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The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively). [ABSTRACT FROM AUTHOR]

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively).

A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.

California is expected to experience great spatial/temporal variations evaporation. These variations arise from strong north‐south, east‐west gradients in rainfall and vegetation, strong interannual variability in rainfall (±30%) and strong seasonal variability in the supply and demand for moisture. We used the Breathing Earth System Simulator to evaluate the rates and sums of evaporation across California, over the 2001–2017 period. Breathing Earth System Simulator is a bottom‐up, biophysical model that couples subroutines that calculate the surface energy balance, photosynthesis, and stomatal conductance. The model is forced with high‐resolution remote sensing data (1 km).The questions we address are as follows: How much water is evaporated across the natural and managed ecosystems of California? How much does evaporation vary during the booms and busts in annual rainfall? and Is evaporation increasing with time due to a warming climate? Mean annual evaporation, averaged over the 2001–2017 period, was relatively steady (393 ± 21 mm/year) given the high interannual variation in precipitation (519 ± 140 mm/year). No significant trend in evaporation at the statewide level was detected over this time period, despite a background of a warming climate. Irrigated agricultural crops and orchards, at 1‐km scale, use less water than inferred estimates for individual fields. This leaves the potential for sharing water, a scarce resource, more equitably among competing stakeholders, for example, farms, fish, people, and ecosystems. Plain Language Summary: Many stakeholders are contending for the limited water budget that is available to California, the world's fifth largest economy. Yet the amount of water used by natural and managed ecosystems across the state is not well known. We produced a new, process‐oriented estimate of statewide water use by natural and managed ecosystems using a biophysical model forced with satellite remote sensing. Despite the booms and busts in rainfall over the 2001 to 2017 period, we find that statewide water use is conservative, compared to the annual variability in rainfall. Nor do we detect that statewide evaporation is increasing as the climate has warmed over this period. We find that crops use less water than conventional wisdom because a subset of fields across a 1‐km pixel are fallow and are at peak leaf area and maximum evaporation potential for a relatively short period. Forests, on the other hand, use more water than conventional wisdom because they have a long growing season and absorb more energy than crops. Our intent is to provide water managers with new information on water use to better share water among the various stakeholders, for example, agricultural, cities, fish, ground water reservoirs, and water quality. Key Points: We produced new granular information (1‐km resolution) on evaporation across California using the biophysical model, Breathing Earth System SimulatorAnnual variations in evaporation were highly damped (393 mm/year +/− 5%) relative to the booms and busts in rainfall between 2001 and 2017 (+/− 28%)Statewide evaporation is not increasing with global warming, over 17 years of inspection, because feedbacks dampen the response to a warming climate [ABSTRACT FROM AUTHOR]

Abstract Background To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. Results Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R2 of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R2 increases to 0.82. Conclusions The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

Daily evapotranspiration (ET) is modeled globally for the period 2000-2013 based on the Penman-Monteith equation with radiation and vapor pressures derived using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites. The ET for a given land area is based on four surface conditions: wet/dry and vegetated/non-vegetated. For each, the ET resistance terms are based on land cover, leaf area index (LAI) and literature values. The vegetated/non-vegetated fractions of the land surface are estimated using land cover, LAI, a simplified version of the Beer-Lambert law for describing light transition through vegetation and newly derived light extension coefficients for each MODIS land cover type. The wet/dry fractions of the land surface are nonlinear functions of LST derived humidity calibrated using in-situ ET measurements. Results are compared to in-situ measurements (average of the root mean squared errors and mean absolute errors for 39 sites are 0.81 mm day-1 and 0.59 mm day-1, respectively) and the MODIS ET product, MOD16, (mean bias during 2001-2013 is -0.2 mm day-1). Although the mean global difference between MOD16 and ET estimates is only 0.2 mm day-1, local temperature derived vapor pressures are the likely contributor to differences, especially in energy and water limited regions. The intended application for the presented model is simulating ET based on long-term climate forecasts (e.g., using only minimum, maximum and mean daily or monthly temperatures). [ABSTRACT FROM AUTHOR]

The availability of multiangular information from the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument provides an excellent opportunity for the characterization of surface anisotropy, which can be used for improving surface albedo estimation. However, the MISR data have been reported with large uncertainties and data gaps due to inaccurate aerosol estimation and/or cloud masking limiting its otherwise broader applications. To mitigate these issues, two approaches were proposed to estimate land surface albedo directly from surface reflectance (LSA_sfc) and Top-of-Atmosphere reflectance (LSA_toa), respectively. As a further development of the traditional albedo algorithms, this is the first attempt to simultaneously utilize multispectral and multiangular information in surface albedo estimation without any prior constraining information. Validations at AmeriFlux sites show that the proposed algorithms can achieve accuracies similar to that of the MISR product with respective bias and RMSE of 0.004 and 0.032 for LSA_sfc and 0.005 and 0.032 for LSA_toa algorithms. We found that the LSA_toa algorithm can significantly reduce data gaps and provide accurate surface albedo retrievals with two to three times more valid data than the current MISR product. In addition, these approaches can be easily applied to other optical sensors to produce accurate and gap-free clear-sky surface albedo estimations. The results of this paper also highlight the importance of having two to three simultaneous observations with sufficient angular sampling, which can improve albedo accuracy and reduce data gaps. [ABSTRACT FROM PUBLISHER]

Background: To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. Results: Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R increases to 0.82. Conclusions: The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces. [ABSTRACT FROM AUTHOR]

Daily evapotranspiration (ET) is modeled globally for the period 2000–2013 based on the Penman–Monteith equation with radiation and vapor pressures derived using remotely sensed Land Surface Temperature (LST) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites. The ET for a given land area is based on four surface conditions: wet/dry and vegetated/non-vegetated. For each, the ET resistance terms are based on land cover, leaf area index (LAI) and literature values. The vegetated/non-vegetated fractions of the land surface are estimated using land cover, LAI, a simplified version of the Beer–Lambert law for describing light transition through vegetation and newly derived light extension coefficients for each MODIS land cover type. The wet/dry fractions of the land surface are nonlinear functions of LST derived humidity calibrated using in-situ ET measurements. Results are compared to in-situ measurements (average of the root mean squared errors and mean absolute errors for 39 sites are 0.81 mm day−1 and 0.59 mm day−1, respectively) and the MODIS ET product, MOD16, (mean bias during 2001–2013 is −0.2 mm day−1). Although the mean global difference between MOD16 and ET estimates is only 0.2 mm day−1, local temperature derived vapor pressures are the likely contributor to differences, especially in energy and water limited regions. The intended application for the presented model is simulating ET based on long-term climate forecasts (e.g., using only minimum, maximum and mean daily or monthly temperatures).

Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature ( T NARR ) and downward shortwave radiation ( R NARR ) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPP VPM ) at seven AmeriFlux crop sites, and investigated the uncertainties in GPP VPM from climate inputs as compared with eddy covariance-based GPP (GPP EC ). Results showed that T NARR agreed well with in-situ measurements; R NARR , however, was positively biased. An empirical linear correction was applied to R NARR , and significantly reduced the relative error of R NARR by ∼25% for crop site-years. Overall, GPP VPM calculated from the in-situ (GPP VPM(EC) ), original (GPP VPM(NARR) ) and adjusted NARR (GPP VPM(adjNARR) ) climate data tracked the seasonality of GPP EC well, albeit with different degrees of biases. GPP VPM(EC) showed a good match with GPP EC for maize ( Zea mays L.), but was slightly underestimated for soybean ( Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPP VPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean) . GPP VPM(adjNARR) showed a good agreement with GPP VPM(EC) for both crops due to the reduction in the bias of R NARR . The results imply that the bias of R NARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales. [ABSTRACT FROM AUTHOR]

Abstract: There are currently numerous data sources available for estimating the timing of recurrent plant phenology transitions. We compared measurements from several phenology data sources to understand the relationship between phenology metrics derived from these data sources and the timing of seasonal transitions in net ecosystem exchange (NEE). We identified the timing of start, peak, end and the duration of the carbon uptake season, as well as the timing of the transitions from sink to source and source to sink using 11 years of NEE data from the University of Michigan Biological Station (UMBS). Using fitted logistic functions we identified proxy metrics for phenological transitions from the time series of Albedo, fraction of absorbed photosynthetically active radiation (fPAR), Plant Area Index (PAI), and MODIS normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) products of various spatial representations. We found that no single source of phenological data was able to accurately describe annual patterns of flux phenology. However, for each transition in NEE (e.g., start of season, transition to net sink), the metrics from one or more data sources were significantly (p <0.05) correlated with the timing of these recurring events. A marginally significant trend toward a longer NEE carbon uptake period over 11 years was not detected by any of the metrics, primarily because none of the metrics were available for the full duration of the NEE data, and NEE did not show significant and consistent trends during the sub-sets of the time when proxy data were available. The results of our study highlight the relative strengths and weaknesses of each phenology data source for directly estimating seasonal transitions and interannual trends in carbon flux phenology of a deciduous forest. [Copyright &y& Elsevier]

Abstract: The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1km×1km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33PgCyr−1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances. [Copyright &y& Elsevier]

Abstract: A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel. [Copyright &y& Elsevier]

Abstract: Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration''s (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well (r =0.73, p <0.001). We then applied the model to the continental scale and estimated NEE for each 1km×1km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas. [Copyright &y& Elsevier]

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley–Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley–Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation (R n), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature (T max), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r 2 was 0.90 (RMS=16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986–1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally. [Copyright &y& Elsevier]

Abstract: The concept of light-use efficiency (LUE) is the underlying basis for estimating carbon exchange in many ecosystem models, especially those models that utilize remote sensing to constrain estimates of canopy photosynthesis. An understanding of the factors that control the efficiency with which forest canopies harvest available light to fix carbon via photosynthesis is therefore necessary for the development of useful production efficiency models. We present an analysis of observations of daily LUE for 2004 in a northern hardwood stand at the Bartlett Experimental Forest CO2 flux tower, White Mountains, New Hampshire (USA). We used eddy covariance measurements to estimate gross carbon exchange (GCE), and radiometric instruments mounted above and below the canopy to estimate the fraction of incident photosynthetically active radiation absorbed by the canopy (fAPAR). Both GCE and fAPAR show strong seasonal and day-to-day variability that contribute to temporal variation in LUE. During the middle of the growing season, when fAPAR is relatively constant, day-to-day variation in LUE is largely explained (r 2 =0.85) by changes in the ratio of diffuse to total downwelling radiation, but is not strongly correlated with any other measured meteorological variable. We also calculated top-of-canopy NDVI based on measurements of reflected radiation at 400–700 and 305–2800nm. Seasonal variation in this broadband NDVI paralleled that of the 500m MODIS pixel containing the flux tower. The relationship between broadband NDVI and fAPAR is approximately linear during green-up, but non-linear during autumn senescence. This seasonal hysteresis has implications for the use of remote sensing indices (such as NDVI or EVI) in satellite estimation of fAPAR for production efficiency modeling. [Copyright &y& Elsevier]

Abstract: In this paper, we describe the process of assessing tower footprint climatology, spatial variability of site vegetation density based on satellite image analysis, and sensor location bias in scaling up to 1km×1km patch. Three flat sites with different vegetation cover and surface heterogeneity were selected from AmeriFlux tower sites: the oak/grass site and the annual grassland site in a savannah ecosystem in northern California and a slash pine forest site in Florida, USA. The site vegetation density was expressed in terms of normalized difference vegetation index (NDVI) and crown closure (CC) by analyzing the high-resolution IKONOS satellite image. At each site, the spatial structure of vegetation density was characterized using semivariogram and window size analyses. Footprint maps were produced by a simple model based on the analytical solution of the Eulerian advection–diffusion equation. The resulting horizontal arrays of footprint functions were then superimposed with those of NDVI and CC. Annual sensor location biases for the oak/grass and the pine forest sites were <4% for both NDVI and CC, requiring no flux corrections in scaling from tower to landscape of 1km2. Although the annual grassland site displayed much larger location biases (28% for NDVI, 94% for CC), their temporal changes associated with averaging time showed a real potential to develop algorithms aimed at upscaling tower fluxes to the landscape in an effort to provide validation data for MODIS products. [Copyright &y& Elsevier]

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models. [ABSTRACT FROM AUTHOR]

Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration's (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well (r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km × 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas., Organismic and Evolutionary Biology, Earth and Planetary Sciences, Engineering and Applied Sciences