[Show abstract][Hide abstract] ABSTRACT: Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the global carbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal variation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporal scales. In this context, understanding the strengths and weaknesses of remote sensing-based models of GPP and improving their performance is a key contemporary scientific activity. We used measurements from 157 research sites (~470 site-years) in the FLUXNET "La Thuile" data and compared the skills of 11 different remote sensing models in capturing intra- and inter-annual variations in daily GPP in seven different biomes. Results show that the models were able to capture significant intra-annual variation in GPP (Index of Agreement. = 0.4-0.80) in all biomes. However, the models' ability to track inter-annual variation in daily GPP was significantly weaker (IoA. <. 0.45). We examined whether the inclusion of different mechanisms that are missing in the models could improve their predictive power. The mechanisms included the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differential rates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmental variables, better representation of soil-moisture dynamics, and allowing spatial variation in model parameters. Our analyses suggest that the next generation remote sensing models need better representation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studies may not have significant bearing on model performance at continental and global scales. Understanding the relative controls of biotic vis-a-vis abiotic factors on GPP and accurately scaling up leaf level processes to the ecosystem scale are likely to be important for recognizing the limitations of remote sensing model and improving their formulation.
[Show abstract][Hide abstract] ABSTRACT: Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model-based phenology representations fail to capture local-to-regional scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground-based observations to estimate models that better represent how community-level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA-National Phenology Network, and satellite remote sensing-based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the Eastern United States. Specifically, we evaluated two different approaches: (1) using species-specific models in combination with species composition information to "upscale" model predictions, and (2) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species-specific models. More generally, results from this analysis demonstrate how in-situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models. This article is protected by copyright. All rights reserved.
Global Change Biology 10/2015; DOI:10.1111/gcb.13122 · 8.04 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.
[Show abstract][Hide abstract] ABSTRACT: Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.
International Journal of Applied Earth Observation and Geoinformation 06/2015; 38. DOI:10.1016/j.jag.2015.01.014 · 3.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.
[Show abstract][Hide abstract] ABSTRACT: Time series of multispectral images are widely used to monitor and map land cover. However, high dimensionality and missing data present significant challenges for classification algorithms that use multi-temporal remotely sensed data. Further, generation and assessment of high quality training data, including detection of outliers and changed pixels in training data, is difficult. In this paper we present a new statistical framework that is based on a parametric model that enables a targeted principal component analysis (PCA) to reduce the dimensionality of multi-temporal remote sensing data. In doing so, the model provides a novel basis for land cover classification and evaluating the nature and quality of training data used for supervised classifications. The methodology we describe uses a Kronecker operator to reduce the spectral dimensionality of multi-temporal images while preserving their temporal structure, thereby providing low-dimensional data that is well-suited for classification and outlier detection problems. As part of our framework, we use an expectation–maximization method to impute missing data, and propose new metrics that characterize the representativeness and pixel-to-pixel homogeneity of training sites used for supervised classification. To evaluate our approach, we use data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and extracted more than 200 training sites where the land cover has been characterized from high spatial resolution imagery. The original input data was composed of 196 features (28 dates × 7 bands), and the PCA-based approach we describe captured 91% of the variance, in these 7 bands, in 3 components. Results from maximum likelihood classification show that the retained principal components successfully distinguish land cover classes from one another, with classification results that were comparable to supervised machine learning methods applied to the original MODIS data. Analysis of our site composition metrics show that they successfully characterize the homogeneity (or lack thereof) and representativeness of individual pixels and entire sites relative to other training sites in the same class.
ISPRS Journal of Photogrammetry and Remote Sensing 11/2014; 97:219–228. DOI:10.1016/j.isprsjprs.2014.09.004 · 3.13 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.
[Show abstract][Hide abstract] ABSTRACT: Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 08/2014; 7(8). DOI:10.1109/JSTARS.2014.2344630 · 3.03 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Rapid urban expansion is a major contributor to environmental change in many parts of the world. This study investigated land cover changes that occurred between 1988 and 2010 in the Calgary–Edmonton corridor in Alberta, a region that has undergone considerable recent urban expansion. We used satellite imagery to develop land cover maps for four different snapshots in time between 1988 and 2010 and used these maps to investigate two principal questions related to urban expansion: (1) How did urban expansion affect other land cover types? and (2) How did urban expansion affect the availability of high-quality agricultural land in the region? Our results show that 60 % of new urban and peri-urban growth between 1988 and 2010 occurred on agricultural land. Nevertheless, total agricultural land increased in the region because of the greater clearing of natural vegetation for agriculture away from the urban core. Urban expansion predominantly occurred on soils that were highly suitable for farming, while new agricultural expansion occurred on soils of poorer quality. As a result, the average soil quality of land used for agriculture has declined in the Calgary–Edmonton corridor, confirming other studies of the food security implications of urbanization.
[Show abstract][Hide abstract] ABSTRACT: The timing of phenological events exerts a strong control over ecosystem function and leads to multiple feedbacks to the climate system1. Phenology is inherently sensitive to temperature (although the exact sensitivity is disputed2) and recent warming is reported to have led to earlier spring, later autumn3,4 and increased vegetation activity5,6. Such greening could be expected to enhance ecosystem carbon uptake7,8, although reports also suggest decreased uptake for boreal forests4,9. Here we assess changes in phenology of temperate forests over the eastern US during the past two decades, and quantify the resulting changes in forest carbon storage. We combine long-term ground observations of phenology, satellite indices, and ecosystem-scale carbon dioxide flux measurements, along with 18 terrestrial biosphere models. We observe a strong trend of earlier spring and later autumn. In contrast to previous suggestions4,9 we show that carbon uptake through photosynthesis increased considerably more than carbon release through respiration for both an earlier spring and later autumn. The terrestrial biosphere models tested misrepresent the temperature sensitivity of phenology, and thus the e�ect on carbon uptake. Our analysis of the temperature–phenology–carbon coupling suggests a current and possible future enhancement of forest carbon uptake due to changes in phenology. This constitutes a negative feedback to climate change, and is serving to slow the rate of warming.
[Show abstract][Hide abstract] ABSTRACT: More than 12 years of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). As this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250–500 m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500 m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel's annual time series to annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500 m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer's accuracy of 85%. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of deforestation within a MODIS pixel increases, but that relatively small changes in forest cover were still detectable from MODIS. Annually, over 80% of pixels with >20% deforested area were correctly identified and the timing of change showed good agreement with reference data. Errors of commission were largely associated with pixels located at the edges of disturbance events and inadequate characterization of land cover changes unrelated to deforestation in the reference data. Although our case studies focused on forests, this method is not specific to detection of forest cover change and has the potential to be applied to other types of land cover change including urban and agricultural expansion and intensification.
International Journal of Applied Earth Observation and Geoinformation 06/2014; 29(1):78–92. DOI:10.1016/j.jag.2014.01.004 · 3.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: By the end of this century, mean annual temperatures in the Northeastern United States are expected to warm by 3–5 °C, which will have significant impacts on the structure and function of temperate forests in this region. To improve understanding of these impacts, we exploited two recent climate anomalies to explore how the springtime phenology of Northeastern temperate deciduous forests will respond to future climate warming. Specifically, springtime temperatures in 2010 and 2012 were the warmest on record in the Northeastern United States, with temperatures that were roughly equivalent to the lower end of warming scenarios that are projected for this region decades from now. Climate conditions in these two years therefore provide a unique empirical basis, that complements model-based studies, for improving understanding of how northeastern temperate forest phenology will change in the future. To perform our investigation, we analyzed near surface air temperatures from the United States Historical Climatology Network, time series of satellite-derived vegetation indices from NASA's Moderate Resolution Imaging Spectroradiometer, and in situ phenological observations. Our study region encompassed the northern third of the eastern temperate forest ecoregion, extending from Pennsylvania to Canada. Springtime temperatures in 2010 and 2012 were nearly 3 °C warmer than long-term average temperatures from 1971–2000 over the region, leading to median anomalies of more than 100 growing degree days. In response, satellite and ground observations show that leaf emergence occurred up to two weeks earlier than normal, but with significant sensitivity to the specific timing of thermal forcing. These results are important for two reasons. First, they provide an empirical demonstration of the sensitivity of springtime phenology in northeastern temperate forests to future climate change that supports and complements model-based predictions. Second, our results show that subtle differences in the character of thermal forcing can substantially alter the timing of leaf emergence and canopy development. By explicitly comparing and contrasting the timing of thermal forcing and leaf phenology in 2010 and 2012, we show that even though temperatures were warmer in 2012 than in 2010, the nature and timing of thermal forcing in 2010 lead to leaf emergence that was almost a week earlier than 2012.
Environmental Research Letters 05/2014; 9(5):054006. DOI:10.1088/1748-9326/9/5/054006 · 3.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: MODIS Collection 5 land cover product (MCD12Q1) provides annually updated global land cover maps since 2001. This time series product has become an essential data source for the generation of many other land surface products and for a variety of regional and global studies. However, classification errors are inherent in the land cover product, which can misrepresent land cover transitions. In particular, land cover transitions are illogical if they contradict ecological rules and are unlikely to be observed. In this study, we evaluated the MODIS land cover product by analyzing the nature and magnitude of its illogical land cover transitions using annual MCD12Q1 land cover maps from 2001 to 2010. Our analysis revealed that illogical transitions exist in the product for all consecutive years, and are distributed most commonly in several regions over the world. To enhance the MODIS land cover product, we applied a spatial–temporal modeling algorithm that incorporates expert knowledge to reduce illogical transitions on five such “hotspot” tiles. The results showed substantial improvements in both accuracy and consistency of the land cover product using the spatial–temporal modeling algorithm. The percentage of illogical transitions in each of the five tiles was significantly reduced among consecutive years and across the entire time series. This study demonstrates the effectiveness of the spatial–temporal modeling algorithm for producing high quality time series of land cover maps, and also highlights the importance of temporal consistency in land cover mapping.
[Show abstract][Hide abstract] ABSTRACT: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10-80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40-60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.
[Show abstract][Hide abstract] ABSTRACT: As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.
[Show abstract][Hide abstract] ABSTRACT: Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.
[Show abstract][Hide abstract] ABSTRACT:  The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS.
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Impacts of climate change and urbanization on phenology have been documented around the globe, with considerable implications for ecosystem structure and function. Two continental-scale observation networks, Phenocam (http://phenocam.sr.unh.edu) and the USA National Phenology Network (USA-NPN; http://www.usanpn.org) are working with the National Ecological Observatory Network (NEON) to develop phenological monitoring protocols and explore new opportunities for synergistic phenology research. Phenocam, a network of 130+ Internet-linked cameras distributed across North America, uses high frequency canopy monitoring for the development of predictive models of plant phenology and associated ecosystem services. The USA-NPN leverages the efforts of volunteers and professional scientists across the United States to construct a diverse database on plant and animal phenology to support research, management, education and conservation needs. Both organizations are collaborating with NEON, a continental-scale ecological observing system, to enhance and codify phenology data collection, processing and dissemination over its 30-year life span.
Members of the Phenocam network are working with NEON to establish protocols for camera operation and develop algorithms to estimate phenophase transition dates from imagery time series. Camera-based monitoring supplements organismal (in-situ, ground-based) field observations collected by both NEON and USA-NPN (according to a common set of standardized protocols). A key element of these protocols is the designation of cross-biome phenophase definitions, allowing seamless integration of NEON phenology data with standardized USA-NPN data sets. Organismal data collection at NEON sites is being designed to describe both interspecific and intraspecific variation in plant phenology, and to facilitate both population- and community-level research.
The joint efforts of NEON, Phenocam and USA-NPN will bridge two major knowledge gaps in the field of phenology. First, cameras will provide a tool to harmonize phenophase observations of individual plants to the synoptic-scale observations of remote sensing platforms and enhance techniques for validation of satellite derived land surface phenology products. Second, development of multi-scale phenology data sets at NEON eddy covariance sites will facilitate investigation of the feedbacks between ecosystem phenology and carbon/water/energy fluxes between the biosphere and atmosphere. Intermediate products include protocols for linking organisms to cameras, and cameras to satellites. Ultimately, protocols for integrating data across all scales, for a variety of biome types, will contribute to the development of a common, dynamic database of cross-scale phenology datasets, as well as complementary datasets (e.g., climatology, land-cover) for research, education and management applications.
[Show abstract][Hide abstract] ABSTRACT: Gross primary productivity (GPP) is the largest and most variable
component of the global terrestrial carbon cycle. Repeatable and
accurate monitoring of terrestrial GPP is therefore critical for
quantifying dynamics in regional-to-global carbon budgets. Remote
sensing provides high frequency observations of terrestrial ecosystems
and is widely used to monitor and model spatiotemporal variability in
ecosystem properties and processes that affect terrestrial GPP. We used
data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and
FLUXNET to assess how well four metrics derived from remotely sensed
vegetation indices (hereafter referred to as proxies) and six remote
sensing-based models capture spatial and temporal variations in annual
GPP. Specifically, we used the FLUXNET "La Thuile" data set, which
includes several times more sites (144) and site years (422) than
previous efforts have used. Our results show that remotely sensed
proxies and modeled GPP are able to capture statistically significant
amounts of spatial variation in mean annual GPP in every biome except
croplands, but that the total variance explained differed substantially
across biomes (R2 ≈ 0.1-0.8). The ability of remotely
sensed proxies and models to explain interannual variability GPP was
even more limited. Remotely sensed proxies explained 40-60% of
interannual variance in annual GPP in moisture-limited biomes including
grasslands and shrublands. However, none of the models or remotely
sensed proxies explained statistically significant amounts of
interannual variation in GPP in croplands, evergreen needleleaf forests,
and deciduous broadleaf forests. Because important factors that affect
year-to-year variation in GPP are not explicitly captured or included in
the remote sensing proxies and models we examined (e.g., interactions
between biotic and abiotic conditions, and lagged ecosystems responses
to environmental process), our results are not surprising. Nevertheless,
robust and repeatable characterization of interannual variability in
carbon budgets is critically important and the carbon cycle science
community is increasingly relying on remotely sensing data. As larger
and more comprehensive data sets derived from the FLUXNET community
become available, additional systematic assessment and refinement of
remote sensing-based methods for monitoring annual GPP is warranted.