhenoCams in GPP modeling has been restricted. In this study, the stage of vegetation phenology (Pscalar) is proposed, which is calculated from the excess green index (ExGI) derived from PhenoCam data. We integrate Pscalar with the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to generate a daily time-series of the fAPAR (fAPARCAM), and then to estimate daily GPP (GPPCAM) with a light use efficiency model in a semi-arid grassland area from 2012 to 2014. Over the three continuous years, the daily fAPARCAM exhibited similar temporal behavior to the eddy covariance–measured GPP (GPPEC), and the overall determination coefficients (R²) were all > 0.81. GPPCAM agreed well with GPPEC, and these agreements were highly statistically significant (p < 0.01); R² varied from 0.80 to 0.87, the relative error (RE) varied from -2.9% to 2.81%, and the root mean square error (RMSE) ranged from 0.83 to 0.98 gC/m²/d. GPPCAM was then resampled to 8-day temporal resolution (GPPCAM8d), and further evaluated by comparisons with MODIS GPP products (GPPMOD17) and vegetation photosynthesis model (VPM)–derived GPP (GPPVPM). Validation revealed that the variance explained by GPPCAM8d was still the greatest among these three GPP products. The RMSE and RE of GPPCAM8d were also lower than those of the other two GPP products. The explanatory power of predictors in GPP modeling was also explored; the fAPAR was found to be the most influential predictor, followed by photosynthetically active radiation (PAR). The contributions of the environmental stress indices of temperature and water (Tscalar and Wscalar, respectively) were less than that of PAR. These results highlight the potential for PhenoCam images in high temporal resolution GPP modeling. Our GPP modeling method will help reduce uncertainties by using PhenoCam images for monitoring the seasonal development of vegetation production.
... Although the idea of repeat photography to study environmental change goes back a century (Stephens et al., 1987;Turner, 2003), using digital repeat photography has become increasingly popular to monitor and study the environment for a diverse range of applications such as studying plant phenology (Berra et al., 2019;de Moura et al., 2017;Olivera-Guerra et al., 2017;Richardson et al., 2018b;Sonnentag et al., 2012;Watson et al., 2019;Yan et al., 2019), assessing the seasonality of gross primary production (Crimmins and Crimmins, 2008;Migliavacca et al., 2011;Yuan et al., 2018), salt marsh restoration (Knox et al., 2017), monitoring tidal wetlands (O'Connell and Alber, 2016), investigating growth in croplands (Liu and Pattey, 2010;Zhou et al., 2013), and evaluating phenological data products derived from satellite remote sensing (Richardson et al., 2018c;Seyednasrollah et al., 2018). However, extracting "clean" and high quality data from a large set of images often presents three main challenges: (a) delineating region of interests (ROI) (Richardson et al., 2018a), (b) computational costs (Filippa et al., 2016a); and (c) handling expected and unexpected field of view (FOV) shifts Moore et al., 2016). ...
Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process
... Therefore, we define the sample size as ROIs from crowns of several species, a population, a portion of the canopy, a community profile, or a habitat or vegetation type in a heterogeneous landscape (Fig. S1). Several indexes have been applied to detect leaf color changes in time series of digital images exploring the RGB color channels (Richardson et al., 2007;Nagai et al., 2011;Sonnentag et al., 2012;Zhao et al., 2012;Zhou et al., 2013). Woebbecke et al. (1995) was one of the first to calculate several indexes using RGB channels of digital images to evaluate which are better to detect weeds considering different types of soil, residue, and light conditions. ...
The application of digital cameras to monitor the environment is becoming global and changing the way of phenological data collection. The technique of repeated digital photographs to monitor plant phenology (phenocams) has increased due to its low-cost investment, reduced size, easy set up installation, and the possibility of handling high-resolution near-remote data. Considering the widespread use of phenocams worldwide, our main goals here are: (i) to provide a step-by-step guide for phenocam set up in the tropics, reinforce its appliance as an efficient tool for monitoring tropical phenology and foster networking, (ii) to discuss phenocam applications for biological conservation, management, and ecological restoration. We provide the concepts and properties for image analysis which allow representing the phenological status of the vegetation. The association of a long-term imagery data with local sensors (e.g., meteorological stations and surface-atmosphere flux towers) allows a wide range of studies, especially linking phenological patterns to climatic drivers; and the impact of climate changes on plant responses. We show phenocams applications for conservation as to document disturbances and changes on vegetation structure, such as deforestation, fire events, and flooding and the vegetation recovery. Networks of phenocams are growing globally and represent an important tool for conservation and restoration, as it provides hourly to daily information of monitored systems spread over several sites, ecosystems, and climatic zones. Moreover, websites enriched by vegetation dynamic imagery data can promote science knowledge by engaging citizen science participation.
... Therefore, we define the sample size as ROIs from crowns of several species, a popula- tion, a portion of the canopy, a community profile, or a habitat or vegetation type in a heterogeneous landscape (Fig. S1). Several indexes have been applied to detect leaf color changes in time series of digital images exploring the RGB color channels ( Richardson et al., 2007;Nagai et al., 2011;Sonnentag et al., 2012;Zhao et al., 2012;Zhou et al., 2013). Woebbecke et al. (1995) was one of the first to calculate several indexes using RGB channels of digi- tal images to evaluate which are better to detect weeds considering different types of soil, residue, and light conditions. ...
... Winter wheat is often sown in the late September to early October and harvested in the next early summer. In practice, the arrival of winter wheat SOS is defined as the date when the wheat leaves begin to turn green after winter dormancy [49]. The field records of winter wheat SOS were collected by well-trained agricultural technicians starting in 1993 under the guidance of the Phenology Observation Guide released by the National Meteorological Information Center (NMIC) of China Meteorological Administration (CMA) [50]. ...
Remote sensing is a valuable way to retrieve spatially continuous information on vegetation phenological changes, which are widely used as an indicator of climate change. We propose a simple method called weighted cross-correlogram spectral matching—phenology (CCSM-P), which combines CCSM and a weighted correlation system, for detecting vegetation phenological changes by using multiyear vegetation index (VI) time series. In experiments with simulated enhanced VI (EVI) for various scenarios, CCSM-P exhibited high accuracy and robustness to noise and the potential to capture long-term phenological change trends. For a temperate grassland in northern China, CCSM-P retrieved more reasonable vegetation spring phenology from Moderate Resolution Imaging Spectroradiometer (MODIS) EVI images than the MODIS phenology product (MCD12Q2). When validated against field phenological observations in five of the AmeriFlux Network sites in the U.S. (four deciduous broadleaf forest sites and a closed shrublands site), and a cropland site in China, CCSM-P exhibited mean absolute differences (MADs) ranging from 2 to 10 days (median: 4.2 days), whereas MAD of non-CCSM methods showed larger variations, ranging from 5 to 58 days (median: 21.3 days). This is because CCSM-P integrates field phenological observations. Compared with non-CCSM methods, which are widely used to identify phenological events, CCSM-P is more accurate and less dependent on prior knowledge (thresholds or predefined functions), which indicates its effectiveness and applicability for detecting year-to-year variations and long-term change trends in phenology, and should facilitate more reliable assessments of phenological changes in climate change studies.
... However, the practical integration of digital imaging as a monitoring tool needs some considerations. Among them, that the image quality from different commercial cameras can vary and may result in different information for the same canopy, and also the existence of exposure problems due to the changing in the light intensity over time, which requires a radiometric calibration of digital numbers into reflectance, which is frequently not easy (Gates, 1980;Hunt et al., 2005;Kipp et al., 2014;Zhou et al., 2013). In our study we did not do direct calibrations for the Green and Red bands of the digital RGB camera because the wavelengths of these bands were not supported by the manufacturer of the digital camera. ...
... Among them, that the image quality from different commercial cameras can vary and may result in different information for the same canopy (Li et al., 2010). Also, the existence of exposure problems due to changes in light intensity over time under field conditions, which require a radiometric calibration of digital numbers into reflectance, a task that is frequently not easy (Gates, 1980;Hunt et al., 2005;Kipp et al., 2014;Zhou et al., 2013). In our study we did not undertake direct radiometric calibration for the Green and Red channels of the digital RGB camera because the wavelengths of these bands were not supported by the manufacturer of the digital camera. ...
Improving durum wheat performance to abiotic stresses is often limited by a lack of proper monitoring methods in support of crop management and efficient phenotyping tools for breeding. The objectives of this study were: (1) comparing the performance under contrasting water treatments of different physiological traits, which evaluate plant growth and water status; and (2) understanding how these traits can predict grain yield (GY) performance under contrasting water conditions. Thus, five modern durum wheat genotypes were subjected to rainfed (RF) and supplemental irrigation (SI) treatments. Two categories of physiological traits were tested; (1) the vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Green Red Difference Index (NGRDI); and (2) the stable carbon and oxygen isotope compositions (δ13C and δ18O) of different plant parts. The NGRDI at anthesis and the δ13C of mature grains were the traits best correlated (positively and negatively, respectively) with GY. Both traits in combination explained at least 50% of variability in GY within each water treatment. The produced path models for RF and SI conditions highlighted the particular role of NGRDI and δ13C in predicting GY. In addition, the study showed the potential of using vegetation indices derived from digital Red-Green-Blue (RGB) images as a low-cost technique for assessing aerial biomass (AB) and GY under different water availabilities.
Current, how to use limited water resources efficiently and improve agricultural water use efficiency, has become one of the greatest challenges for global food security. In this study, multiple site-years of carbon and water flux data across the major crops including maize, winter wheat and soybean, were used to quantify the variability in canopy-scale transpiration (T), ecosystem-scale evapotranspiration (ET) as well as the associated water use efficiencies (WUET and WUEET). On the basis of ET partitioning, the results indicated that the transpiration ratio–T/ET as well as T and ET exhibited an obvious single-peak seasonal pattern across the typical croplands. However, at the early and late growing stages, there existed large discrepancies in T and ET owing to low vegetation coverage, while T and ET were very close during the peak period. Among them, maize exhibited the largest T/ET by 0.50 ± 0.12, followed by soybean of 0.43 ± 0.08 and winter wheat of 0.38 ± 0.09, respectively. Furthermore, the coupling relationships between gross primary productivity (GPP) and water fluxes including T and ET changed from linear to nonlinear. The study also found that the variability in WUET and WUEET were not consistent. Specifically, WUEET showed distinct seasonal characteristic whereas WUET kept constant as a plateau almost throughout the growth period, which reflected the inherent physiological property controlled by plant stomata at the canopy scale. Among these crops, maize exhibited the largest WUET and WUEET (5.30 ± 0.89 and 2.48 ± 1.14 g C kg⁻¹ H2O), followed by winter wheat (4.97 ± 1.52 and 2.35 ± 0.64 g C kg⁻¹ H2O) and soybean (4.88 ± 1.59 and 1.89 ± 0.99 g C kg⁻¹ H2O), respectively.
Plant phenology is a commonly used indicator representing the impacts on vegetation by the climate and other environmental factors. The use of repeated digital photography provides an opportunity to conduct long-term monitoring of plant phenology and to extract phenological transition dates. Here, we tracked the phenological changes in the flowering tree, Elaeagnus angustifolia L., in the Cherigele (CRGL) of the Badain Jaran Desert (BJD) and the nearby Lianggejing (LGJ); using one-year near-surface digital repeat photography and meteorological data, the phenological difference between the two sites was revealed. We found that the use of digital cameras allowed for the monitoring of plant phenology with high temporal and spatial accuracy in these dryland ecosystems. Furthermore, in the lake group region of BJD, the onset of greening occurred 23 days earlier, the onset of dormancy began 13 days later and the growing season was 36 days longer, compared to those of the surrounding area. This difference is partly related to the higher altitude in the LGJ; however, it is dominantly related to the warm island effect in the lake group region. This effect resulted in the mean annual temperature in the CRGL being ~1.6 °C higher than in the LGJ.
Ice plays an important role in hydraulic processes of rivers in cold regions such as Canada. The formation, progression, recession and breakup of river ice cover known as river ice processes affect river hydraulics, sediment transport characteristics as well as river morphology. Ice jamming and break up are responsible of winter flash floods, river bed modification and bank scour. River ice cover monitoring using terrestrial images from cameras installed on the shores can help monitor and understand river ice processes. In this study, the benefits of terrestrial monitoring of river ice using a camera installed on the shore are evaluated. A time-lapse camera system was installed during three consecutive winters at two locations on the shores of the Lower Nelson River, in Northern Manitoba and programmed to take an image of the river ice cover approximatively every hour. An image analysis algorithm was then developed to automatically extract quantitative characteristics of the river ice cover from the captured images. The developed algorithm consists of four main steps: preprocessing, image registration, georectification and river ice detection. The contributions of this thesis include the development of a novel approach for performing georectification while accounting for a fluctuating water surface elevation, and the use of categorization approach and a locally adaptive image thresholding technique for target detection. The developed algorithm was able to detect and quantify important river ice cover characteristics such as the area covered by ice, border ice progression and ablation rate, and river ice break up processes with an acceptable accuracy.
We evaluated the usability of the red (R), green (G), and blue (B) digital numbers (DNRGB) extracted from daily phenological images of a tropical rainforest in Malaysian Borneo. We examined temporal patterns in the proportions of DNR, DNG, and DNB as percentages of total DN (denoted as %R, %G and %B), in the hue, saturation, and lightness values in the HSL color model, and in a green excess index (GEI) of the whole canopy and of individual trees for 2 years. We also examined temporal patterns in the proportions of the red, green, and blue reflectance of the whole canopy surface as percentages of total reflectance (denoted as %ref_R, %ref_G, and %ref_B), and vegetation indices (the normalized-difference vegetation index, enhanced vegetation index, and green–red vegetation index) of the whole canopy by using daily measurements from quantum sensors. The temporal patterns of %RGB and saturation of individual trees revealed the characteristics of tree phenology caused by flowering, coloring, and leaf flushing. In contrast, those of the whole canopy did not, nor did those of %ref_R, %ref_G, or %ref_B, or the vegetation indices. The temporal patterns of GEI, however, could detect differences among individual trees caused by leaf flushing and coloring. Our results show the importance of installing multiple time-lapse digital cameras in tropical rainforests to accurately evaluate the sensitivity of tree phenology to meteorological and climatic changes. However, more work needs to be done to adequately describe whole-canopy changes.
Net radiation (Rn), water vapor flux (LE), sensible heat flux (Hs) and soil heat flux (G) were measured above a summer maize field with the eddy-covariance technique, simulation and analysis of water, heat fluxes and crop water use efficiency were made with the RZ-SHAW model at the same time in this study. The results revealed significant diurnal and seasonal variability of water vapor flux for summer maize. Most part of Rn was consumed by the evapotranspiration of the summer maize. The proportion of water vapor flux to net radiation ((LE/Rn) increased with the crop development and peaked around milk-filling stage with a value of 60%, a slightly lower than that obtained by the RZ-SHAW model. Daily evapotranspiration estimated by the model agreed with the results measured with the eddy-covariance technique, indices of agreement (IA) for hourly water vapor fluxes simulated and measured were above 0.75, root mean square errors (RMSE) were no more than 1.0. Diurnal patterns of Hs showed the shape of inverted "U" shifted to the forenoon with a maximum value around 11:30 (Beijing time), while LE exhibited an inverted "V" with a maximum value at around 13:00, about an hour later than Hs. Diurnal change of CO 2 showed an asymmetrical "V" curve and its maximal rates occurred at about 11:30. Variations of water use efficiency during the phonological stages of the summer maize showed a rapid increase with the photosynthetic photon flux density (PPFD) after sunrise, a state of equilibrium around 10:00 followed a decrease. Maximum values of water use efficiency were 24.3, and its average value ranged from 7.6 to 10.3 g kg-1.
Model calibration is a critical phase in the modelling process, and the need for a well-established calibration strategy is obvious. Therefore a systematic approach for model calibration is proposed which is guided by the intended model use, and which is supported by adequate techniques, prior knowledge and expert judgement. The success of calibration will be primarily limited by the nature, amount and quality of the available data, in relation to the complexity of the model; additional limitations are the effectiveness of the applied techniques and the availability of time, man- and computer power, adequate expertise and financial resources. These limitations will often preclude a unique calibrated model. As a consequence, calibration studies should provide information on the non-uniqueness and/or uncertainty which will be left in the model (parameters) after calibration, and this uncertainty should be adequately accounted for in subsequent model applications.
the purpose of understanding pat-terns and processes controlling carbon budgets across a broad range of scales, explicit activities to assess the impact of phenol-ogy on ecosystem carbon bal-ance are still somewhat lacking within the carbon cycle commu-nity. The reasons are clear: long-term observations, otherwise called 'monitoring' are not popu-lar with those that sponsor re-search in this area; three or five year projects are the norm, when in practice much longer records are required to detect long-term trends and their rela-tionships to climatic drivers. There is however, evidence for a shift in attitudes. Keeling's meas-urements of atmospheric CO 2 concentrations, that began in 1958, are an outstanding exam-ple of the value long-term moni-toring represents in the context of a changing world (Nisbet, 2007). Moreover, continuous eddy covariance measurements of CO 2 fluxes began in the early 1990s at a handful of sites. Every year, more and more sites have been added to FLUXNET, and many of these are now providing useful long term data not only with regard to spatial patterns of carbon uptake and release, but also in relation to the influence of phenology on carbon seques-tration. One example of a synergy be-tween phenology and flux moni-toring networks in Europe has Why observe phenology within FLUXNET? Phenology is the study of the timing of lifecycle events, espe-cially as influenced by the sea-sons and by the changes in weather patterns from year to year. The oldest phenological records, observations of cherry flowering at the Royal Court in Kyoto date back to 705 AD, and are still maintained to this day across Japan where the Japanese Meteorological Agency use these data to provide weekly forecast maps of expected blooming dates (http: Marsham, the father of modern phenological recording, was a wealthy landowner and amateur naturalist who recorded "Indications of spring" in Nor-folk, England, beginning in 1736. His family maintained these re-cords until the 1950s. In the modern era, phenology has gained a new impetus, as people realize that such records, if sus-tained over many years, can reveal how plants and animals respond to climate change. Moreover, phenological events such as the spring leaf-out and the autumn fall exert a strong control on both spatial and tem-poral patterns of the carbon cycle. Phenology also influences hydrologic processes, as spring leaf-out is accompanied by a marked increase in evapotranspi-ration, and nutrient cycling as autumn senescence results in a flush of fresh litter (nutrient) input to the forest floor. Phenology is a robust integrator of the effects of climate change on natural systems (Schwartz et al., 2006; IPCC 2007), and it is recognized that improved moni-toring of phenology on local-to-continental scales is needed. Historically, phenological obser-vations were a pastime of ama-teur naturalists (e.g. the Mar-sham family) and reliable records were often dependent on the skills and effort of the observer. The increased demand for inter-national co-operation and stan-dardisation in this area led to the creation of many large-scale phenological monitoring net-works such as the International Phenology Garden (IPG) pro-gram (http: (established in 1998) as well as the recently-established USA-National Phenology Network (U S A -N P N) (http://www.usanpn.org) and associated regional networks (e.g., http://www.nerpn.org). These networks have focused on developing standardized proto-cols for phenological observa-tions, and ensuring overlap be-tween plant species found across locations. Although there are obvious advantages in creating explicit linkages between these
We used Ulmus pumila leaf unfolding and leaf fall data collected at 46 stations during the 1986–2005 period to construct and validate daily temperature-based spatial phenology models. These models allowed simulation of the 20-year mean and yearly spatial patterns of U. pumila growing season beginning and end dates. This work was undertaken to explore the ecological mechanisms driving tree phenology spatial patterns and examine tree phenology spatial responses to temperature across China's temperate zone. The results show that spatial patterns of daily temperatures within the optimum spring and autumn length periods control spatial patterns of growing season beginning and end dates, respectively. Regarding 20-year mean growing season modeling, mean growing season beginning date correlates negatively with mean daily temperature within the optimum spring length period at the 46 stations. The mean spring spatial phenology model explained 90% of beginning date variance (P < 0.001) with a Root Mean Square Error (RMSE) of 4.6 days. In contrast, mean growing season end date correlates positively with mean daily temperature within the optimum autumn length period at the 46 stations. The mean autumn spatial phenology model explained 82% of end date variance (P < 0.001) with a RMSE of 5.6 days. On average, a spatial shift in mean spring and autumn daily temperatures by 1 °C may induce a spatial shift in mean beginning and end dates by −3.1 days and 2.6 days, respectively. Similarly, a significant negative and positive correlation was detectable between beginning date and spring daily temperature and between end date and autumn daily temperature at the 46 stations for each year, respectively. In general, the explained variances for yearly spatial phenology models are less than those of mean spatial phenology models, whereas the RMSEs of yearly models are greater than those of mean models. On average, a spatial shift in spring and autumn daily temperatures by 1 °C in a year may induce a spatial shift in beginning and end dates between −4.28 days and −2.75 days and between 2.17 days and 3.16 days in the year, respectively. Moreover, both mean and yearly spatial phenology models perform satisfactorily in predicting beginning and end dates of the U. pumila growing season at external stations. Further analysis showed that the negative spatial response of yearly beginning date to spring daily temperature was stronger in warmer years than in colder years. This finding suggests that climate warming in late winter and spring may enhance sensitivity of the growing season's spatial response due to the relationship of beginning date to temperature.
Documenting crop senescence rates is often difficult because of the need for frequent sampling during periods of rapid change and the subjective nature of human visual observations. The purpose of this study was to determine the feasibility of using images produced by a digital camera to measure the senescence rate of wheat and to compare the results with changes in greenness determined by two established methods. Measurements were made as part of an experiment to determine the effects of elevated CO2 and limited soil nitrogen on spring wheat (Triticum aestivum L.) at the University of Arizona's Maricopa Agricultural Center, near Phoenix, AZ. 'Greenness' measurements were made during senescence of the crop with a color digital camera, a hand-held radiometer, and a SPAD chlorophyll meter. The green to red (G/R) for each pixel in an image was calculated and the average G/R computed for cropped images from a digital camera representing 1 m2 for each treatment and sample date. The normalized difference vegetation index (NDVI) was calculated from the red and near-infrared canopy reflectances measured with a hand held radiometer. A SPAD reading was obtained from randomly selected flag leaves. All three methods of measuring plant greenness showed similar temporal trends. The relationships between G/R with NDVI and SPAD were linear over most of the range of G/R. However, NDVI was more sensitive at low values than G/R. G/R was more sensitive above G/R values of 1.2 than SPAD because the upper limits of SPAD measurements were constrained by the amount of chlorophyll in the leaf, while G/R responded to both chlorophyll concentration in the leaves as well as the number of leaves present. Color digital imaging appears useful for quantifying the senescence of crop canopies. The cost of color digital cameras is expected to decrease and the quality and convenience of use to improve.
For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region (500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.
Phenology and associated canopy development exert a strong control over seasonal energy and mass exchanges between the earth's surface and the atmosphere. Satellite measurements are used to assess main phenological stages of the vegetation at the global scale. The authors propose a method to derive the start, the maximum, the end, and the length of the vegetation cycle, based on the analysis of temporal series of weekly vegetation index, at a resolution of 1° lat × 1° long for year 1986. Global maps of these characteristics of the vegetation are presented, and their zonal distribution is discussed. The start of the vegetation cycle has been related to temperature sums in the case of temperate deciduous forest and to precipitation in the case of savannahs. It is concluded that satellite measurements offer interesting perspectives for global-scale quantitative phenology modeling.
Simple visible-light digital cameras offer a potential for expanded forms of plant ecological research. The moss Tortula princeps undergoes changes in reflected visible light during cycles of drying and hydrating in the field, and the MossCam project has collected digital images of T. princeps at least daily since 2003. Laboratory studies can be used to calibrate these images to indicate field physiological conditions. Drying the moss 6 d in the laboratory resulted in a decrease of net CO2 uptake to near 0; recovery after rewetting occurred within 10 min. The difference in reflectance between hydrated and dry T. princeps was maximal ca. 550 nm, and maximal net CO2 uptake was linearly related to the green : red ratio of laboratory images when net CO 2 uptake was positive. Using the green: red ratio of field images and otherwise assuming ideal conditions, the total carbon gain for a 6-d period around a 1.3-mm rain event was ca. 208 mmol CO2 m-2, equivalent to 69 d of respiration under dry conditions. Using a visible-light digital camera with micrometeorological data and laboratory-based gas exchange measurements, T. princeps can be used as a model species for simple field estimations of photosynthesis, carbon gain, and phenological events.
Vegetation phenology has a strong influence on the timing and phase of global terrestrial carbon and water exchanges and is an important indicator of climate change and variability. In this study we tested the application of inexpensive digital visible-light cameras in monitoring phenology. A standard digital camera was mounted on a 45 m tall flux tower at the Lägeren FLUXNET/CarboEuropeIP site (Switzerland), providing hourly images of a mixed beech forest. Image analysis was conducted separately on a set of regions of interest representing two different tree species during spring in 2005 and 2006. We estimated the date of leaf emergence based on the levels of the extracted red, green and blue colors. Comparisons with validation data were in accordance with the phenology of the observed trees. The mean error of observed leaf unfolding dates compared with validation data was 3 days in 2005 and 3.6 days in 2006. An uncertainty analysis was performed and demonstrated moderate impacts on color values of changing illumination conditions due to clouds and illumination angles. We conclude that digital visible-light cameras could provide inexpensive, spatially representative and objective information with the required temporal resolution for phenological studies.
The North China Plain (NCP) is one of China's most important social, economic, and agricultural regions. Currently, the Plain has 17,950 thousand ha of cultivated land, 71.1 percent of which is irrigated, consuming more than 70 percent of the total water supply. Increasing water demands associated with rapid urban and industrial development and expansion of irrigated land have led to overexploitation of both surface and the ratio of groundwater resources, particularly north of the Yellow River. In 1993, the ratio of groundwater exploitation to recharge in many parts of the NCP exceeded 1.0; in some areas, the ratio exceeded 1.5. Consequently, about 1.06 million ha of water-short irrigated areas in the NCP also have poor water quality. Persistent groundwater overexploitation in the northern parts of the NCP has resulted in water-level declines in both shallow and deep aquifers. According to data from 600 shallow groundwater observation wells in the Hebei Plain, the average depth to water increased from 7.23 m in 1983 to 11.52 m in 1993, indicating an average water-table decline of 0.425 m/year. Water table declines are not uniformly distributed throughout the area. Depletion rates are generally greatest beneath cities and intensively groundwater-irrigated areas. Water-table declines have also varied over time. With the continued decline of groundwater levels, large depression cones have formed both in unconfined and confined aquifers beneath the Hebei Plain. Groundwater depletion in the NCP has severely impacted the environment. Large tracts of land that overlie cones of depression have subsided, seawater has intruded into previously freshwater aquifers in coastal plains, and ground-water quality has deteriorated due to salinization, seawater intrusion, and untreated urban and industrial wastewater discharge. In order to balance groundwater exploitation with recharge, the major remedial measures suggested are to strengthen groundwater management, to raise water use efficiency, to adjust the water-consumed structure, and to increase water supply
The objective of this work was to document the utility of phenological data derived from satellite sensors by comparing them with modelled phenology. Surface phenological model outputs (first leaf and first bloom dates) were correlated positively with satellite sensor-derived start of season (SOS) dates for 1991-1995 across the eastern United States. The correlation was highest for forest (r 0.62 for deciduous trees and 0.64 for mixed woodland) and tall grass (r 0.46) and lowest for short grass (r 0.37). The average correlation over all land cover types was 0.61. Average SOS dates were consistently earlier than Spring Index dates across all land cover types. This finding and limited native tree phenology data suggest that the SOS technique detects understorey green-up in the forest rather than overstorey species. The biweekly temporal resolution of the satellite sensor data placed an upper limit on prediction accuracy; thus, year-to-year variations at individual sites were typically small. Nevertheless, the correct biweek SOS could be identified from the surface models 61% of the time, and 1 biweek 96% of the time. Further temporal refinement of the satellite sensor measurements is necessary in order to connect them with surface phenology adequately and to develop links among 'green wave' components in selected biomes.
FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of car-bon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five con-tinents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide infor-mation for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas ex-change models. Findings so far include 1) net CO 2 exchange of temperate broadleaved forests increases by about 5.7 g C m −2 day −1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO 2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO 2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO 2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities.
Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000–2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m−2 yr−1 during the spring transition period and +75 ± 130 g C m−2 yr−1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphere–atmosphere feedbacks and interactions in coupled global climate models.
Using phenological and normalized difference vegetation index (NDVI) data from 1982 to 1993 at seven sample stations in temperate eastern China, we calculated the cumulative frequency of leaf unfolding and leaf coloration dates for deciduous species every 5 days throughout the study period. Then, we determined the growing season beginning and end dates by computing times when 50% of the species had undergone leaf unfolding and leaf coloration for each station year. Next, we used these beginning and end dates of the growing season as time markers to determine corresponding threshold NDVI values on NDVI curves for the pixels overlaying phenological stations. Based on a cluster analysis, we determined extrapolation areas for each phenological station in every year, and then implemented the spatial extrapolation of growing season parameters from the seven sample stations to all possible meteorological stations in the study area.
Results show that spatial patterns of growing season beginning and end dates correlate significantly with spatial patterns of mean air temperatures in spring and autumn, respectively. Contrasting with results from similar studies in Europe and North America, our study suggests that there is a significant delay in leaf coloration dates, along with a less pronounced advance of leaf unfolding dates in different latitudinal zones and the whole area from 1982 to 1993. The growing season has been extended by 1.4–3.6 days per year in the northern zones and by 1.4 days per year across the entire study area on average. The apparent delay in growing season end dates is associated with regional cooling from late spring to summer, while the insignificant advancement in beginning dates corresponds to inconsistent temperature trend changes from late winter to spring. On an interannual basis, growing season beginning and end dates correlate negatively with mean air temperatures from February to April and from May to June, respectively.
CO2 flux was measured continuously in a wheat and maize rotation system of North China Plain using the eddy covariance technique to study the characteristic of CO2 exchange and its response to key environmental factors. The results show that nighttime net ecosystem exchange (NEE) varied exponentially with soil temperature. The temperature sensitivities of the ecosystem (Q
10) were 2.94 and 2.49 in years 2002–2003 and 2003–2004, respectively. The response of gross primary productivity (GPP) to photosynthetically active radiation (PAR) in the crop field can be ex-pressed by a rectangular hyperbolic function. Average A
max and α for maize were more than those for wheat. The values of α increased positively with leaf area index (LAI) of wheat. Diurnal variations of NEE were significant from March to May and from July to September, but not remarkable in other months. NEE, GPP and ecosystem respiration (R
ec) showed significantly seasonal variations in the crop field. The highest mean daily CO2 uptake rate was −10.20 and −12.50 gC·m−2−d−1 in 2003 and 2004, for the maize field, respectively, and −8.19 and −9.50 gC−m−2·d−1 in 2003 and 2004 for the wheat field, respectively. The maximal CO2 uptake appeared in April or May for wheat and mid-August for maize. During the main growing seasons of winter wheat and summer maize, NEE was controlled by GPP which was chiefly influenced by PAR and LAI. R
ec reached its annual maximum in July when R
ec and GPP contributed to NEE equally. NEE was dominated by R
ec in other months and temperature became a key factor controlling NEE. Total NEE for the wheat field was −77.6 and −152.2 gC·m−2·a−1 in years 2002–2003 and 2003–2004, respectively, and −120.1 and −165.6 gC·m−2·a−1 in 2003 and 2004 for the maize field, respectively. The cropland of North China Plain was a carbon sink, with annual −197.6 and −317.9 gC·m−2·a−1 in years 2002–2003 and 2003–2004, respectively. After considering the carbon in grains, the cropland became a carbon source, which was 340.5 and 107.5 gC·m−2·a−1 in years 2002–2003 and 2003–2004, respectively. Affected by climate and filed managements, inter-annual carbon exchange varied largely in the wheat and maize rotation system of North China Plain.
The photochemical reflectance index (PRI), derived from narrow-band reflectance at 531 and 570 nm, was explored as an indicator
of photosynthetic radiation use efficiency for 20 species representing three functional types: annual, deciduous perennial,
and evergreen perennial. Across species, top-canopy leaves in full sun at midday exhibited a strong correlation between PRI
and ΔF/Fm′, a fluorescence-based index of photosystem II (PSII) photochemical efficiency. PRI was also significantly correlated
with both net CO2 uptake and radiation use efficiency measured by gas exchange. When species were examined by functional type, evergreens exhibited
significantly reduced midday photosynthetic rates relative to annual and deciduous species. This midday reduction was associated
with reduced radiation use efficiency, detectable as reduced net CO2 uptake, PRI, and ΔF/Fm′ values, and increased levels of the photoprotective xanthophyll cycle pigment zeaxanthin. For each
functional type, nutrient deficiency led to reductions in both PRI and ΔF/Fm′ relative to fertilized controls. Laboratory
experiments exposing leaves to diurnal courses of radiation and simulated midday stomatal closure demonstrated that PRI changed
rapidly with both irradiance and leaf physiological state. In these studies, PRI was closely correlated with both ΔF/Fm' and
radiation use efficiency determined from gas exchange at all but the lowest light levels. Examination of the difference spectra
upon exposure to increasing light levels revealed that the 531 nm Δ reflectance signal was composed of two spectral components.
At low irradiance, this signal was dominated by a 545-nm component, which was not closely related to radiation use efficiency.
At progressively higher light levels above 100 μmol m−2 s−1, the 531-nm signal was increasingly dominated by a 526-nm component, which was correlated with light use efficiency and with
the conversion of the xanthophyll pigment violaxanthin to antheraxanthin and zeaxanthin. Further consideration of the two
components composing the 531-nm signal could lead to an index of photosynthetic function applicable over a wide range of illumination.
The results of this study support the use of PRI as an interspecific index of photosynthetic radiation use efficiency for
leaves and canopies in full sun, but not across wide ranges in illumination from deep shade to full sun. The discovery of
a consistent relationship between PRI and photosynthetic radiation use efficiency for top-canopy leaves across species, functional
types, and nutrient treatments suggests that relative photosynthetic rates could be derived with the “view from above” provided
by remote reflectance measurements if issues of canopy and stand structure can be resolved.
One of the aims of the EU funded project Long-term regional effects of climate change on European forests: impact assessment and consequences for carbon budgets (LTEEF-II, ENV4-CT97-0577) is to quantify the fluxes of carbon and water between vegetation (forests) and atmosphere and to assess the carbon balance of forests in Europe.This paper presents the results of the application of the C-Fix model within the frame of part of the objectives of the LTEEF-II project, as defined for the European continental scale.A description of the C-Fix model is presented in the first part of this paper. C-Fix is a Monteith type parametric model driven by temperature, radiation and fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the last variable derived by processing NOAA/AVHRR data of 1997 acquired over Europe as well as VEGETATION (VGT) data for the same region for the period 1998–1999. In a second part of the paper, the main results obtained are described, e.g. net ecosystem fluxes for forest, calculated locally for the Euroflux CO2 measurement sites as well as for the whole continent. The model simulations were evaluated with eddy covariance measurements of NEP performed during 1997 at the Euroflux sites. Moreover, a comparison between NEP determined with NOAA/AVHRR data, opposed to NEP simulations based on VGT data is performed. A difference up to 20% is obtained between both data sets. Finally A C-Fix estimate of total European vegetation NEP obtained with NOAA/AVHRR data for 1997 is 2.70±32% Pg C, (P=1015 g, % is 95% confidence limit). This estimate decreases to 2.15±43% Pg C when VGT data for the period April 1998–March 1999 are used. The total forest NEP for Europe, however, is estimated at 0.735 Pg C for 1997. Along a North–South transect at 13° east, through Europe a clear increase in NEP and the other basic carbon mass fluxes GPP and NPP is modelled from northern to southern latitudes. Moreover, along this transect the values simulated with C-Fix for NEP corresponds well with the eddy covariance NEP estimates for the Euroflux sites.
The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO2 between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO2 eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO2 flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai–Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai–Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales.
Documenting crop senescence rates is often difficult because of the need for frequent sampling during periods of rapid change and the subjective nature of human visual observations. The purpose of this study was to determine the feasibility of using images produced by a digital camera to measure the senescence rate of wheat and to compare the results with changes in greenness determined by two established methods. Measurements were made as part of an experiment to determine the effects of elevated CO2 and limited soil nitrogen on spring wheat (Triticum aestivum L.) at the University of Arizona's Maricopa Agricultural Center, near Phoenix, AZ. "Greenness" measurements were made during senescence of the crop with a color digital camera, a hand-held radiometer, and a SPAD chlorophyll meter. The green to red (GIR) for each pixel in an image was calculated and the average GIR computed for cropped images from a digital camera representing 1 m(2) for each treatment and sample date. The normalized difference vegetation index (NDVI) was calculated from the red and near-infrared canopy reflectances measured with a hand held radiometer. A SPAD reading was obtained from randomly selected flag leaves. All three methods of measuring plant greenness showed similar temporal trends. The relationships between GIR with NDVI and SPAD were linear over most of the range of GIR. However, NDVI was more sensitive at low values than GIR. GIR was more sensitive above G/R values of 1.2 than SPAD because the upper limits of SPAD measurements were constrained by the amount of chlorophyll in the leaf, while GIR responded to both chlorophyll concentration in the leaves as well as the number of leaves present. Color digital imaging appears useful for quantifying the senescence of crop canopies. The cost of color digital cameras is expected to decrease and the quality and convenience of use to improve.
The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km2) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.
Climatic and flowering data from a site in the northern Sonoran Desert of southern Arizona were used to define flowering triggers and developmental requirements for 6 woody plants. These formulations were then used to predict flowering dates at a second northern Sonoran Desert site. It was determined that flowering is triggered by rain in Larrea tridentata (DC.) Cov., Fouquieria splendens Engelm., Encelia farinosa A. Gray, Ambrosia deltoidea (A. Gray) Payne and Acacia constricta Benth., and that flowering is triggered by photoperiod in Cercidium microphyllum (Torr.) Rose & Johnst. The base temperature for floral development in L. tridentata, F. splendens, E. farinosa, A. deltoidea and C. microphyllum is about 10⚬C. Their mean degree-day requirements range from 414 to 719. Acacia constricta requires 522 degree-days above 15⚬C. Minimum rainfall triggers varied from 9 mm for Ambrosia to 20 mm for Encelia. Flowering time in C. microphyllum may reflect phylogenetic constraints, while flowering time in F. splendens may be strongly influenced by pollinator availability. Flowering times of the remaining species seem constrained more by climate than by biotic considerations such as phylogeny, seed germination and competition for pollinators.
Recent studies have reported that seasonal variation in camera-based indices that are calculated from the digital numbers of the red, green, and blue bands (RGB_DN) recorded by digital cameras agrees well with the seasonal change in gross primary production (GPP) observed by tower flux measurements. These findings suggest that it may be possible to use camera-based indices to estimate the temporal and spatial distributions of photosynthetic productivity from the relationship between RGB_DN and GPP. To examine this possibility, we need to investigate the characteristics of seasonal variation in three camera-based indices (green excess index [GE], green chromatic coordinate [rG], and HUE) and the robustness of the relationship between these indices and tower flux-based GPP and how it differs among ecosystems. Here, at a daily time step over multiple years in a deciduous broad-leaved and an evergreen coniferous forest, we examined the relationships between canopy phenology assessed by using the three indices and GPP determined from tower CO2 flux observations, and we compared the camera-based indices with the corresponding spectra-based indices estimated by a spectroradiometer system. We found that (1) the three camera-based indices and GPP showed clear seasonal patterns in both forests; (2) the amplitude of the seasonal variation in the three camera-based indices was smaller in the evergreen coniferous forest than in the deciduous broad-leaved forest; (3) the seasonal variation in the three camera-based indices corresponded well to seasonal changes in potential photosynthetic activity (GPP on sunny days); (4) the relationship between the three camera-based indices and GPP appeared to have different characteristics at different phenological stages; and (5) the camera-based and spectra-based HUE indices showed a clear relationship under sunny conditions in both forests. Our results suggest that it might be feasible for ecologists to establish comprehensive networks for long-term monitoring of potential photosynthetic capacity from regional to global scales by linking satellite-based, in situ spectra-based, and in situ camera-based indices.
Surface soil samples from a wide range of naturally occurring soils were obtained for the purpose of studying the characteristic variations in soil reflectance as these variations relate to other soil properties and soil classification. A total of 485 soil samples from the U.S. and Brazil representing 30 suborders of the 10 orders of Soil Taxonomy was examined. Spectral bidirectional reflectance factor was measured on uniformly moist soils over the 0.52 to 2.32 µm wavelength range with a spectroradiometer adapted for indoor use.
Five distinct soil spectral reflectance curve forms were identified according to curve shape, the presence or absence of absorption bands, and the predominance of soil organic matter and iron oxide composition. These curve forms were further characterized according to genetically homogeneous soil properties in a manner similar to the subdivisions at the suborder level of Soil Taxonomy . Results indicate that spectroradiometric measurements of soil spectral bidirectional reflectance factor can be used to characterize soil reflectance in terms that are meaningful to soil classification, genesis, and survey.
To investigate the annual and spatial variability in the beginning of growing season across Europe, phenological data of the International Phenological Gardens for the period 1969-1998 were used. The beginning of growing season (BGS) was defined as an average leaf unfolding index of 4 tree species (Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum). The study shows significant changes in the mean air temperatures from February to April and in the average BGS in Europe since 1989. In the last decade the mean temperature in early spring increased by 0.8°C. As a result the average BGS advanced by 8 d. Between 1989 and 1998, 8 out of 10 years tend towards an earlier onset of spring. The earliest date was observed in 1990. The relationships between air temperature and the beginning of growing season across Europe were investigated by canonical correlation analysis (CCA). The spatial variability of both fields can be described by 3 pairs of CCA patterns. The first pattern, which explains most of the variance, shows a uniform structure with above (below) normal temperatures in whole Europe and consequently an advanced (delayed) beginning of growing season. The other 2 patterns show regional differences in the anomaly fields. Whereas the second CCA pattern has a meridional structure, the third pattern shows a zonal distribution. In all cases the anomalies of the regional air temperature and of the beginning of growing season corre- spond very well. The correlation coefficients between the anomaly fields range between 0.90 and 0.66. For all patterns appropriate examples in the observed data were found.
In thermodynamic terms, ecosystems are machines supplied with energy from an external source, usually the sun. When the input of energy to an ecosystem is exactly equal to its total output of energy, the state of equilibrium which exists is a special case of the First Law of Thermodynamics. The Second Law is relevant too. It implies that in every spontaneous process, physical or chemical, the production of 'useful' energy, which could be harnessed in a form such as mechanical work, must be accompanied by a simultaneous 'waste' of heat. No biological system can break or evade this law. The heat produced by a respiring cell is an inescapable component of cellular metabolism, the cost which Nature has to pay for creating biological order out of physical chaos in the environment of plants and animals. Dividing the useful energy of a thermodynamic process by the total energy involved gives a figure for the efficiency of the process, and this procedure has been widely used to analyse the flow of energy in ecosystems. For example, the efficiency with which a stand of plants produces dry matter by photosynthesis can be defined as the ratio of chemical energy stored in the assimilates to radiant energy absorbed by foliage during the period of assimilation. The choice of absorbed energy as a base for calculating efficiency is convenient but arbitrary. To derive an efficiency depending on the environment of a particular site as well as oil the nature of the vegetation, dry matter production can be related to the receipt of solar energy at the top of the earth's atmosphere. This exercise was attempted by Professor William Thomson, later Lord Kelvin, in 1852. 'The author estimates the mechanical value of the solar heat which, were none of it absorbed by the atmosphere, would fall annually on each square foot of land, at 530 000 000 foot pounds; and infers that probably a good deal more, 1/1000 of the solar heat, which actually falls on growing plants, is converted into mechanical effect.' Outside the earth's atmosphere, a surface kept at right angles to the sun's rays receives energy at a mean rate of 1360 W m-2 or 1f36 kJ m-2 s-1, a figure known as the solar constant. As the energy stored by plants is about 17 kJ per gram of dry matter, the solar constant is equivalent to the production of dry matter at a rate of about 1 g m-2 every 12 s, 7 kg m-2 per day, or 2 6 t m-2 year-'. The annual yield of agricultural crops ranges from a maximum of 30-60 t ha-' in field experiments to less than I t ha-' in some forms of subsistence farming. When these rates are expressed as a fraction of the integrated solar constant, the efficiencies of agricultural systems lie between 0-2 and 0 004%, a range including Kelvin's estimate of 0-1%. Conventional estimates of efficiency in terms of the amount of solar radiation incident at the earth's surface provide ecologists and agronomists with a method for comparing plant productivity under different systems of land use and management and in different * Opening paper read at IBP/UNESCO Meeting on Productivity of Tropical Ecosystems, Makerere University, Uganda, September 1970.
The timing of leaf expansion in spring and leaf senescence in fall determines growing season length; hence leaf phenology is important in modelling carbon production. Previous work monitoring net ecosystem exchange using eddy flux technology found phenological timing to be a key factor determining ecosystem carbon balance. Growing degree days (GDD; the sum of daily mean temperature above 0 degrees C) have long been used to track tree phenological behavior. Ecosystem carbon balance simulations should employ models that reliably track leaf phenological activities. Our goals for this study were to evaluate the GDD-based phenological algorithm for the forest carbon simulation model PnET and to develop a robust budburst model for the implementation of PnET in the forests of Ohio. We obtained 11 years (1990-2000) of leaf budburst phenology data from Harvard Forest, MA and found large interannual variations on the date of budburst. Contrary to the previous sensitivity analysis on GDD, we used the observed range in the data to inform our new GDD sensitivity analyses. In fact the broad inter- annual variation of the budburst phenology had substantial impacts on the annual net primary production. Moreover, GDD did not provide reliable prediction on the date of budburst although it was employed by the PnET family of models. The predictions of budburst dates were largely improved by incorporating a chilling factor, with 16 of 17 deciduous tree species showing significant linear relationships between predicted and observed dates of budburst. However, this updated phenology model showed geographic specificity that is not suitable for our simulations in southern Ohio. For successful development of a more comprehensive phenological model, locally developed and long-term leaf phenology records are required. (c) 2007 Elsevier B.V All rights reserved.
A global prognostic physiologically based model of the carbon budget in terrestrial ecosystems, the Frankfurt Biosphere Model (FBM), is applied to simulate the interannual variation of carbon exchange fluxes between the atmosphere and the terrestrial biosphere. The data on climatic forcing are based on Cramer and Leemans climate maps; the interannual variation is introduced according to records of temperature anomalies and precipitation anomalies for the period 1980 to 1993. The calculated net exchange flux between the atmosphere and the terrestrial biosphere is compared to the biospheric signal deduced from C-13 measurements. Some intermediate results are presented as well: the contributions of the most important global ecosystems to the biospheric signal, the contributions of different latitudinal belts to the biospheric signal, and the responses of net primary production (NPP) and heterotrophic respiration (R(h)) From the simulation results it can be inferred that the complex temperature and precipitation responses of NPP and R(h) in different latitudes and different ecosystem types add up to a global CO2 signal contributing substantially to the atmospheric CO2 anomaly on the interannual timescale. The temperature response of NPP was found to be the most important factor determining this signal.
Leaf area index (LAI) was estimated from vertical gap fraction measurements obtained using top-ofcanopy
digital colour photography over corn, soybean and wheat canopies. A histogram-based threshold
technique was used to separate green vegetation tissues from background soil and residue materials
in order to derive the canopy vertical gap fraction from the digital photos. The results show that the
gap fraction obtained using digital photography was linearly related with the diffuse non-interceptance
obtained with a LAI-2000 plant canopy analyzer (R2 = 0.78). LAI derived from the photographic method
was comparable to the LAI measured using LAI-2000 (R2 = 0.83) in the absence of senescence. We recommend
using digital photography in addition to conventional equipment for acquiring LAI and gap fraction
of agricultural crops, because the approach is less limited by radiation conditions, and the protocol can
easily be implemented for extensive sampling at low cost.
No method exists to reliably predict percent vegetation coverage using indirect measures. This study was conducted to evaluate the use of digital image processing techniques applied to digital color, red‐green‐blue (RGB), images of crop canopies to estimate percent vegetation coverage and biomass. Two field experiments with winter wheat (Triticum aestivum L.) “Tonkawa”; were planted in October 1996 and 1997 at Perkins, OK on a Teller sandy loam (Udic Argiustoll) and at Tipton, OK on a Tipton silt loam (Pachic Argiustoll). Plot images from winter wheat canopies were taken using a Kodak DC40 Digital Camera (1995) with an image resolution of 756 × 504 pixels. Spectral irradiance readings were taken from wheat canopies in red (671±6 nm) and near infrared (780±6 nm) wavelengths, and normalized difference vegetation index (NDVI) was calculated. Percent vegetation coverage was estimated using image‐processing routines in Micrografx Picture Publisher® version 7.0. The digital images were converted from 8‐bit RGB tagged image file format (TIFF) files, which were produced by processing the images from the camera with Photo Enhancer®, to binary pseudo‐color images. Percent of pixels corresponding to the vegetation color was then calculated and used as the percent coverage for each plot. Binary pseudo‐color images provided useful estimates of percent vegetation coverage that were highly correlated with wheat canopy NDVI measurements.
A commercially available digital camera can be used in a low-cost automatic observation system for monitoring crop growth change in open-air fields. We developed a prototype Crop Phenology Recording System (CPRS) for monitoring rice growth, but the ready-made waterproof cases that we used produced shadows on the images. After modifying the waterproof cases, we repeated the fixed-point camera observations to clarify questions regarding digital camera-derived vegetation indices (VIs), namely, the visible atmospherically resistant index (VARI) based on daytime normal color images (RGB image) and the nighttime relative brightness index (NRBINIR) based on nighttime near infrared (NIR) images. We also took frequent measurements of agronomic data such as plant length, leaf area index (LAI), and aboveground dry matter weight to gain a detailed understanding of the temporal relationship between the VIs and the biophysical parameters of rice. In addition, we conducted another nighttime outdoor experiment to establish the link between NRBINIR and camera-to-object distance. The study produced the following findings. (1) The customized waterproof cases succeeded in preventing large shadows from being cast, especially on nighttime images, and it was confirmed that the brightness of the nighttime NIR images had spatial heterogeneity when a point light source (flashlight) was used, in contrast to the daytime RGB images. (2) The additional experiment using a forklift showed that both the ISO sensitivity and the calibrated digital number of the NIR (cDNNIR) had significant effects on the sensitivity of NRBINIR to the camera-to-object distance. (3) Detailed measurements of a reproductive stem were collected to investigate the connection between the morphological feature change caused by the panicle sagging process and the downtrend in NRBINIR during the reproductive stages. However, these agronomic data were not completely in accord with NRBINIR in terms of the temporal pattern. (4) The time-series data for the LAI, plant length, and aboveground dry matter weight could be well approximated by a sigmoid curve based on NRBINIR and VARI. The results confirmed that NRBINIR was more sensitive to all of the agronomic data for overall season, including the early reproductive stages. VARI had an especially high correlation with LAI, unless yellow panicles appeared in the field of view.
Vegetation indices derived from reflectance data are related to canopy variables such as aboveground biomass, leaf area index (LAI), and the fraction of intercepted photosynthetically active radiation (fIPAR). However, under N stress the relationships between vegetation indices (VI) and these canopy variables might be confounded due to plant chlorosis. We studied the relationships between reflectance-based VI and canopy variables (aboveground biomass, LAI canopy chlorophyll A content [LAI x Chl A], and fIPAR) for a wheat (Triticum aestivum L.) crop growing under different N supplies. Nitrogen fertilization promoted significant increases in radiation interception (plant growth) and, to a lesser extent, in radiation use efficiency (RUE). The VI vs. LAI relationships varied significantly among treatments, rendering the VI-based equations unreliable to estimate LAI under contrasting N conditions. However, a single relationship emerged when LAI x Chl A was considered. Moreover, VI were robust indicators of fIPAR by green canopy components independently of N treatment and phenology. Aboveground biomass was poorly correlated with grain yield, whereas cumulative VI simple ratio (SR) was a good predictor of grain yield, probably because cumulative SR closely tracked the duration and intensity of the canopy photosynthetic capacity.
Aim We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982–2006.
Location North America.
Methods A piecewise logistic model is used to extract phenological metrics from a time-series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite-derived phenological metrics and ground-based phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study.
Results The spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year−1, P= 0.013) and an extended growing season length (0.683 days year−1, P= 0.011) are found over the mid and high latitudes in North America during 1982–2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite-derived phenology information.
Main conclusions It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.
National Oceanic and Atmospheric Administration (NOAA)‐series satellites, carrying advanced very high‐resolution radiometer (AVHRR) sensors, have allowed moderate resolution (1 km) measurements of the normalized difference vegetation index (NDVI) to be collected from the Earth's land surfaces for over 20 years. Across the conterminous USA, a readily accessible and decade‐long data set is now available to study many aspects of vegetation activity in this region. One feature, the onset of deciduous plant growth at the start of the spring season (SOS) is of special interest, as it appears to be crucial for accurate computation of several important biospheric processes, and a sensitive measure of the impacts of global change.
In this study, satellite‐derived SOS dates produced by the delayed moving average (DMA) and seasonal midpoint NDVI (SMN) methods, and modelled surface phenology (spring indices, SI) were compared at widespread deciduous forest and mixed woodland sites during 1990–93 and 1995–99, and these three measures were also matched to native species bud‐break data collected at the Harvard Forest (Massachusetts) over the same time period. The results show that both SOS methods are doing a modestly accurate job of tracking the general pattern of surface phenology, but highlight the temporal limitations of biweekly satellite data. Specifically, at deciduous forest sites: (1) SMN SOS dates are close in time to SI first bloom dates (average bias of +0.74 days), whereas DMA SOS dates are considerably earlier (average bias of −41.24 days) and also systematically earlier in late spring than in early spring; (2) SMN SOS tracks overall yearly trends in deciduous forests somewhat better than DMA SOS, but with larger average error (MAEs 8.64 days and 7.37 days respectively); and (3) error in both SOS techniques varies considerably by year. Copyright © 2002 Royal Meteorological Society.
Seven years of carbon dioxide flux measurements indicate that a ∼90‐year‐old spruce dominated forest in Maine, USA, has been sequestering 174±46 g C m ⁻² yr ⁻¹ (mean±1 standard deviation, nocturnal friction velocity ( u * ) threshold >0.25 m s ⁻¹ ). An analysis of monthly flux anomalies showed that above‐average spring and fall temperatures were significantly correlated with greater monthly C uptake while above‐average summer temperatures were correlated with decreased net C uptake. Summer months with significantly drier or wetter soils than normal were also characterized by lower rates of C uptake. Years with above‐average C storage were thus typically characterized by warmer than average spring and fall temperatures and adequate summer soil moisture.
Environmental and forest–atmosphere flux data recorded from a second tower surrounded by similar forest, but sufficiently distant that flux source regions (‘footprints’), did not overlap significantly showed almost identical temperature and solar radiation conditions, but some differences in energy partitioning could be seen. Half‐hourly as well as integrated (annual) C exchange values recorded at the separate towers were very similar, with average annual net C uptake differing between the two towers by <6%. Interannual variability in net C exchange was found to be much greater than between tower variability. Simultaneous measurements from two towers were used to estimate flux data uncertainty from a single tower. Carbon‐flux model parameters derived independently from each flux tower data set were not significantly different, demonstrating that flux towers can provide a robust method for establishing C exchange model parameters.
Rapidly depleting unconfined aquifers are the primary source of water for irrigation on the North China Plain. Yet, despite its critical importance, groundwater recharge to the Plain remains an enigma. We introduce a one-dimensional soil-water-balance model to estimate precipitation- and irrigation-generated areal recharge from commonly available crop and soil characteristics and climate data. To limit input data needs and to simplify calculations, the model assumes that water flows vertically downward under a unit gradient; infiltration and evapotranspiration are separate, sequential processes; evapotranspiration is allocated to evaporation and transpiration as a function of leaf-area index and is limited by soil-moisture content; and evaporation and transpiration are distributed through the soil profile as exponential functions of soil and root depth, respectively. For calibration, model-calculated water contents of 11 soil-depth intervals from 0 to 200 cm were compared with measured water contents of loam soil at four sites in Luancheng County, Hebei Province, over 3 years (1998–2001). Each 50-m2 site was identically cropped with winter wheat and summer maize, but received a different irrigation treatment. Average root mean-squared error between measured and model-calculated water content of the top 180 cm was 4·2 cm, or 9·3% of average total water content. In addition, model-calculated evapotranspiration compared well with that measured by a large-scale lysimeter. To test the model, 12 additional sites were simulated successfully. Model results demonstrate that drainage from the soil profile is not a constant fraction of precipitation and irrigation inputs, but rather the fraction increases as the inputs increase. Because this drainage recharges the underlying aquifer, improving irrigation efficiency by reducing seepage will not reverse water-table declines. Copyright © 2003 John Wiley & Sons, Ltd.
Leaf phenology remains one of the most difficult processes to parameterize in terrestrial ecosystem models because our understanding of the physical processes that initiate leaf onset and senescence is incomplete. While progress has been made at the molecular level, for example by identifying genes that are associated with senescence and flowering for selected plant species, a picture of the processes controlling leaf phenology is only beginning to emerge. A variety of empirical formulations have been used with varying degrees of success in terrestrial ecosystem models for both extratropical and tropical biomes. For instance, the use of growing degree-days (GDDs) to initiate leaf onset has received considerable recognition and this approach is used in a number of models. There are, however, limitations when using GDDs and other empirically based formulations in global transient climate change simulations.
In agriculture and horticulture, phenological observations have a long tradition since many management decisions and the timing of field works (planting, fertilizing, irrigating, crop protection, harvesting, etc.) are based on plant develop- ment. This chapter deals with both the historical and modern aspects of phenology in agriculture, including the impacts of climate change on plant development. The individual paragraphs give some examples how important are phenological observations to detect changes in the duration of phenological phases, to define the length of growing season – which sets the environmental limits for crop production – to select suitable growing areas for perennial and field crops, and how these data can be used to develop phenological models in agriculture and horticulture. The chapter ends with some applications of phenological models to calculate possible shifts in the timing of ripening and blossoming stages in relation to climate change.
The continued spread of invasive weeds is threatening ecosystem health throughout North America. Understanding the relationships between invasive weeds' key phenological phases and structural and/or functional canopy development is an essential step for making informed decisions regarding their management. We analyzed a three-year image archive obtained from an inexpensive webcam overlooking a perennial pepperweed (Lepidium latifolium L) infestation in California to explore the ability of red (R)-green (G)-blue (B) color space information to track the structural and functional development of the pepperweed. We characterized structural and functional canopy development through surface roughness length (z(0m); a proxy for canopy height and leaf area index) and canopy photosynthesis (F-A), respectively, both of which we derived from eddy covariance measurements. Here we demonstrate the use of cross-correlation functions to determine the temporal lags between chromatic coordinates and two color indices, all calculated from RGB brightness levels, with z(0m) and F-A. We found that these color metrics fail to represent the structural and/or functional state of the canopy. In contrast, relative luminance (CIE Y) appears to be a better indicator for z(0m), and especially for F-A. We calculated CIE Y from pepperweed RGB brightness levels in relation to hypothetical horizontal reference RGB brightness levels. We obtained the latter by applying the ratio between horizontally measured and hypothetical incoming solar radiation on a vertical surface to RGB brightness levels of a vertically oriented reference of invariant light-grey color. We conclude that webcam image archives may provide an inexpensive tool for making informed decisions regarding the timing but not for assessing the effectiveness of invasive plant control measures such as mowing.
Observations of net ecosystem exchange (NEE) of carbon and its biophysical drivers have been collected at the AmeriFlux site in the Morgan-Monroe State Forest (MMSF) in Indiana, USA since 1998. Thus, this is one of the few deciduous forest sites in the world, where a decadal analysis on net ecosystem productivity (NEP) trends is possible. Despite the large interannual variability in NEP, the observations show a significant increase in forest productivity over the past 10 years (by an annual increment of about 10 g C m-2 yr-1). There is evidence that this trend can be explained by longer vegetative seasons, caused by extension of the vegetative activity in the fall. Both phenological and flux observations indicate that the vegetative season extended later in the fall with an increase in length of about 3 days yr-1 for the past 10 years. However, these changes are responsible for only 50% of the total annual gain in forest productivity in the past decade. A negative trend in air and soil temperature during the winter months may explain an equivalent increase in NEP through a decrease in ecosystem respiration.
To reveal the seasonal change of leaf ecophysiological and canopy characteristics and to evaluate the functional role of canopy and shrub tree species in forest CO2 uptake, we measured forest canopy leaf area index (LAI) using a hemispherical canopy photography technique, leaf CO2 gas exchange and shoot architecture for canopy (Betula ermanii and Quercus crispula) and shrub (Hydrangea paniculata and Viburnum furcatum) tree species in a deciduous broadleaved forest in a cool-temperate region in central Japan. Canopy LAI and photosynthetic capacity of canopy tree leaves increased rapidly with leaf expansion. LAI reached its maximum in early summer but photosynthetic capacity reached its maximum in late summer. Development of photosynthetic capacity was dependent on the changes of leaf mass per area and leaf chlorophyll content (evaluated by SPAD). The seasonal maximum photosynthetic capacity of the leaves at the forest canopy top (B. ermanii and sun leaves of Q. crispula) was about more than double of the leaves in the shrub layer (H. paniculata, shade leaves of Q. crispula and V. furcatum). Light interception and photosynthetic carbon gain at a shoot level were simulated under three air temperature conditions by a three-dimensional canopy photosynthesis model (Y-plant) involving the combined leaf photosynthesis and stomatal conductance responses and shoot architecture. Results showed that (1) calculations without considering the heterogeneous light distribution in a foliage made by geometrical feature of plants would overestimate the photosynthetic carbon gain by +40% even at the canopy surface, and (2) the steep leaf angle in B. ermanii avoided midday depression of photosynthesis while the rather horizontal leaves in Q. crispula received excess light and heat load which led larger midday depression of photosynthesis. In addition to the large capacity of photosynthetic productivity of the canopy top foliage, our model also suggests the functional role of shrub species in forest ecosystem carbon gain, due to their high photosynthetic utilization efficiency of low light incidence available in the forest understory.
Information related to land surface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the land surface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatio-temporal dynamics in land surface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to land surface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of variance suggesting that the algorithm is reliable and robust, except in the tropics where some systematic differences are observed. Finally, comparisons with ground data suggest that the algorithm is performing well, but that end of season metrics associated with vegetation senescence and dormancy have higher uncertainties than start of season metrics.
An increasing number of studies have reported on shifts in timing and length of the growing season, based on phenological, satellite and climatological studies. The evidence points to a lengthening of the growing season of ca. 10–20 days in the last few decades, where an earlier onset of the start is most prominent. This extension of the growing season has been associated with recent global warming. Changes in the timing and length of the growing season (GSL) may not only have far reaching consequences for plant and animal ecosystems, but persistent increases in GSL may lead to long-term increases in carbon storage and changes in vegetation cover which may affect the climate system. This paper reviews the recent literature concerned with GSL variability.
The North China Plain (NCP) is one of the major winter wheat (Triticum aestivum L.) producing areas in China. Current wheat yields in the NCP stabilize around 5 Mg ha−1 while the demand for wheat in China is growing due to the increase in population and the change in diet. Since options for area expansion of winter wheat are limited, the production per unit of area need to be increased. The objective of this study is to quantify the production potential of winter wheat in the NCP taking into account the spatial and temporal variability caused by climate. We use a calibrated crop growth simulation model to quantify wheat yields for potential and water-limited production situations using 40 years of weather data from 32 meteorological stations in the NCP. Simulation results are linked to a Geographic Information System (GIS) facilitating their presentation and contributing to the identification of hotspots for interventions aimed at yield improvements. In the northern part of the NCP, average simulated potential yields of winter wheat go up to 9.7 Mg ha−1, while average water-limited yields only reach 3 Mg ha−1. In the southern part of the NCP, both average potential and water-limited yields are about 7.5 Mg ha−1. Rainfall is the limiting factor to winter wheat yields in the northern part of the NCP, while in the southern part, the joint effect of low radiation and high temperature are major limiting factors. Temporal variation in potential yields throughout the NCP is low in contrast with the temporal variation in water-limited yields, which is especially great in the northern part. The study calls for the collection of location-specific and disaggregated irrigated and rainfed wheat yield statistics in the NCP facilitating the identification of hotspots for improvement of current wheat yields.