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Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States

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Abstract

Satellite remote sensing provides continuous observations of land surfaces, thereby offering opportunities for large-scale monitoring of terrestrial productivity. Production Efficiency Models (PEMs) have been widely used in satellite-based studies to simulate carbon exchanges between the atmosphere and ecosystems. However, model parameterization of the maximum light use efficiency ( ) varies considerably for croplands in agricultural studies at different scales. In this study, we evaluate cropland in the MODIS Gross Primary Productivity (GPP) model (MOD17) using in situ measurements and inventory datasets across the Midwestern US. The site-scale calibration using 28 site-years tower measurements derives values of 2.78 ± 0.48 gC MJ−1 (± standard deviation) for corn and 1.64 ± 0.23 gC MJ−1 for soybean. The calibrated models could account for approximately 60–80% of the variances of tower-based GPP. The regional-scale study using 4-year agricultural inventory data suggests comparable values of 2.48 ± 0.65 gC MJ−1 for corn and 1.18 ± 0.29 gC MJ−1 for soybean. Annual GPP derived from inventory data (1848.4 ± 298.1 gC m−2 y−1 for corn and 908.9 ± 166.3 gC m−2 y−1 for soybean) are consistent with modeled GPP (1887.8 ± 229.8 gC m−2 y−1 for corn and 849.1 ± 122.2 gC m−2 y−1 for soybean). Our results are in line with recent studies and imply that cropland GPP is largely underestimated in the MODIS GPP products for the Midwestern US. Our findings indicate that model parameters (primarily ) should be carefully recalibrated for regional studies and field-derived can be consistently applied to large-scale modeling as we did here for the Midwestern US.

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... For example, Wang et al. [9] calculated LUE max values ranging from approximately 2.3 to 3.7 gC/MJ/day for maize and 1.4 gC/MJ/day for wheat from flux tower sites. Similarly, Xin et al. [24] calculated mean values of 2.78 and 1.64 gC/MJ/day for maize and soybean, respectively. These values are generally greater than estimates of LUE max for global or continental products such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP, but less than values generated in field experiments. ...
... These values are generally greater than estimates of LUE max for global or continental products such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP, but less than values generated in field experiments. This has led to an underestimation of cropland GPP in continental products and overestimation of GPP when fixed LUE max values are used in cropland-specific applications [7,24,25]. ...
... This area was chosen across sites because flux tower height was generally set at a level which best captured the immediate field and eliminated surrounding landscapes [44], and this area generally fell within the target fields while enabling consistent spatial sampling from each site. This area was also consistent with former studies on flux tower sites which have used both Landsat and MODIS resolutions and therefore enhanced comparability of error across approaches [22,24,27,45]. The mean of values across the 10 × 10 matrix were taken for all remotely sensed variables to generate the final dataset with the number of observations shown in Table 1. ...
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Gross Primary Productivity (GPP) for cropland is often estimated using a fixed value for maximum light use efficiency (LUEmax) which is reduced to light use efficiency (LUE) by environmental stress scalars. This may not reflect variation in LUE within a crop season, and environmental stress scalars developed for ecosystem scale modelling may not apply linearly to croplands. We predicted LUE on several vegetation indices, crop type, and agroclimatic predictors using supervised random forest regression with training data from flux towers. Using a fixed LUEmax and environmental stress scalars produced an overestimation of GPP with a root mean square error (RMSE) of 6.26 gC/m2/day, while using predicted LUE from random forest regression produced RMSEs of 0.099 and 0.404 gC/m2/day for models with and without crop type as a predictor, respectively. Prediction uncertainty was greater for the model without crop type. These results show that LUE varies between crop type, is dynamic within a crop season, and LUE models that reflect this are able to produce much more accurate estimates of GPP over cropland than using fixed LUEmax with stress scalars. Therefore, we suggest a paradigm shift from setting the LUE variable in cropland productivity models based on environmental stress to focusing more on the variation of LUE within a crop season.
... Values in the range from 0.86 to 2.5 (gC m −2 d −1 MJ −1 ) have been reported for grassland and crop ε max in the literature [45][46][47]). Various definitions also exists for it in different communities and in different modelling setups (e.g., depending on weather non-photosynthetic tissues are included in the definition of FAPAR) [48]. ...
... As a result, crop and grass ε max was set to 1.7 and 1.1 gC m −2 d −1 MJ −1 , respectively. It is noted that ε max of crops was estimated using sites where C3 crops were numerically more represented (only one year of maize, a C4 crop, is present for the site BE-Lon) and may thus underestimate the ε max of C4 crops (maize in our data sample) (Xin et al., 2015). However, the vast majority of the crop samples is indeed represented by C3 crops. ...
... A plausible explanation for this difference may be related to an underestimate of the maximum light use efficiency (ε m ) that is not able to reproduce the photosynthesis level attained by the C4 crop under a Mediterranean climate and served by irrigation. In fact, both the Sim model and MOD17 use a constant ε m of all crops that is likely not representative for this site because the ε m of C4 crops is typically larger than that of C3 crops [47]. However, it is noted that in different climatic conditions the model does not underestimate C4 GPP, as for example in the cropland site DE-Kli of Figure 2 where maize was grown in year 2007 and 2012. ...
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The application of detailed process-oriented simulation models for gross primary production (GPP) estimation is constrained by the scarcity of the data needed for their parametrization. In this manuscript, we present the development and test of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Normalized Difference Vegetation Index (NDVI) observations into a simple process-based model driven by basic meteorological variables (i.e., global radiation, temperature, precipitation and reference evapotranspiration, all from global circulation models of the European Centre for Medium-Range Weather Forecasts). The model is run at daily time-step using meteorological forcing and provides estimates of GPP and LAI, the latter used to simulate MODIS NDVI though the coupling with the radiative transfer model PROSAIL5B. Modelled GPP is compared with the remote sensing-driven MODIS GPP product (MOD17) and the quality of both estimates are assessed against GPP from European eddy covariance flux sites over crops and grasslands. Model performances in GPP estimation (R ² = 0.67, RMSE = 2.45 gC m ⁻² d ⁻¹ , MBE = -0.16 gC m ⁻² d ⁻¹ ) were shown to outperform those of MOD17 for the investigated sites (R 2 = 0.53, RMSE = 3.15 gC m ⁻² d ⁻¹ , MBE = -1.08 gC m ⁻² d ⁻¹ ).
... Other models have been proposed in literature to ameliorate GPP MODIS [11]. A more parsimonious approach is the improvement of the maximum radiation use efficiency (RUE max ) and the fraction of absorbed photosynthetic active radiation (fPAR) estimates with field measurements and EC-derived GPP, as well as the use of land use maps [12][13][14][15]. In an analysis of GPP MODIS for Africa, it was stated that the MODIS biome parameters need further improvement [16]. ...
... This is also true for other biomes worldwide [17]. Accordingly, new input parameters were used to up-scale GPP from field to whole regions in the Midwestern US using MODIS products [12,13]. However, previous studies in the Midwestern US were either constrained by the density of ground-based EC stations (i.e., number of EC stations per area), or by the lack of field-based measurements of RUE max or fPAR [12,13]. ...
... Accordingly, new input parameters were used to up-scale GPP from field to whole regions in the Midwestern US using MODIS products [12,13]. However, previous studies in the Midwestern US were either constrained by the density of ground-based EC stations (i.e., number of EC stations per area), or by the lack of field-based measurements of RUE max or fPAR [12,13]. In addition, new land use maps were developed to distinguish crop fields with higher accuracy [18]. ...
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The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( x ¯ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m−2 for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m−2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ−1 for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m−2 d−1, for corn and soybean, respectively, which is an improvement to the GPPMODIS product (2.44 and 3.30 g C m−2 d−1, respectively). Corn yield, calculated from GPPcalc (12.82 ± 0.65 Mg ha−1), corresponded well to official yield data (13.09 ± 0.09 Mg ha−1), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha−1). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.
... Therefore, a fixed value for LUE max within a specific biome is usually given for PEM applications, such as the MODIS GPP/NPP algorithm (MO17A2) [5], the Carnegie-Ames-Stanford Approach biogeochemical model [6], and the Vegetation Photosynthesis Model (VPM) [7]. However, studies have demonstrated that a fixed value for LUE max is a source of error in vegetation productivity estimation [4], [8]- [11]. The problem is mainly due to the different expressions of environmental stresses on LUE max in different PEMs [12], [13]. ...
... These indicate that LUE max in a specific PEM can be smaller than in theory [4] and may be different in various kinds of PEMs even within the same plant functional type and region [12]- [14]. For crops, a few studies have demonstrated that LUE max varies with crop cultivar, number of ratoons, and spatial scale [2], [11], [15]. Therefore, deriving LUE max for a specific PEM under spatially heterogeneous conditions based on remote sensing may improve the accuracy of crop productivity estimation [11], [16], [17]. ...
... For crops, a few studies have demonstrated that LUE max varies with crop cultivar, number of ratoons, and spatial scale [2], [11], [15]. Therefore, deriving LUE max for a specific PEM under spatially heterogeneous conditions based on remote sensing may improve the accuracy of crop productivity estimation [11], [16], [17]. ...
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Maximum light use efficiency (LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> from satellite remote sensing data. LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> , our results suggest that LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> from remote sensing observations. To evaluate the quality of LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax. Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax . This study offers a new way to derive LUE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.
... Numerous models have been developed to approximate or simulate terrestrial GPP, and they fall broadly into three categories: 1) models based on calculating essential ecological processes Peng et al., 2013;Wagle et al., 2016b); 2) satellite-based GPP models derived by satellite remote sensing data and climatic variables (Horn and Schulz, 2011;Wang et al., 2010;Yuan et al., 2019); 3) models based on a byproduct of plant photosynthesis process (i.e., sun-induced chlorophyll fluorescence) (Gitelson et al., 2006;Wagle et al., 2016b). Among those prognostic models, satellite-based models estimate GPP using simple algorithms and satellite remote sensing data, which are recognized as the widely used methods for mapping temporal-spatial variations of regional or global GPP (Xin et al., 2015;Yuan et al., 2010). Satellite-based GPP models can be further divided into two subcategories. ...
... Actually, the capacity of vegetation carbon fixed varies greatly within biomes Wagle et al., 2016a). Site-levels studies have pointed out that the ε max for C 3 crops ranged from 1.43 to 1.96 g C MJ − 1 , and ranged from 2.25 to 4.06 g C MJ − 1 for C 4 crops (Xin et al., 2015;Yuan et al., 2015). Besides, global and regional GPP or net primary productivity (NPP) assessment of agroecosystems still exists considerable uncertainty due to different methods and the diversity of crops and cropping systems (Sjöström et al., 2013;Turner et al., 2003). ...
... A strong correlation between EVI and GPP EC has been found in recent years (Nagler et al., 2005;Wagle et al., 2016a). Besides, EVI has been found to outperform other vegetation indices, like NDVI in explaining the seasonal variability of cropland carbon exchange, because it minimizes the effects of residual atmospheric contamination and considers soil background reflectance variation (Bandaru et al., 2013;Huete and Didan, 2004;Kalfas et al., 2011;Xin et al., 2015). Vina and Gitelson (2005) reported a significantly decreased sensitivity of NDVI to fPAR when the latter exceeded 0.7. ...
Article
Satellite-based gross primary productivity (GPP) models have been widely used for simulating carbon exchanges of terrestrial ecosystems. However, the performances of various GPP models in agroecosystems have been rarely explored. In this study, we calibrated the model parameters and compared the performances of seven light use efficiency (LUE-GPP) models and five vegetation-index (VI-GPP) models for simulating daily GPP of agroecosystems over 106 crop growing seasons, and examined the effects of model structure on model performance. The simulations were carried out based on 19 eddy covariance (EC) sites from the global flux network and vegetation indices obtained from MODIS. The calibrated potential LUE (εmax) for C4 crop (summer maize, 2.59±0.94 g C MJ⁻¹) was higher than that for C3 crops (1.42±0.58 g C MJ⁻¹) in any LUE-GPP models. The performances of models differed across the crops. Generally, all models performed better for C3 crops than C4 crops, and for winter crops (winter wheat-Triticum aestivum L, rape-Brassica napus L, and winter barley-Hordeum vulgare L) than summer crops (summer maize-Zea mays L, potato-Solanum tuberosum L, rice-Oryza sativa L. and soybean-Glycine max (L.) Merr.). Cloudiness index-LUE (CI-LUE) model outperformed the other LUE-GPP models, and vegetation index (VEI) model outperformed the other VI-GPP models. LUE-GPP models demonstrated better performance than VI-GPP models due to the inclusion of water stress (Ws) and temperature stress (Ts). A comparison of the model structures showed that models only considering the effects of Ws produced smaller errors than those only considering the effects of Ts in simulating GPP. Ws algorithms generated the larger variations in LUE-GPP models compared to those of Ts, especially during the drought period. All models obtained higher R² and smaller errors using the minimum method (Min (Ts, Ws)) than using the multiplication method (Ts × Ws) to integrate the effects of Ts and Ws on GPP, which suggested that the minimum method was better than the multiplication method to integrate Ts and Ws on LUE. These results showed that satellite-based models with calibrated crop-specific parameters have the potential to serve as the basis for estimation of agroecosystem GPP, and can provide direction for future model structure optimization.
... In addition to adjusted E max , satellite-derived fPAR is a good indicator of productive potential, and has been widely used to provide powerful information on the spatial and temporal detail of primary productivity. It improves seasonal phasing of productivity increases and decreases (Christian et al., 2015;Running et al., 2000;Xin et al., 2015), and representing features such as agricultural planting and harvest (Bradford et al., 2005;Xin et al., 2015), deciduous leaf loss (Xiao et al., 2004), and complex foliage heterogeneity (Shabanov et al., 2003). Somewhat surprisingly, the simple temporal downscaling of monthly productivity and respiration provided Journal of Geophysical Research: Biogeosciences similar or better skill in capturing diurnal carbon fluxes compared to models with a more detailed representation of fast processes such as 10 to 15 min (e.g., SiB3CSU; Baker et al., 2008;Baker et al., 2013) variations in canopy physiology (canopy conductance and assimilation) as it responds to light, temperature, humidity, and soil water. ...
... In addition to adjusted E max , satellite-derived fPAR is a good indicator of productive potential, and has been widely used to provide powerful information on the spatial and temporal detail of primary productivity. It improves seasonal phasing of productivity increases and decreases (Christian et al., 2015;Running et al., 2000;Xin et al., 2015), and representing features such as agricultural planting and harvest (Bradford et al., 2005;Xin et al., 2015), deciduous leaf loss (Xiao et al., 2004), and complex foliage heterogeneity (Shabanov et al., 2003). Somewhat surprisingly, the simple temporal downscaling of monthly productivity and respiration provided Journal of Geophysical Research: Biogeosciences similar or better skill in capturing diurnal carbon fluxes compared to models with a more detailed representation of fast processes such as 10 to 15 min (e.g., SiB3CSU; Baker et al., 2008;Baker et al., 2013) variations in canopy physiology (canopy conductance and assimilation) as it responds to light, temperature, humidity, and soil water. ...
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Accurate and fine‐scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedbacks to the climate system. Currently available data products from flux towers and model‐intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed‐parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3‐hourly scales for North America at ~500 m and 5 km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light‐use‐efficiency (Emax), optimal temperature of photosynthesis (Topt), and temperature response of respiration (Q10). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned Emax with site‐level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site‐level results at both monthly and 3‐hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. The ensemble's seasonality agrees well with other models for most biome types and in high‐ and mid‐latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.
... They also reported that the decrease in 117 GPP during summer was much larger than the increase of spring GPP, resulting in a moderate 118 loss of annual GPP (-0.38 Pg C) over CONUS in 2012. However, there are large uncertainties 119 among the various GPP products (Schaefer et al. 2012); for example, the MOD17 GPP 120 product has large uncertainties in croplands (Turner et al. 2006;Xin et al. 2015). Therefore, 121 there is a need to evaluate various GPP models and their GPP data products, which will help 122 us to better understand and assess GPP responses to spring warming and summer drought in 123 2012. ...
... 260capture environmental limitations such as vapor pressure deficit and air temperature. ε max 261 values are specific for different biome types (e.g., forest, shrub, grass, crop)(Running et al. 262 2004), but the product does not account for the differences of ε max between C 3 and C 4 263 croplands, and ε max for croplands is substantially too low(Turner et al. 2006;Xin et al. 2015).264 We also compared GPP VPM with GPP simulated by CASA-GFED3 (GPP CASA ). ...
Article
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Large spatial‐scale effects of climate extremes on gross primary production (GPP), the largest terrestrial carbon flux, are highly uncertain even as these extremes increase in frequency and extent. Here we report the impacts of spring warming and summer drought in 2012 on GPP across the contiguous United States (CONUS) using estimates from four GPP models: Vegetation Photosynthesis Model (VPM), MOD17A2H V006, Carnegie‐Ames‐Stanford Approach, and Simple Biosphere/Carnegie‐Ames‐Stanford Approach. VPM simulations are driven by Moderate Resolution Imaging Spectroradiometer, North American Regional Reanalysis climate data, and C3 and C4 cropland maps from the United States Department of Agriculture Cropland Data Layer data set. Across 25 eddy covariance flux tower sites, GPP estimates from VPM (GPPVPM) showed better accuracy in terms of cross‐site variability and interannual variability (R2 = 0.84 and 0.46, respectively) when compared to MOD17 GPP. We further assessed the spatial and temporal (seasonal) consistency between GPP products and the Global Ozone Monitoring Experiment‐2 solar‐induced chlorophyll fluorescence over CONUS during 2008–2014. The results suggested that GPPVPM agrees best with solar‐induced chlorophyll fluorescence across space and time, capturing seasonal dynamics and interannual variations. Anomaly analyses showed that increased GPP during the spring compensated for the reduced GPP during the summer, resulting in near‐neutral changes in annual GPP for the CONUS. This study demonstrates the importance of assessing the impacts of different types and timing of climate extremes on GPP and the need to improve light use efficiency models by incorporating C3 and C4 plant functional types.
... However, the use of correct LULC classes did not guarantee significant improvements in GPP estimates, indicating deficiencies in the BPLUT parameters. Inferred ε max calculated from tower data and MODIS FAPAR showed considerable differences between the ε max prescribed in the MOD17 algorithm, whose uncertainties have been attributed to under-or over-predictions of MOD17 GPP in several biome types (Wang et al., 2017a;Xin et al., 2015). ...
... For instance, croplands are very diverse, yet the same set of parameters is applied indiscriminately to croplands everywhere (Zhao et al., 2011). This introduces large uncertainties for some crops with different LUE, as observed by Xin et al. (2015) in corn and soybean plantations. ...
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Tropical forests and savannas are responsible for the largest proportion of global Gross Primary Productivity (GPP), a major component of the global carbon cycle. However, there are still deficiencies in the spatial and temporal information of tropical photosynthesis and its relations with environmental controls. The MOD17 product, based on the Light Use Efficiency (LUE) concept, has been updated to provide GPP estimates around the globe. In this research, the MOD17 GPP collections 5.0, 5.5 and 6.0 and their sources of uncertainties were assessed by using measurements of meteorology and eddy covariance GPP from eight flux towers in Brazilian tropical ecosystems, from 2000 to 2006. Results showed that the MOD17 collections tend to overestimate GPP at low productivity sites (bias between 111% and 584%) and underestimate it at high productivity sites (bias between −2% and −18%). Overall, the MOD17 product was not able to capture the GPP seasonality, especially in the equatorial sites. Recalculations of MOD17 GPP using site-specific meteorological data, corrected land use/land cover (LULC) classification, and tower-based LUE parameter showed improvements for some sites. However, the improvements were not sufficient to estimate the GPP seasonality in the equatorial forest sites. The use of a new soil moisture constraint on the LUE, based on the Evaporative Fraction, just showed improvements in water-limited sites. Modifications in the algorithm to account for separate LUE for cloudy and clear sky days presented noticeably improved GPP estimates in the tropical ecosystems investigated, both in magnitude and in seasonality. The results suggest that the high cloudiness makes the diffuse radiation an important factor to be considered in the LUE control, especially over dense forests. Thus, the MOD17 GPP algorithm needs more updates to accurately estimate productivity in tropical ecosystems. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
... This is a common problem of spectral remote sensing proxies used for the estimation of canopy biochemical, physiological and structural canopy traits (Gitelson et al. 2014). The second reason for the underestimation by the MOD17 gr , is seen in the too low e max term for crops used by the MODIS GPP/NPP algorithm (Chen et al. 2011;Bandaru et al. 2013;Xin et al. 2015). found e max to be 50% higher in C 3 soybean and 250% higher in C 4 maize than the value used in the MODIS model. ...
Article
To include within-canopy leaf acclimation responses to light and other resource gradients in photosynthesis modelling, it is imperative to understand the variation of leaf structural, biochemical and physiological traits from canopy top to bottom. In the present study, leaf photosynthetic traits for top and bottom canopy leaves, canopy structure and light profiles, were measured over one growing season for two contrasting crop types, winter barley (Hordeum vulgare L.) and rape seed (Brassica napus L.). With the exception of quantum yield, other traits such as maximum photosynthetic capacity (Amax), dark respiration, leaf nitrogen and chlorophyll contents, and leaf mass per area, showed consistently higher (P < 0.05) values for top leaves throughout the growing season and for both crop types. Even though Amax was higher for top leaves, the bottom half of the canopy intercepted more light and thus contributed the most to total canopy photosynthesis up until senescence set in. Incorporating this knowledge into a simple top/bottom-leaf upscaling scheme, separating top and bottom leaves, resulted in a better match between estimated and measured total canopy photosynthesis, compared with a one-leaf upscaling scheme. Moreover, aggregating to daily and weekly temporal resolutions progressively increased the linearity of the leaf photosynthetic responses to light for top leaves.
... The maximum light use efficiency ε max under no environmental stresses is a key parameter in the satellite-based LUE models and influences the model accuracy largely. The maximum light use efficiency is dependent on a number of factors such as vegetation type and canopy structure and could vary across sites (Xin et al. 2015;Cheng et al. 2014;Still, Randerson, and Fung 2004). Given that the LUE models are used for large-scale applications where simple parameterization scheme is preferable, all LUE models were calibrated using daily flux tower data (i.e., Ta, VPD, incoming PAR, and GPP) and remote sensing data (i.e., EVI, LSWI, and LAI) to obtain optimal ε max using the linear regression with no intercept based on the least square method (Table 2). ...
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Vegetation gross primary production (GPP), the photosynthetic yields by green plants per unit area per unit time, is a key metric of carbon flux in understanding the land–atmosphere interactions and terrestrial carbon cycles. Satellite-based light use efficiency (LUE) models are valuable methods to retrieve large-scale terrestrial GPP using remote sensing data. As studies have reported that maximum light use efficiency, a key parameter that is often assumed to be constant in the LUE models, there is a need to explore the effects of LUE seasonality on GPP simulation and ways for correction. This study proposes a method based on leaf area index to account for LUE seasonality and applies it to four different light use efficiency models (i.e., the MOD17 algorithm, the vegetation photosynthesis model, the radiation partitioning model, and the vegetation index model) for comparisons. Based on 59 site-years flux tower data from deciduous broadleaf forest sites in the United States, the results show that all models could simulate daily GPP time series well and explain more than 85.0% variance of tower-based GPP. There is, however, a tendency to overestimate GPP during the non-growing season but underestimate GPP during the growing season. By applying the correction function, GPP simulation using the LUE models improved in all experiments as indicated by increased correlation coefficients, the index of agreement and decreased root-mean-square errors. Among all models, the radiation partitioning model achieves the highest correlation coefficients between modelled and observed daily GPP likely because it considers the influences of direct and diffuse radiation partitioning on daily canopy photosynthesis. Our study indicates that satellite-based light use efficiency models could be successfully applied for deriving daily vegetation GPP and potentially producing daily routine satellite products, while considering the effects of LUE seasonality on canopy could help improve significantly the simulation accuracy of daily GPP in phenology.
... The resulting 30-m GPP simulations over MT croplands produced results that were generally similar, but more productive than the MODIS MOD17 (MOD17A2H, Collection 6) operational GPP product (Figure 4). However, the MODIS GPP product has been reported to have low productivity in croplands [18,20,53], indicating that the model results from this study benefit from a finer spatial resolution and a refined model cropland calibration. Though the Gridmet database shows favorable accuracy in relation to in situ weather station network measurements [49], the daily meteorological inputs may contribute uncertainties that propagate to cropland productivity model errors. ...
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Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.
... The ɛ max varies widely with different vegetation types (Turner et al. 2003) and with different temporal-spatial pattern of same vegetation type (Hember et al. 2010). For regional studies, the ɛ max should be carefully recalibrated and field-derived ɛ max can be consistently applied to large-scale modelling (Xin et al. 2015). However, it is hard to get accurate and representative ɛ max for regional studies through limited field inventory data and EC measurements, resulting in lager uncertainty in spatial variation of GPP estimates (Wang et al. 2010;Groenendijk et al. 2011;Keenan et al. 2012). ...
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Assessing the contribution of Moso bamboo (Phyllostachys pubescens) forest to forest ecosystem carbon storage requires accurate estimation of gross primary production (GPP). Based on measurements of light-use efficiency (LUE), defined as the ratio of measured GPP to photosynthetically active radiation (PAR), from the eddy covariance flux tower, the linear regression model and partial least squares regression model were used for estimation of LUE using the Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance data. GPP estimates were then calculated by the product of LUE estimates and PAR (named the LUE-PAR model), which was compared with GPP from the GPP algorithm designed for the MODIS sensor aboard the Aqua and Terra platforms (MOD17A2 model) and the EC-LUE model. The results revealed the PLS model performed better than the linear regression model in LUE estimation but had lager uncertainties in high and low LUE values. GPP estimates driven by a MODIS-based radiation product with high spatial resolution was more accurate than those driven by Modern-Era Retrospective Analysis for Research and Applications (MERRA) radiation product from the NASA’s Global Modelling and Assimilation Office data set. The LUE-PAR model had the highest accuracy than the other two LUE models. The GPP values derived from the EC-LUE model driven by photosynthetically active radiation (PAR) from MERRA and maximum LUE from the EC data were overestimated due to the overestimation in MERRA radiation product. The GPP values derived from the MOD17A2 model driven by PAR from the MERRA and maximum LUE from the biome properties look-up table were underestimated due to underestimation in the maximum LUE of Moso bamboo forest. This study implied that the LUE-PAR model driven by LUE estimates from the PLS model and PAR from MERRA is a superior approach in improving GPP simulations, and PAR products with high spatial resolution and accurate species-specific maximum LUE are necessary for the LUE models in estimating GPP at regional scale.
... Web of Science 中以 " net primary production /productivity " and " remote sensing " and " model " 为 主 题 , 在 CNKI 中以 " 净初级生产力 " 并且 " 遥感 " 并且 " 模型 " 为主题进行文献检索。结合已有划分方法(Ruimy et al, 1994)et al, 2016)。有学者利用年内累积 NDVI 成功构建 相应区域 NPP 估算模型, 如指数函数模型(Lo SeenChong et al, 1993)、 对数函数模型(肖乾广等, 1996)、 幂函数模型(Paruelo et al, 1997); 也有学者利用季度 NDVI 与横轴所构成的面积求取年内累积值, 进而 估算 NPP (Roces-Díaz et al, 2015Bandaru et al, 2013Wu et al, 2012Chen et al, 2008陈斌等, 2007赵志平等, 2015冀咏赞等, 2015Sasai et al, 2005He et al, 2013Wang F M et al, 2014McAdam, 2015Yuan et al, 2016Yan et al, 2015Ruimy et al, 1996King et al, 2011注:(Xin et al, 2015)。学界对于Ɛ*的取值存在争议, 因 此这方面仍有待进一步研究。(Yuan et al, 2016)、 植 被 覆 盖 度 指 数 f( 侯 湖 平 等, 2012)、 植物覆盖比(Cook et al, 2009)及 LAI()Liao et al, 2015); 基于月尺度通量数据和贝叶斯推理方法 对 SAT-TEM 模型进行参数优化(Chen et al, 2011); 基于半小时步长 BEPSHourly 模型, 利用迭代优化 的方法对最大羧化速率 Vcmax 和最大电子传递速率 Jmax进行不同组合, 以优化 BEPSHourly 模型参数(卢Liao et al, 2015Zhou et al, 2015He et al, 2015李明泽等, 2015张娜等, 2003雷慧闽等, 2012Hazarika et al, 2005*过程较复杂, 主要部分详见 http://www.ntsg.umt.edu/project/biome-bgc。 地 理 科 学 进 展 第 36 卷 伟等, 2016b); 将优化后的参数引入日步长 BEPSDaily 模 型 , 提 高 计 算 效 率 和 模 拟 精 度 ( 卢 伟 等, 2016a)等。值得一提的是, 利用基于遥感数据获取 的叶绿素含量指代 Vcmax 估算植被生产力(Houborg et al, 2015its relationship with climate factors in subtropical mountainous and hilly regions of China: A case study in Hunan Province[J]. ...
... Large equifinality between fPAR and εmax led to senseless coefficients (not shown) and had to be constrained using prior knowledge of the NDVI-LAI relationship. Some authors already reported the sensitivity of this parameter, and suggested modifying εmax to improve GPP prediction [84,85]. Retrieved parameters are quite close to those estimated for MOD17A2 in Savanna ecosystems. ...
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Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems.
... The lowest value of ε max (=1.506) was in climate zone 'Csa' which was contributed by 'IT-CA2 site with C3 crop (winter wheat). Our values of ε max parameter are within the range in previous studies found at site-level with ε max ranging from 2.25 to 4.06 g C MJ −1 for C4 crops and from 1.43 to 1.96 g C MJ −1 for C3 crops [3,71,72]. Table 5. Optimized model parameters (ε max , T opt , and VPD 0 ) of the LUE-based model for crop GPP estimate in different climate zones ('with EF' denotes our new EF-LUE model; 'without EF' means water availability constraint was not considered). ...
Article
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Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.
... To assess the influential factors, temperature, moisture, photoperiod and atmospheric compositions were investigated [33][34][35][36][37]. With regard to the consequences of the impact of urbanization on vegetation phenology, studies have assessed ecological processes, human health and economic development [38][39][40][41]. The datasets used in these studies were mainly derived from site-based observations and satellite remote sensing. ...
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Urbanization can affect the ecological processes, local climate and human health in urban areas by changing the vegetation phenology. In the past 20 years, China has experienced rapid urbanization. Thus, it is imperative to understand the impact of urbanization on vegetation phenology in China. In this study, we quantitatively analyzed the impact of urbanization on vegetation phenology at the national and climate zone scales using remotely sensed data. We found that the start of the growing season (SOS) was advanced by approximately 2.4 days (P < 0.01), and the end of the growing season (EOS) was delayed by approximately 0.7 days (P < 0.01) in the urban areas compared to the rural areas. As a result, the growing season length (GSL) was extended by approximately 3.1 days (P < 0.01). The difference in the SOS and GSL between the urban and rural areas increased from 2001 to 2014, with an annual rate of 0.2 days (R2 = 0.39, P < 0.05) and 0.2 days (R2 = 0.31, P < 0.05), respectively. We also found that the impact of urbanization on vegetation phenology varied among different vegetation types at the national and climate zone levels (P < 0.05). The SOS was negatively correlated with land surface temperature (LST), with a correlation coefficient of −0.24 (P < 0.01), and EOS and GSL were positively correlated with LST, with correlation coefficients of 0.56 and 0.44 (P < 0.01), respectively. The improved understanding of the impact of urbanization on vegetation phenology from this study will be of great help for policy-makers in terms of developing relevant strategies to mitigate the negative environmental effects of urbanization in China.
... In addition to eddy covariance fluxes, grain yield records of wheat and maize in county statistics are also used to verify the GPP predictions at the regional scale. The method of crop yield converted to GPP follows Xin et al. (2015). ...
Article
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Attributing changes in evapotranspiration (ET) and gross primary productivity (GPP) is crucial for impact and adaptation assessment of the agro-ecosystems to climate change. Simulations with the VIP model revealed that annual ET and GPP slightly increased from 1981 to 2013 over the North China Plain. The tendencies of both ET and GPP were upward in the spring season, while they were weak and downward in the summer season. A complete factor analysis illustrated that the relative contributions of climatic change, CO2 fertilization, and management to the ET (GPP) trend were 56 (−32) %, −28 (25) %, and 68 (108) %, respectively. The decline of global radiation resulted from deteriorated aerosol and air pollution was the principal cause of GPP decline in summer, while air warming intensified the water cycle and advanced the plant productivity in the spring season. Generally, agronomic improvements were the principal drivers of crop productivity enhancement.
... In addition to eddy covariance fluxes, grain yield records of wheat and maize in county statistics are also used to verify the GPP predictions at the regional scale. The method of crop yield converted to GPP follows Xin et al. (2015). ...
Article
Full-text available
Attributing changes in evapotranspiration (ET) and gross primary productivity (GPP) is crucial for impact and adaptation assessment of the agro-ecosystems to climate change. Simulations with the VIP model revealed that annual ET and GPP slightly increased from 1981 to 2013 over the North China Plain. The tendencies of both ET and GPP were upward in the spring season, while they were weak and downward in the summer season. A complete factor analysis illustrated that the relative contributions of climatic change, CO2 fertilization, and management to the ET (GPP) trend were 56 (􀀀32) %, 􀀀28 (25) %, and 68 (108) %, respectively. The decline of global radiation resulted from deteriorated aerosol and air pollution was the principal cause of GPP decline in summer, while air warming intensified the water cycle and advanced the plant productivity in the spring season. Generally, agronomic improvements were the principal drivers of crop productivity enhancement.
... Yet research suggested that the MODIS GPP and NPP products could be further improved through employing better driver data, higher-quality satellite observations, and recalibrated model parameters [Kanniah et al., 2009;Medlyn, 2011;Samanta et al., 2011;Sjostrom et al., 2011Sjostrom et al., , 2013Wu et al., 2014], which is especially necessary for regional applications. Yet few studies have validated both GPP and NPP products using localized inputs and calibrated parameters at the regional level [Xin et al., 2015;Yan et al., 2015]. ...
Article
Terrestrial ecosystems have continued to provide the critical service of slowing the atmospheric CO2 growth rate. Terrestrial net primary productivity (NPP) is thought to be a major contributing factor to this trend. Yet our ability to estimate NPP at the regional scale remains limited due to large uncertainties in the response of NPP to multiple interacting climate factors and uncertainties in the driver data sets needed to estimate NPP. In this study, we introduced an improved NPP algorithm that used local driver data sets and parameters in China. We found that bias decreased by 30% for gross primary production (GPP) and 17% for NPP compared with the widely used global GPP and NPP products, respectively. From 2000 to 2012, a pixel-level analysis of our improved NPP for the region of China showed an overall decreasing NPP trend of 4.65TgCa⁻¹. Reductions in NPP were largest for the southern forests of China (-5.38TgCa⁻¹), whereas minor increases in NPP were found for North China (0.65TgCa⁻¹). Surprisingly, reductions in NPP were largely due to decreases in solar radiation (82%), rather than the more commonly expected effects of drought (18%). This was because for southern China, the interannual variability of NPP was more sensitive to solar radiation (R² in 0.29-0.59) relative to precipitation (R²<0.13). These findings update our previous knowledge of carbon uptake responses to climate change in terrestrial ecosystems of China and highlight the importance of shortwave radiation in driving vegetation productivity for the region, especially for tropical forests.
... Croplands are distributed across the entire United States; however, the increase in ΔGSL in the Western region (i.e., Northwest, West, and Southwest) was about 5 days on average which is notably lower than 15 days for Northeastern region (i.e., Upper Midwest, Central, Southeast, and Northeast). This difference in phenology response to urbanization from West to East is due to a combination of change of climate, vegetation types, and human activities (Zhang et al., 2006;Xin et al., 2015). For example, most irrigation activities occur in the Central and Northeastern regions (Ozdogan & Gutman, 2008). ...
Article
The influence of urbanization on vegetation phenology is gaining considerable attention due to its implications for human health, cycling of carbon and other nutrients in Earth system. In this study, we examined the relationship between change in vegetation phenology and urban size, an indicator of urbanization, for the conterminous United States. We studied more than 4500 urban clusters of varying size to determine the impact of urbanization on plant phenology, with the aids of remotely sensed observations since 2003–2012. We found that phenology cycle (changes in vegetation greenness) in urban areas starts earlier (start of season, SOS) and ends later (end of season, EOS), resulting in a longer growing season length (GSL), when compared to the respective surrounding urban areas. The average difference of GSL between urban and rural areas over all vegetation types, considered in this study, is about 9 days. Also, the extended GSL in urban area is consistent among different climate zones in the United States, whereas their magnitudes are varying across regions. We found that a tenfold increase in urban size could result in an earlier SOS of about 1.3 days and a later EOS of around 2.4 days. As a result, the GSL could be extended by approximately 3.6 days with a range of 1.6–6.5 days for 25th ~ 75th quantiles, with a median value of about 2.1 days. For different vegetation types, the phenology response to urbanization, as defined by GSL, ranges from 1 to 4 days. The quantitative relationship between phenology and urbanization is of great use for developing improved models of vegetation phenology dynamics under future urbanization, and for developing change indicators to assess the impacts of urbanization on vegetation phenology.
... Here, the key questions are: (1) how well does the tower represent flux footprint from the targeted vegetation type; and (2) how representative is the flux tower footprint of the broader landscape and regional extents (in terms of NDVI). Furthermore, the spatial scope varies and can be divided into three sub-scales for spatial-scale level: (1) flux site scale, ranging from 0 to 1 km 2 [2,27,28]; (2) remote sensing pixel scale, which ranges from 1 km 2 to 9 km 2 , is most pixel sizes of moderate resolution satellite remote sensing images [29][30][31][32][33]; and (3) land model grid scale, which ranges from 9 km 2 to 324 km 2 , is roughly from 0.01 • × 0.01 • to 0.2 • × 0.2 • , was set to be comparable with the scale of regional or global land surface models [6, 34,35]. Our object is to assess the EC flux data in different aspects and scales, to understand Remote Sens. 2016, 8, 742 3 of 18 the variation of spatial representativeness of EC flux measurements, to further reduce the uncertainties in up-scaling and improve the spatial representativeness. ...
... Here, the key questions are: (1) how well does the tower represent flux footprint from the targeted vegetation type; and (2) how representative is the flux tower footprint of the broader landscape and regional extents (in terms of NDVI). Furthermore, the spatial scope varies and can be divided into three sub-scales for spatial-scale level: (1) flux site scale, ranging from 0 to 1 km 2 [2,27,28]; (2) remote sensing pixel scale, which ranges from 1 km 2 to 9 km 2 , is most pixel sizes of moderate resolution satellite remote sensing images [29][30][31][32][33]; and (3) land model grid scale, which ranges from 9 km 2 to 324 km 2 , is roughly from 0.01 • × 0.01 • to 0.2 • × 0.2 • , was set to be comparable with the scale of regional or global land surface models [6, 34,35]. Our object is to assess the EC flux data in different aspects and scales, to understand Remote Sens. 2016, 8, 742 3 of 18 the variation of spatial representativeness of EC flux measurements, to further reduce the uncertainties in up-scaling and improve the spatial representativeness. ...
Article
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Combining flux tower measurements with remote sensing or land surface models is generally regarded as an efficient method to scale up flux data from site to region. However, due to the heterogeneous nature of the vegetated land surface, the changing flux source areas and the mismatching between ground source areas and remote sensing grids, direct use of in-situ flux measurements can lead to major scaling bias if their spatial representativeness is unknown. Here, we calculate and assess the spatial representativeness of 15 flux sites across northern China in two aspects: first, examine how well a tower represents fluxes from the specific targeted vegetation type, which is called vegetation-type level; and, second, examine how representative is the flux tower footprint of the broader landscape or regional extents, which is called spatial-scale level. We select fraction of target vegetation type (FTVT) and Normalized Difference Vegetation Index (NDVI) as key indicators to calculate the spatial representativeness of 15 EC sites. Then, these sites were ranked into four grades based on FTVT or cluster analysis from high to low in order: (1) homogeneous; (2) representative; (3) acceptable and (4) disturbed measurements. The results indicate that: (1) Footprint climatology for each site was mainly distributed in an irregular shape, had similar spatial pattern as spatial distribution of prevailing wind direction; (2) At vegetation-type level, the number of homogeneous, representative, acceptable and disturbed measurements is 8, 4, 1 and 2, respectively. The average FTVT was 0.83, grass and crop sites had greater representativeness than forest sites; (3) At spatial-scale level, flux sites with zonal vegetation had greater representativeness than non-zonal vegetation sites, and the scales were further divided into three sub-scales: (a) in flux site scale, the average of absolute NDVI bias was 4.34%, the number of the above four grades is 9, 4, 1 and 1, respectively (b) in remote sensing pixel scale, the average of absolute NDVI bias was 8.27%, the number is 7, 2, 2 and 4, respectively; (c) in land model grid scale, the average of absolute NDVI bias was 12.13%, the number is 5, 4, 3 and 3. These results demonstrate the variation of spatial representativeness of flux measurements among different application levels and scales and highlighted the importance of proper interpretation of EC flux measurements. These results also suggest that source area of EC flux should be involved in model validation and/or calibration with EC flux measurements.
... In contrast, soybean as a C 3 plant (meaning that the protein Rubisco is used in its carbon assimilation pathway), was least effective in carbon uptake. Values of IWUE* and LUE were within typical ranges of cropland in the Midwest (Turner et al., 2003;Xin et al., 2015) or elsewhere (Beer et al., 2009). The efficiency of prairie vegetation to assimilate carbon was lower than in corn, but higher than soybean. ...
... The default ε max value for grassland in the BPLUT was less than half of the optimized ε max estimations from field observations in this specific biome (Table 1). Validations in parameters of LUE model in most recent studies revealed that the realistic ε max values were undervalued in the MODIS default GPP algorithms [12,25,27,34] with a few exceptions [26], which emphasized the urgent need to reconcile the optimized ε max for more extensive biomes [17,29,35,38,69]. The global look-up table of ε max in the MOD17A2 GPP algorithm is hard to satisfy all vegetation properties due to various biomes with complex climatic, soil types, and associated stand structures and ages [25,28]. ...
Article
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Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAIG) measurements were used to revise the key inputs, the maximum light use efficiency (εmax) and the fractional photosynthetically active radiation (FPARM) in the MOD17 algorithm. Using two approaches to determine the site-specific εmax value, we suggested that the suitable εmax was about 1.61 g C MJ−1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ−1 for grassland. The FPARM underestimated 22.2% of the actual FPAR (FPARG) simulated from the LAIG during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the εmax and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the εmax and FPARG, are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow.
... For IB1, the model captured the NEE trend intra-annually but overestimated daily net CO 2 uptake in summer. This result might be due to the crop type change from corns to beans in 2007 in this cropland (Xin et al., 2015). But the MLEs of parameters at IB1 were still applicable for comparision with those at other sites in 2006 or 2004. ...
Article
Terrestrial ecosystem models have been extensively used in global change research. When a model calibrated with site-specific parameters is applied to another site, how and why the parameters have to be adjusted again in order to fit data well are pervasive yet underexplored issues. In this exploratory study, we examined how and why model parameters of a Flux-Based Ecosystem Model (FBEM) varied across different sites. Parameters were estimated from data at 12 eddy-covariance towers in the conterminous USA using the conditional inversion method. Results showed that optimized values of these parameters varied across sites. For example, the estimated coefficients in the Leuning model, and , exhibited high cross-site variation, but the ratio of internal to air CO2 concentration ( ) and canopy light extinction coefficient ( ) varied little among these sites. Parameters greatly varied with ecosystem types at adjacent sites where climate conditions were similar. Five parameters (activation energy of carboxylation, ; activation energy of oxygenation, ; ecosystem respiration, ; temperature sensitivity of respiration, ; and stomatal conductance coefficient, ) were highly correlated with mean annual temperature and precipitation across sites, which were distributed in different climate regions of conterminous US. Our results indicate that individual parameters vary to different degrees across sites and parameter variation can be related to different biological factors (e.g., ecosystem types) and environmental conditions (e.g., temperature and precipitation). It is essential to further examine magnitudes of and mechanisms underlying the parameter variation in ecosystem models so as to improve model prediction.
... Traditional approaches to crop mapping and yield forecasting involve routine field visits which are costly and often biased (Castillejo-Gonzalez & Lopez-Granados 2009). Remote sensing offers an unbiased, cost effective, and reliable procedure of mapping crops at a local, regional, and national scale (Xin et al. 2015). The use of remotely sensed optical imagery for the identification and monitoring of crop types has gained popularity in recent years, mainly due to an increase in its availability (Vieira et al. 2012;Simms et al. 2014;Muller et al. 2015;Ozelkan et al. 2015;Wardlow et al. 2015;Zheng et al. 2015). ...
Article
This study evaluates the potential of pan-sharpening multi-temporal Landsat 8 imagery for the differentiation of crops in a Mediterranean climate. Five Landsat 8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired. A statistical pan-sharpening algorithm was used to increase the spatial resolution of the 30 m multispectral bands to 15 m. The pan-sharpened images and original multispectral bands were used to generate two sets of input features at 30 and 15 m resolutions respectively. The two sets of spatial variables were separately used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forests (RF) machine learning classifiers. The analyses were carried out in both the object-based image analysis (OBIA) and pixel-based image analysis (PBIA) paradigms. For the OBIA experiments, three image segmentation scenarios were tested (good, over and under segmentation). The PBIA experiments were carried out at 30 m and 15 m resolutions. The results show that pan-sharpening led to dramatic (∼15%) improvements in classification accuracies in both the PBIA and OBIA approaches. Compared to the other classifiers, SVM consistently produced superior results. When applied to the pan-sharpened imagery SVM produced an overall accuracy of nearly 96% using OBIA, while PBIA’s overall accuracy was 1.63% lower. We conclude that pan-sharpening Landsat 8 imagery is highly beneficial for classifying agricultural fields whether an object- or pixel-based approach is used.
... On the other hand, the fundamental assumptions underlying LUE models -that plant canopies behave like a big single-leaf, and their LUE is independent of the directional nature of solar radiation and vegetation structure-have been widely questioned already by Pury and Farquhar (1997) and continue to be discussed with support of flux data measurements (Gu et al., 2002;Propastin et al., 2012;Zhang et al., 2011). Furthermore, it is unclear how well these empirical relationships hold for spatial and temporal scales beyond those used to derive them, and how they might change under altering environmental conditions (e.g., Xin et al., 2015). The most widely used LUE model is applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product, MOD17 (Running et al., 2004), currently available (Collection 6) globally at 8-day and 500 m resolution . ...
Article
A R T I C L E I N F O Keywords: Gross primary productivity (GPP) Sentinel-2 (S2) Landsat 8 Machine learning (ML) Neural networks (NN) Radiative transfer modeling (RTM) Hybrid approach Soil-canopy-observation of photosynthesis and the energy balance (SCOPE) C3 crops A B S T R A C T Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to oversimplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we bypass the need for an intermediate step to retrieve the set of vegetation bio-physical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r 2 of 0.92 and RMSE of 1.38 gC d −1 m −2 , which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r 2 of 0.82 and RMSE of 1.97 gC d −1 m −2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
... However, a maximum LUE, the results of environmental stresses such as light intensity, temperature, water, and nutrients, is often used as actual LUE and is a constant for a specific crop in estimating vegetation productivity, resulting in large errors [133][134][135]. In practice, LUEmax varies greatly with different crop varieties and is also affected by other factors such as spatial scale [136,137]. ...
... Current RS based estimates of crop productivity are based on the Monteith approach (Monteith 1972), which relates production to absorbed photosynthetic active radiation (APAR) and Light Use Efficiency (LUE) (Bandaru et al. 2013;Prince and Goward 1995). From crop production, crop yields can be estimated through the harvest index (HI), which provides a ratio between grain mass to aboveground biomass (He et al. 2018;Prince et al. 2001;Xin et al. 2015). It should be noted that crop production is usually accumulated over the growing season, and therefore, estimation of the crop productivity requires the use of high-temporal satellite data. ...
Article
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For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.
... Wang et al. (2018) found that the increase in both biomass production and CO 2 fixation with light intensity and CO 2 concentration in C 4 was faster than that in C 3 [24]. Therefore, the characteristics of C 4 plants determine a more efficient use of light and CO 2 than that of C 3 plants [50,52,53]. Figure 4), with the highest Pearson correlation coefficient (r) of 0.87 (R 2 = 0.76) in soybean, followed by summer maize (0.81, R 2 = 0.66), paddy rice (0.79, R 2 = 0.62) and winter wheat (0.73, R 2 = 0.53). ...
Article
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Ecosystem light use efficiency (ELUE) is generally defined as the ratio of gross primarily productivity (GPP) to photosynthetically active radiation (PAR), which is an important ecological indictor used in dry matter prediction. Herein, investigating the dynamics of ELUE and its controlling factors is of great significance for simulating ecosystem photosynthetic production. Using 35 site-years eddy covariance fluxes and meteorological data collected at 11 cropland sites globally, we investigated the dynamics of ELUE and its controlling factors in four agroecosystems with paddy rice, soybean, summer maize and winter wheat. A “U” diurnal pattern of hourly ELUE was found in all the fields, and daily ELUE varied with crop growth. The ELUE for the growing season of summer maize was highest with 0.92 ± 0.06 g C MJ−1, followed by soybean (0.80 ± 0.16 g C MJ−1), paddy rice (0.77 ± 0.24 g C MJ−1) and winter wheat (0.72 ± 0.06 g C MJ−1). Correlation analysis showed that ELUE positively correlated with air temperature (Ta), normalized difference vegetation index (NDVI), evaporative fraction (EF) and canopy conductance (gc, except for paddy rice sites), while it negatively correlated with the vapor water deficit (VPD). Besides, ELUE decreased in the days after a precipitation event during the active growing seasons. The path analysis revealed that the controlling variables considered in this study can account for 73.7, 85.3, 75.3 and 65.5% of the total ELUE variation in the rice, soybean, maize and winter wheat fields, respectively. NDVI is the most confident estimators for ELUE in the four ecosystems. Water availability plays a secondary role controlling ELUE, and the vegetation productivity is more constrained by water availability than Ta in summer maize, soybean and winter wheat. The results can help us better understand the interactive influences of environmental and biophysical factors on ELUE.
... gC/m 2 /d, respectively). This finding is aligned with previous research [50,87]. In crop ecosystems, different crops have different ε max and growth cycles, so a single ε max cannot represent well all types of crops [88,89]. ...
Article
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Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPPMODIS-GPP and GPPEC-LUE, respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPPGR and GPPVI, respectively). Lastly, GPPMODIS-GPP, GPPEC-LUE, GPPGR, and GPPVI were evaluated against the eddy covariance flux data (GPPEC) at the site scale and MODIS GPP products (GPPMOD17) at the regional scale. The results showed that, at the site scale, GPPMODIS-GPP and GPPEC-LUE agreed well with GPPEC for the US-Ton site, with R2 = 0.73 and 0.74, respectively. The performance of GPPGR and GPPVI varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPPMODIS-GPP and GPPEC-LUE agreed well with GPPMOD17 in 2017 and 2018, with R2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.
... MODIS GPP products in the midwestern U.S. croplands were underestimated due to the inaccuracy of ε max estimation. Therefore, the LUE values measured in the field should be used consistently in large-scale GPP modeling [50]. Lin conducted a monthly parameterization of the one-leaf LUE model and the two-leaf LUE model of eighteen typical plant function types. ...
Article
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Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.
... On the other hand, the fundamental assumptions underlying LUE models -that plant canopies behave like a big single-leaf, and their LUE is independent of the directional nature of solar radiation and vegetation structurehave been widely questioned already by Pury and Farquhar (1997) and con-tinue to be discussed with support of flux data measurements (Gu et al., 2002;Zhang et al., 2011;Propastin et al., 2012). Furthermore, it is unclear how well these empirical relationships hold for spatial and temporal scales beyond those used to derive them, and how they might change under altering environmental conditions (e.g., Xin et al., 2015). The most widely used LUE model is applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product, MOD17 (Running et al., 2004), currently available (Collection 6) globally at 8-day and 500 m resolution . ...
Preprint
Full-text available
Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
... Agricultural resources, agricultural production, etc. all have spatial distribution and spatial correlation [4][5][6][7], using 3S integration technology for powerful spatial analysis, rapid information collection and accurate spatial positioning. Effective management and analysis can be conducted to guide agricultural production decisions or farming [8,9]. Since the earliest thermal infrared remote sensing temperature was used in the evapotranspiration calculation and the crop impedance-evapotranspiration model was proposed, the evapotranspiration model has evolved from a simple empirical formula to a dual-source evapotranspiration model with a solid physical basis [10]. ...
Article
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Water resources are not only natural resources for all living things, but also economic resources with important strategic significance. Water shortages have become an important constraint to global and regional food security and ecological security. At the same time, the efficiency of irrigation water use in China is generally low, the construction standards of irrigation districts are low, the supporting methods are incomplete, and the irrigation methods are extensive. The scientific and accurate evaluation of irrigation scale and crop water use efficiency in arid area is an important part of water resources management. Sustainable use and sustainable social development are essential. Among them, remote sensing technology is an important means of monitoring agricultural conditions in recent years. With its macroscopic, periodic and economical access to surface information, it has obvious advantages in monitoring crop planting conditions. At the irrigation area scale, meteorological and vegetation factors have great spatial variability. In addition to ground observations, remote sensing has become the main means of obtaining information on evapotranspiration in irrigation districts. In this paper, environmental satellite remote sensing data is used as an auxiliary data source, and stratified sampling is taken as the core to study the method of water use efficiency of crops. Using remote sensing technology to obtain surface spectral information for soil condition monitoring, which makes the focus of remote sensing monitoring of soil condition monitoring research, and studies the application of remote sensing evapotranspiration and crop yield estimation results in irrigation water use efficiency and crop water use efficiency evaluation in irrigation areas, and Data on specific studies of crops that can adapt to complex planting structures.
... Alternatively, crop productivity can be estimated from satellite data using a semi-empirical approach. This approach integrates remotely sensed biophysical variables and climate variables directly into a satellite-data-based Production Efficiency Model (PEM) (Dong et al., 2017;Liu et al., 2010;Xin et al., 2015;Yuan et al., 2016). In contrast to empirical approaches, the satellite-based PEM is usually developed based on the theory of light use efficiency (LUE) or water use efficiency (WUE) to simulate daily crop productivity, such as gross or net primary productivity (Campos et al., 2018;Liu et al., 2010;Yuan et al., 2016). ...
Article
The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for crop biomass estimation for six crops in Manitoba, Canada. Crop biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple crop growth model. The results showed that the LAI of six different crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry biomass of these six crops was more accurately estimated from the assimilation of LAI derived from both satellites (R² (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m², nRMSE (the normalized RMSE) = 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R² = 0.80, RMSE = 136.7 g/m², nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R² = 0.68, RMSE = 191.0 g/m², nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to crop phenology and biomass partitioning. Both crop growth stage (e.g., the emergence date for crop growth) and leaf dry biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.
... NDVI) for the method. Several studies indicate that NDVI may become insensitive in dense vegetation canopies (Asner et al., 2003;Chen et al. 2006;Gu and Wylie 2015;Xin et al. 2015), thus leading to an underestimation of NDVI in high biomass regions, and further having an influence on the accuracy of the planting area identification. This situation would be more serious when it comes to identify the planting area of corn (Gu et al. 2013). ...
Article
Winter wheat is a staple food crop for most of the world’s population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.
... However, we note here that the MODIS GPP/NPP algorithm treats cropland with the same set of biome-specific parameters. In reality, different crops may have different grain formation mechanisms, and different light use efficiency [50]. Further study is necessary to improve the modeling of crop GPP and NPP for final grain yield estimation. ...
Article
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We evaluated the utility of Terra/MODIS derived crop metrics for yield estimation across the Canadian Prairies. This study was undertaken at the Census Agriculture Region (CAR) and the Rural Municipality (RM) of the province of Saskatchewan, in three prairie agro-climate zones. We compared MODIS-derived vegetation indices, gross primary productivity (GPP) and net primary productivity (NPP) to the known yields for barley, canola, and spring wheat. Multiple linear regressions were used to assess the relationships between the metrics and yield at the CAR and RM levels for the years 2000 to 2016. Models were evaluated using a leave-one-out cross validation (LOOCV) approach. Results showed that vegetation indices at crop peak growing stages were better predictors of yield than GPP or NPP, and EVI2 was better than NDVI. Using seasonal maximum EVI2, CAR-level crop yields can be estimated with a relative root-mean-square-error (RRMSE) of 14-20% and a Nash—Sutcliffe model efficiency coefficient (NSE) of 0.53-0.70, though the exact relationship varies by crop type and agro-climate zone. LOOCV showed the stability of the models across different years, although inter-annual fluctuations of estimation accuracy were observed. Assessments using RM-level yields showed slightly reduced accuracy, with NSE of 0.37-0.66, and RRMSE of 18-28%. The best-performing models were used to map annual crop yields at the Soil Landscapes of Canada (SLC) polygon level. The results indicated that the models could perform well at both spatial scales, and thus, could be used to disaggregate coarse resolution crop yields to finer spatial resolutions using MODIS data.
... Previous studies have reported that the weather conditions can influence GPP estimates made using the light-use efficiency model [62,63]. As CI influences the relationship between SIF and GPP, we added CI into the SIF-based GPP estimation models (both linear and non-linear), which are expressed as ...
Article
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Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. and had an R2 value of 0.66 (2017) and 0.66 (2018) for instantaneous observations made within a period of half an hour and 0.82 (2017) and 0.76 (2018) for daily mean observations. Secondly, it was found that the SIF760–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.
... Usually, LUEmax is treated as a constant for a specific crop biomass, which can lead to large errors in vegetation productivity estimation. In practice, LUEmax varies with different flora even in the same forest, crop, and spatial scale (Morel et al. 2014;Xin et al. 2015). Another important source of uncertainty in MOD17 GPP is problems with the algorithm at water-limited sites. ...
Book
This book contributes to our understanding of linkages between carbon management and local livelihoods by taking stock of the existing evidence and drawing on field experiences in the Hindu Kush Himalayan (HKH) region, an area that provides fresh water to more than 2 billion people and supports the world’s largest population of pastoralists and millions of livestock. This edited volume addresses two main questions: 1. Does carbon management offer livelihood opportunities or present risks, and what are they? 2. Do the attributes of carbon financing alter the nature of livelihood opportunities and risks? Chapters analyze the most pressing deficiencies in understanding carbon storage in both soils and in above ground biomass, and the related social and economic challenges associated with carbon sequestration projects. Chapters deliver insights to both academics from diverse disciplines (natural sciences, social sciences and engineering) and to policy makers.
... Additionally, several authors have identified the need to distinguish LUE max based on crop type. Xin et al. (2015) identified a large variation in GPP LUE for different crops, highlighting the importance of correcting generalised datasets for factors including not only HI and moisture content, but also maximum LUE. Bastiaanssen and Ali (2003) compiled LUE max values from literature, which varied significantly between crops, particularly between C3 and C4 crops. ...
Article
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The scarcity of water and the growing global food demand has fevered the debate on how to increase agriculturalproduction without further depleting water resources. Crop water productivity (CWP) is a performance indicatorto monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and itscomponents, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ETa), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users anddevelopers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First,the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy,is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracybenchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7% to 67%, depending on method and scale. The error of CWP from remote sensingranges from 7% to 22%, based on the highest reported performing remote sensing products. However, whenconsidering the entire breadth of reported crop yield and ETaaccuracy, the achievable errors propagate to CWPranges of 74% to 108%. Although the remote sensing CWP appears comparable to the accuracy of in-situmethods in many cases, users should determine whether it is suitable for their specific application of CWP.
... Usually, LUEmax is treated as a constant for a specific crop biomass, which can lead to large errors in vegetation productivity estimation. In practice, LUEmax varies with different flora even in the same forest, crop, and spatial scale (Morel et al. 2014;Xin et al. 2015). Another important source of uncertainty in MOD17 GPP is problems with the algorithm at water-limited sites. ...
Chapter
The Hindu-Kush Himalaya region’s land cover is comprised of 54% rangeland, 25% agricultural land, 14% forest, 5% permanent snow and 1% water bodies. The Himalayans contain some of the largest water reservoirs, which are critical for HKH countries. Amidst these, wetlands have remained important to ecosystem services and the overall water cycle of the basins. Beside their cultural and provisioning amenities, wetlands are important carbon reservoirs, accounting for 20–30% of the global carbon pool. They act as a sink for atmospheric carbon, thus can influence GHG emissions, especially CH4, and, thus, should be managed properly. However, substantial data gaps remain in quantifying carbon sequestration and the potential of CH4 emission. Furthermore, studies on CH4 fluxes in high-altitude wetlands, particularly in remote areas, remain inconclusive. Hence, more research is required to understand the role of wetlands in term of GHG emissions and carbon sequestration.
... Usually, LUEmax is treated as a constant for a specific crop biomass, which can lead to large errors in vegetation productivity estimation. In practice, LUEmax varies with different flora even in the same forest, crop, and spatial scale (Morel et al. 2014;Xin et al. 2015). Another important source of uncertainty in MOD17 GPP is problems with the algorithm at water-limited sites. ...
Chapter
As the key part of HKH, the Qinghai-Tibetan plateau supports the largest population of pastoralists (10 million) in the world. Livestock production on the plateau produces large quantities of dung, but approximately 80% is collected for energy purposes such as cooking and heating needs, which is a link with carbon cycling being a source of carbon to soil and livelihood activity i.e by providing energy and imrpoving grassland productivity. However, inefficient combustion of the dung results in indoor as well as environmental pollution with adverse impact on human health. Heating biomass in oxygen-limited conditions transforms the biomass into bio-oil, syn-gas and a carbon-enriched material known as biochar. Biochar can be used to store carbon in soil and to improve soil quality. This chapter explores the importance of biochar for grasslands restoration and the potential of dung biochar for carbon capture and for increasing grassland productivity. In addition, future biochar research directions to restore grasslands and to improve the livelihood of the pastoralists are discussed.
... One main reason is that these models fail to consider the growth modules for specific crops, such as C4 crops. For example, it has been widely verified that MODIS standard GPP product (MOD17) assigns a universal  0 (1.04 gC/MJ) for all crop species with different photosynthetic pathway (C3 and C4), and resulted to large underestimate of GPP for C4 crops (Zhang et al., 2008;Chen et al., 2014;Xin et al., 2015). An intercomparison of 26 terrestrial ecosystem models in part by the North American Carbon Project (NACP) found that all models performed poorly when estimating the GPP for crop and grassland . ...
Article
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In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as 'flash drought', developed in May with a rapid intensification afterwards, and peaked in mid-July. ~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to the other years of 2010-2014; 2) the drought had a more severe impact on agroecosystems (crop and grassland) than on forests; 3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence (SIF) and gross primary production (GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest (1.76 Pg C/yr) during 2010-2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.
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It is suspected that corn and soybean production in the Maumee Watershed has contributed to nutrient loading into Lake Erie, therefore affecting the frequency and duration of toxic algae (Dolan 1993), (Michalak, et al. 2012). Accurate crop type estimation is important in order to determine the potential impact on the lake and assess methods to reduce excess nutrient loading. Increasingly, the National Agricultural Statistics Survey (NASS) Crop Data Layer (CDL) is being used as a primary input for agricultural research and crop estimation modeling therefore assessing the accuracy of the CDL is imperative. This study aims to validate the CDL, assess accuracy differences on multiple spatial scales and to examine the efficiencies of using the CDL for future research in the region. Results of CDL validation using in situ field observations from 2011 and 2012 indicate an overall accuracy of at 94% and 92% respectively and khat accuracy of 90% (2011) and 86% (2012). Crop specific accuracy for corn, soy and wheat also resulted in considerably high user accuracy values, with slight differences between years. Accuracy measures vary by region and by year however in each circumstance analyzed, the differences were not significant. Of these measureable difference, it was shown that the 2012 comparison contained a higher degree of difference and this may be attributed to drought in the region for this year. It is concluded that NASS’s CDL is an effective and efficient product for agricultural research.
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While canopy temperature has been extensively utilized for field-level crop health assessment, the application of satellite-based land surface temperature (LST) images for corn yield modeling has been limited. Furthermore, long term yield projections in the context of climate change have primarily employed air temperature (Tair) and precipitation, which may inadequately reflect crop stress. This study assessed potential benefits of satellite-derived LST for predicting annual corn yield across the US Corn Belt from 2010 to 2016. A novel killing degree day metric (LST KDD) was computed with daily LST images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and compared to the typically used Tair-based metric (Tair KDD). Our findings provide strong evidence that LST KDD is capable of predicting annual corn yield with less error than Tair KDD (R²/RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). Even while adjusting for seasonal temperature and precipitation parameters, the R² and RMSE of the LST model were approximately 9% higher and 2.0 Bu/Acre lower than the Tair model, respectively. The superior performance of LST can be attributed to its ability to better incorporate evaporative cooling and water stress. We conclude that MODIS LST can improve yield forecasts several months prior to harvest, especially during extremely warm and dry growing seasons. Furthermore, the better performance of LST models over Tair and precipitation models suggest that subsequent long term yield projections should consider additional factors indicative of water stress.
Chapter
Carbon dynamics, a key index to evaluate ecosystems, are very complex in the Hindu Kush Himalayan (HKH) region due to the topography, diverse regional climate, and different land cover types. MODIS GPP was used to evaluate carbon sequestration in the HKH region from 2001 to 2016. In general, the spatio-temporal variation of the average daily gross primary productivity (GPP) was very heterogeneous due to the changing terrain, diverse regional climate, and different land cover types in the region. Many factors should be considered for GPP measurements, including satellite, airplane, ground-based and modelling data. We concluded that it is necessary to determine the driving forces of GPP in the future in order to establish scientific policies and development programs for the HKH region.
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Modeling vegetation photosynthesis is essential for understanding carbon exchanges between terrestrial ecosystems and the atmosphere. The radiative transfer process within plant canopies is one of the key drivers that regulate canopy photosynthesis. Most vegetation cover consists of discrete plant crowns, of which the physical observation departs from the underlying assumption of a homogenous and uniform medium in classic radiative transfer theory. Here we advance the Geometric Optical Radiative Transfer (GORT) model to simulate photosynthesis activities for discontinuous plant canopies. We separate radiation absorption into two components that are absorbed by sunlit and shaded leaves, and derive analytical solutions by integrating over the canopy layer. To model leaf-level and canopy-level photosynthesis, leaf light absorption is then linked to the biochemical process of gas diffusion through leaf stomata. The canopy gap probability derived from GORT differs from classic radiative transfer theory, especially when the leaf area index is high, due to leaf clumping effects. Tree characteristics such as tree density, crown shape, and canopy length affect leaf clumping and regulate radiation interception. Modeled gross primary production (GPP) for two deciduous forest stands could explain more than 80% of the variance of flux tower measurements at both near hourly and daily time scales. We also demonstrate that the ambient CO2 concentration influences daytime vegetation photosynthesis, which needs to be considered in state-of-the-art biogeochemical models. The proposed model is complementary to classic radiative transfer theory and shows promise in modeling the radiative transfer process and photosynthetic activities over discontinuous forest canopies.
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Vegetation spring onset regulates canopy photosynthetic activities and subsequent ecosystem processes, thereby influencing the complex interactions between the biosphere and the atmosphere. Robust models that predict the timing of vegetation spring onsets are required to account for the ecosystem response and adaption to climate variability. Here, a risk-benefit model is proposed to account for the fundamental tradeoff underlying plant leafing strategies: earlier timing of leaf-out events leads to greater vegetative carbon gain but higher risks associated with hazard damages. The proposed model named the Growing Production-Day (GPD) model uses the cumulative productivity of a hypothetical reference vegetation cover as the overall benefit and predicts the events of vegetation spring onset when a certain threshold that vegetation invests to mitigate potential hazard damages is reached. The daily canopy photosynthesis of the hypothetical reference vegetation cover is simulated by a two-leaf canopy model, which considers sunlit and shaded leaves within a canopy separately and accounts for the biogeochemical processes of canopy radiative transfer, leaf photosynthesis, leaf conductance, leaf transpiration, and soil evaporation. When validated against measurements from available flux tower sites of deciduous broadleaf forests, the two-leaf canopy model accurately simulated daily canopy photosynthesis and evapotranspiration rates, indicated by significant correlations (R2 = 0.787 and 0.745 for gross primary production and latent heat, respectively) and low root-mean-square errors (RMSE = 2.25 gC m2 day−1 for gross primary production and 21.53 W m−2 for latent heat, respectively) between the observed and modeled values. Based on the two-leaf canopy model, the GPD model predicted the dates of spring onsets accurately for three studied biomes (RMSE = 9.10, 5.54, and 12.76 days for evergreen needleleaf forests, deciduous broadleaf forests, and grasslands, respectively) as derived from the flux tower data. In addition, the GPD model could simulate the long-term interannual variation of species-level leaf onset dates as obtained from in-situ observations, and capture the spatiotemporal patterns of multi-decadal variation of vegetation spring onsets across the Northern Hemisphere as derived from satellite data. Although the GPD model requires further refinements, it shows promises with respect to simulating vegetation spring onset in response to multi-decadal climate variability.
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The optimum growth temperature (Topt) and maximum light use efficiency (εmax) of terrestrial vegetation are closely related to plant photosynthesis in current and future Earth environments, yet little is known about their spatial distributions at the global scale. This study derived global maps of Topt and εmax separately, under the light use efficiency (LUE) model framework by utilizing FLUXNET measurements and satellite‐observed solar/sun‐induced chlorophyll fluorescence (SIF), as well as multiple regression and neural network regression based on environmental and biological factors. Topt is found to be positively correlated with annual mean temperature (T), except in cold areas with T < 9°C, where Topt stays within the range of 10°C–15°C. Topt is equal to T in tropical areas with T ≥ 25°C, but is obviously higher than T in other regions. εmax is high in regions with a large amount of diffuse radiation and increases significantly with water stress. The maps of Topt and εmax improved the global gross primary production (GPP) estimation (R² = 0.83, RMSE = 1.38 g C m⁻² d⁻¹ against flux observations). The average annual GPP was 126 ± 1.5 PgC yr⁻¹, with a trend of 0.6 ± 0.1 PgC yr⁻² during 2001–2016, faster than most previous estimates. Our study suggests that the positive anthropogenic impacts on GPP were underestimated in existing products, including cropland expansion in southern Brazil and afforestation/forest protection efforts in China and western Europe. This study also provides a potential method for unified GPP modeling under the LUE framework.
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Maize is one of the most important crops cultivated on the global scale. Accurate estimation of maize Gross Primary Production (GPP) can provide valuable information for regional and global carbon budget studies. From site level to regional/global scales, GPP estimation depends on remote sensing or eddy covariance flux data. In this research, the 8-day composite GPP of maize was estimated by Moderate Resolution Imaging Spectroradiometer (MODIS) and flux tower data at eight study sites using a Regional Production Efficiency Model (REG-PEM). The performance of the model was assessed by analyzing the linearly regression of GPP estimated from the REG-PEM model (GPPEST) with the GPP predicted from the eddy covariance data (GPPEC). The coefficient of determination, root mean squared error and mean absolute error of the regression model were calculated. The uncertainties of the model are also discussed in this research. The seasonal dynamics (phases and magnitudes) of the GPPEST reasonably agreed with those of GPPEC, indicating the potential of the satellite-driven REG-PEM model for up-scaling the GPP in maize croplands. Furthermore, the maize GPP estimated by this model is more accurate than the MODIS GPP products (MOD17A2). In particular, MOD17A2 significantly underestimated the GPP of maize croplands. The uncertainties in the REG-PEM model are mostly contributed by the maximum light use efficiency and the fraction of photosynthetically active radiation.
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Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50–75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R-2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R-2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.
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Gross primary productivity (GPP) estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are converted to wheat yield and compared with observed yield for counties, climate districts and entire states for the 2001 and 2002 growing seasons in Montana and North Dakota. Analyses revealed that progressive levels of spatial aggregation generally improved the relations between estimated and observed wheat yield. However, only state level yield estimates were sufficiently accurate (? 5% deviation from observed yield). The statewide yield results were encouraging because they were derived without the use of retrospective empirical analyses, which constitutes a new opportunity for timely wheat yield estimates for large regions. Additionally, this study identifies six practical limits to estimating wheat yield using MODIS GPP. As a result we describe three suggestions for improving wheat yield estimates for scientists willing to re-compute MODIS-derived GPP estimates using regionally specific inputs.
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Probably the single most fundamental measure of “global change” of highest practical interest to humankind is the change in terrestrial biological productivity. Biological productivity is the source of all the food, fiber, and fuel by which humans survive, and so defines most fundamentally the habitability of Earth. The spatial variability of net primary productivity (NPP) over the globe is enormous, from about 1000 g Cm-2 for evergreen tropical rain forests to less than 30 g Cm-2 for deserts (Scurlock et al. 1999). With increased atmospheric carbon dioxide (CO2) and global climate change, NPP over large areas may be changing (Myneni et al. 1997a, VEMAP 1995, Melillo et al. 1993). Understanding regional variability in carbon cycle processes requires a more spatially detailed analysis of global land surface processes. Since December 1999, the U.S. National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) produces a regular global estimate of (gross primary productivity, GPP) and annual NPP of the entire terrestrial earth surface at 1-km spatial resolution, 150 million cells, each having GPP and NPP computed individually.
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Accurate measurement of crop growth and radiation use efficiency (RUE) under optimal growth conditions is required to predict plant dry matter accumulation and grain yield near the genetic growth potential. Research was conducted to quantify the biomass and leaf area index (LAI) accumulation, extinction coefficient, and RUE of maize (Zea mays L.) under conditions of optimal growth. Maize was grown in two environments over five growing seasons (1998–2002). Total aboveground biomass at maturity ranged from 2257 g m-2 in 1998 to 2916 g m-2 in 2001; values that are considerably greater than the biomass achieved in most previous studies on RUE in maize. Peak LAI ranged from 4.8 to 7.8. Maize extinction coefficients during vegetative growth (k) were within the range of recently published values (0.49 ± 0.03), with no clear pattern of differences in k among years. Seasonal changes in interception of photosynthetically active radiation (PAR) were similar across all but one year. Estimates of RUE were obtained using the short-interval crop growth rate method and the cumulative biomass and absorbed PAR (APAR) method. Values of RUE obtained using the two methods were 3.74 (±0.20) g MJ-1 APAR and 3.84 (±0.08) g MJ-1 APAR, respectively, and did not vary among years. This compares to a published mean RUE for maize of 3.3 g MJ-1 of intercepted PAR (Mitchell et al., 1998). Moreover, RUE did not decline during grain filling. Differences in biomass accumulation among years were attributed in part to differences in observed radiation interception, which varied primarily due to differences in LAI. Maize simulation models that rely on RUE for biomass accumulation should use an RUE of 3.8 g MJ-1 APAR for predicting optimum yields without growth limitations.
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Kumar and Monteith's [1981] model for the remote sensing of crop growth has been used to estimate continental net primary productivity (NPP) as well as its seasonal and spatial variations. The model assumes a decomposition of NPP into independent parameters such as incident solar radiation (S0), radiation absorption efficiency by canopies (f), and conversion efficiency of absorbed radiation into organic dry matter (e). The precision on some of the input parameters has been improved, compared to previous uses of this model at a global scale: remote sensing data used to derive f have been calibrated, corrected of some atmospheric effects, and filtered; e has been considered as biome-dependent and derived from literature data. The resulting global NPP (approximatively 60 Gtc per year) is within the range of values given in the literature. However, mean NPP estimates per biome do not agree with the literature (in particular, the estimation for tropical rain forests NPP is much lower and for cultivations much higher than field estimates), which results in zonal and seasonal variations of continental NPP giving more weight to the temperate northern hemisphere than to the equatorial zone.
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Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes daily gross primary production (GPP) and annual net primary production (NPP) at 1-kilometer (km) resolution over 109,782,756 km2 of vegetated land surface. We summarize the history of global NPP science, as well as the derivation of this calculation, and current data production activity. The first data on NPP from the EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectroradiometer) sensor are presented with different types of validation. We offer examples of how this new type of data set can serve ecological science, land management, and environmental policy. To enhance the use of these data by nonspecialists, we are now producing monthly anomaly maps for GPP and annual NPP that compare the current value with an 18-year average value for each pixel, clearly identifying regions where vegetation growth is higher or lower than normal.
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The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.
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This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (Reco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems.
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This paper presents a modeling approach aimed at seasonal resolution of global climatic and edaphic controls on patterns of terrestrial ecosystem production and soil microbial respiration. We use satellite imagery (Advanced Very High Resolution Radiometer and International Satellite Cloud Climatology Project solar radiation), along with historical climate (monthly temperature and precipitation) and soil attributes (texture, C and N contents) from global (1°) data sets as model inputs. The Carnegie-Ames-Stanford approach (CASA) Biosphere model runs on a monthly time interval to simulate seasonal patterns in net plant carbon fixation, biomass and nutrient allocation, litterfall, soil nitrogen mineralization, and microbial CO2 production. The model estimate of global terrestrial net primary production is 48 Pg C yr-1 with a maximum light use efficiency of 0.39 g C MJ-1 PAR. Over 70% of terrestrial net production takes place between 30°N and 30°S latitude. Seasonal variations in atmospheric CO2 concentrations from three stations in the Geophysical Monitoring for Climate Change Flask Sampling Network correlate significantly with estimated net ecosystem production values by latitude. -from Authors
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Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.
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Croplands cover 12% of the ice-free land surface and play an important role in the global carbon cycle. Light use efficiency (LUE) models have often been employed to estimate the exchange of CO2 between croplands and the atmosphere. A key parameter in these models is the maximum light use efficiency (ε*), but estimates of ε* vary by at least a factor 2. Here we used 12 agricultural eddy-flux measurement sites in North America and Europe to constrain LUE models in general and ε* in particular. We found that LUE models could explain on average about 70% of the variability in net ecosystem exchange (NEE) when we increased the ε* from 0.5 to 0.65-2.0g C per MJ Photosynthetic Active Radiation (PAR). Our results imply that croplands are more important in the global carbon budget than often thought. In addition, inverse modeling approaches that utilize LUE model outputs as a-priori input may have to be revisited in areas where croplands are an important contributor to regional carbon fluxes.
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Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.
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An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities.
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Application and validation of a modified production efficiency model (PEM) appropriate for the regional and global scales is presented. The model calculates not just the conversion efficiency of absorbed photosynthetically active radiation (APAR) but also the component carbon fluxes that ultimately determine net and gross primary production. This approach, driven with remotely sensed observations, moves beyond simple correlative or associative models to a more mechanistic basis and avoids the need for a full suite of ecophysiological process algorithms that require explicit (e.g. species-specific) parameterization. We show that surface variables recovered from the satellite observations, including net primary production, are in good agreement with field measurements and independent model simulations in a number of ecosystems. These results illustrate the utility of PEMs for terrestrial primary production modeling over large areas and suggest that some complex ecophysiological models may be functionally simpler than their structure suggests.
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Discrepancies in closure of the surface energy balance is often an issue for many land surface types. The role of canopy storage terms from canopy water content and photosynthesis is usually neglected in the surface energy balance of crops. Data from a research flux tower in central Illinois were used to evaluate these storage terms and their impact on the closure of the surface energy balance. When considered separately, the storage terms are generally a small fraction (<5%) of the net radiation. However, the combination of soil and canopy heat storage and the stored energy in the carbohydrate bonds from photosynthesis are shown to comprise roughly 15% of the total net radiation for maize and 7% for soybean during the morning hours from 06:00 to 12:00 h when the canopy is fully developed. When all of the storage terms were considered, the slopes of the 1:1 line between net radiation and the partitioned fluxes (latent, sensible, ground, and storage) increased by 10% and the scatter about the 1:1 line decreased for both maize and soybean with the r2 increasing by 0.05.