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Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed radiation (ARAD), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated ARAD, accounting for changes in biomass composition. The RUE and SLN exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages and underestimated the maximum RUE observed during pod setting. Comparison between observed and simulated RUE using a process-based crop simulation model revealed similar discrepancies. The decoupling between RUE and SLN during early crop stages suggests that leaf N is above that needed to maximize crop growth but may play a role in storing N that can be used in later reproductive stages to meet the large seed N demand associated with high-yielding crops.

Non-invasive comparative analysis of the spectral composition of energy absorbed by crop species at leaf and plant levels was carried out using the absorption coefficient retrieved from leaf and plant reflectance as an informative metric. In leaves of three species with contrasting leaf structures and photosynthetic pathways (maize, soybean, and rice), the blue, green, and red fractions of leaf absorption coefficients were 48, 20, and 32%, respectively. The fraction of green light in the total budget of light absorbed at the plant level was higher than at the leaf level approaching the size of the red fraction (24% green vs. 25.5% red) and surpassing it inside the canopy. The plant absorption coefficient in the far-red region (700–750 nm) was significant reaching 7–10% of the absorption coefficient in green or red regions. The spectral composition of the absorbed light in the three species was virtually the same. Fractions of light in absorbed PAR remained almost invariant during growing season over a wide range of plant chlorophyll content. Fractions of absorption coefficient in the green, red, and far-red were in accord with published results of quantum yield for CO2 fixation on an absorbed light basis. The role of green and far-red light in photosynthesis was demonstrated in simple experiments in natural conditions. The results show the potential for using leaf and plant absorption coefficients retrieved from reflectance to quantify photosynthesis in each spectral range.

The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [SV the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data; though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).

Tropospheric ozone (O3) is the third most important anthropogenic greenhouse gas. O3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop) but does not have crop-specific O3 damage parameters and applies default C3 grass O3 parameters for soybean that underestimate O3 damage. Physiological parameters for O3 damage in soybean in JULES-crop were calibrated against leaf gas-exchange measurements from the Soybean Free Air Concentration Enrichment (SoyFACE) with O3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height and leaf carbon and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production.

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed photosynthetically active radiation (APAR), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated APAR, accounting for changes in biomass composition. The SLN and RUE exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. The level of N supply influenced SLN more than RUE via changes in leaf N concentration, with small changes in specific leaf weight. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages. A simple approach based on phenological stages may give more realistic estimates of RUE before seed filling, improving crop growth and yield prediction via crop models and remote sensing.HighlightChanges in radiation-use efficiency during soybean vegetative and early reproductive stages are not related to specific leaf nitrogen.

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. In this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O2A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

Recent reviews about soybean nitrogen (N) requirement have speculated about a possible N limitation in high-yield environments (>4.5 Mg ha−1). However, these studies did not provide definitive experimental data to test that hypothesis and results from the literature are conflicting. To fill this knowledge gap, we evaluated N limitation across 13 high-yield soybean environments in Argentina and USA. Each experiment included a ‘zero-N’ treatment, which forced the crop to rely on biological N2 fixation and indigenous soil N, and a ‘full-N’ treatment, which provided an ample fertilizer N supply during the entire crop cycle based on site-specific yield potential. Accumulated N in aboveground dry matter (ADM) measured in a N-omission maize plot grown adjacent to the soybean experiments was used to determine indigenous soil N supply. Soybean seed yield, protein and oil concentration, ADM, harvest index (HI), accumulated N in ADM, N harvest index (NHI), and seed N were measured in both treatments at physiological maturity. A simple conceptual framework relating ADM with accumulated N was used to assess treatment differences in ADM, accumulated N, and N-use efficiency (NUE). To account for treatment differences in seed biomass composition, mass-based ADM was expressed in glucose equivalents (ADMe). Seed yield ranged from 4.6 to 6.7 Mg ha−1 (full-N) and 4 to 5.8 Mg ha−1 (zero-N), with the full-N treatment averaging 12% (0.6 Mg ha−1) and 3% (9 g kg−1) higher seed yield and protein concentration, respectively. The full-N treatment exhibited 18% (70 kg N ha−1) and 14% (1.6 Mg ha−1) greater accumulated N and ADM, respectively, compared with the zero N treatment, without changes in HI and NHI, but slightly lower NUE (29 versus 30 kg ADM kg−1 N in full and zero-N, respectively). However, NUE differences between treatments became indistinguishable when ADM was expressed as ADMe (45 kg glucose kg−1 N). The (full-N minus zero-N) seed yield difference and the indigenous soil N supply were negatively associated. This research documented (i) the existence of N limitation in high-yield soybean, (ii) that seed yield and protein concentration increases were physiologically associated with changes in accumulated N (but not in NUE and/or NHI), and (iii) that the degree of N limitation is modulated by indigenous soil N supply. Findings from this study can serve as a basis to identify environments with the largest N limitation and thus guide N management in soybean.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

The goal of this study is to quantify variability of daily light use efficiency (LUE) based on radiation absorbed by photosynthetically active vegetation (LUEgreen) in C3 and C4 crops. Data contained GPP, fAPAR, total and green leaf area index taken over 16 site years of irrigated and rainfed maize and 8 site years of soybean in 2001–2008 including years with quite strong drought events. LUEgreen in irrigated and rainfed sites were statistically indistinguishable showing low sensitivity to water availability. Seasonal changes of LUEgreen remained remarkably small over a wide range of water supply, leaf area index and weather conditions. The magnitude and composition of incident radiation affected the magnitude of the day-to-day LUEgreen change – increases in incident PAR caused statistically significant decreases of LUEgreen. Convergence of LUEgreen to a narrow range in irrigated and rainfed crops brought important implications for understanding mechanisms of plant response to stress and remote estimation of primary production in crops.

Study Region: The Nebraska Sand Hills consisting of four major land cover types: (1) lakes and wetlands (∼5% for both), (2) subirrigated meadows (∼10%), (3) dry valleys (∼20%), and (4) upland dunes (∼65%). Study Focus: Examination of surface energy and water balances on multiple temporal scales with primary focus on latent heat flux (λE), and evapotranspiration (ET), to gain a better understanding of the annual, seasonal, and diel properties of surface energy partitioning among different Sand Hills ecosystems to improve regional water resource management. New Hydrological Insights for the Region: Based on surface energy and water balance measurements using Bowen ratio energy balance systems at three locations during 2004, we find a strong spatial gradient between sites in latent (λE) and sensible (H) heat flux due to differences in topography, soils, and plant community composition on all timescales. Seasonally, all land covers show the greatest λE in summer. Our results show that subirrigated meadows, dry valleys, and upland dunes allocate roughly 81%, 50%, and 41% of available energy to λE, respectively, during the growing season. The subirrigated meadow was the only cover type where cumulative annual ET surpassed cumulative annual precipitation (i.e. net loss of water to the atmosphere). Therefore, the dry valleys and upland dunes are where net groundwater recharge to the High Plains Aquifer is occurring. Keywords: Nebraska, High plains aquifer, Evapotranspiration, Energy balance, Bowen ratio

Earth system models (ESMs) are essential tools to study the impacts of historical and future climate on regional and global food production, as well as to assess the effectiveness of possible adaptations and their potential feedback to climate. Several current ESMs have the capabilities to simulate crop growth. However, some critical crop growth processes (e.g. flowering and other reproductive processes) and their responses to environmental extremes (e.g. heat stress) are not yet represented in most of these models. In this paper, an improved maize growth model was implemented in the Community Land Model version 4.5 (CLM4.5) by modifying the maize planting scheme, incorporating the phenology scheme adopted from the APSIM model (Agricultural Production Systems sIMulator), adding a new carbon allocation scheme into CLM4.5, and improving the estimation of canopy structure parameters including leaf area index (LAI) and canopy height. Unique features of the new model (CLM-APSIM) include more detailed phenology stages, an explicit implementation of the impacts of various abiotic environmental stresses (including nitrogen, water, temperature and heat stresses) on maize phenology and carbon allocation, as well as an explicit simulation of grain number. Evaluation of results at 7 AmeriFlux sites located in the US Corn Belt show that the CLM-APSIM model performs better than the original CLM4.5 in simulating phenology (LAI and canopy height), surface fluxes including gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LH), and sensible heat (SH), and especially in simulating the biomass partition and maize yield. The CLM-APSIM model corrects a serious deficiency in CLM4.5-related to CLM4.5’s underestimation of aboveground biomass (i.e. overestimation of belowground biomass) and overestimation of Harvest Index, which lead to a reasonable yield estimation with wrong mechanisms. Moreover, 13-year simulation results from 2001 to 2013 at the three Mead sites (US-Ne1, Ne2 and Ne3) show that the CLM-APSIM model can more accurately reproduce maize yield responses to growing season climate (temperature and precipitation) than the original CLM4.5 when benchmarked with the site-based observations and USDA county-level survey statistics. The CLM-APSIM model is thus more suitable than its predecessor models in terms of simulating abiotic environmental stresses on maize yield. This new model provides an improved tool to attribute maize yield change to various processes under historical and future climate, as well as to assess and design effective climate adaptation strategies for sustainable agricultural production.

This paper develops and proposes a simplified operational remote sensing approach to assist crop growth models in reproducing actual processes in the field by relating satellite based remote sensing data and key canopy biophysical parameters. While relationships between spectral vegetation indices (VI) and biomass production have been conducted in the past, we specifically pursue the relationship between crop transpiration and biomass production as described in the FAO-66 Aquacrop manual. The authors point to a possible general relationship between a transpiration coefficient (herein we propose the basal crop coefficient, Kcb, as a proxy) and biomass production. In parallel, many studies have demonstrated the well-established relationship between Kcb and remote sensing based VI. Thus, the relationship between both parameters has a strong basis but must be demonstrated. We analyze the relationship between biomass production and the reflectance based Kcb using field data obtained during 11 years in irrigated and rainfed soybeans and maize in eastern Nebraska. The analysis confirms that the relationship is strong and paves the way for the use of remote sensing data for a quantitative analysis of crop biomass production and yield.

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Soybean ( Glycine max ) production is expanding into cooler and warmer environments. Temperature influence on nodal leaf appearance rate and, especially, nodal primordium formation rate – two critical parameters that determine potential leaf area, light absorption, and crop growth and yield – is poorly understood. This study was designed to determine the influence of temperature on primordium formation and leaf appearance and to investigate how the co-ordination between these two processes is affected by contrasting temperature regimes. The experiments were conducted in field and greenhouse settings using indeterminate cultivars differing in maturity group. Soil and air temperature was measured at 30-min intervals in all experiments, starting at sowing and ending at physiological maturity. Plants were dissected every 4–7 d to determine the number of primordia at the stem apical meristem and to estimate primordium formation rate. The number of nodal leaves at main stem was assessed periodically during the entire growing season to estimate rate of leaf appearance. Primordium formation ended near the beginning of pod setting, while leaf appearance ceased at the beginning of seed filling. The end-season final number of primordia was greater than the final leaf number, revealing a surplus of primordia that did not advance beyond primordial stage. Across experiments, mean temperature during the phases of primordium formation and leaf appearance ranged from 15 to 26 °C. Both primordium formation and leaf appearance were temperature-dependent, but primordium formation was faster than leaf appearance. The plastochron was 36 °Cd, whereas the phyllochron was biphasic, decreasing from 83 °Cd to 58 °Cd during ontogeny. A strong relationship was found between the number of primordia and the number of leaves, which was stable across experiments, temperature treatments, and cultivars. This study has established that both primordium formation and leaf appearance in soybean are influenced by temperature, with a co-ordination between these two processes. The co-ordination model presented here can be used for robust prediction of seasonal nodal leaf dynamics in soybean.

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7 mm d−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Absorption of radiation in the photosynthetically active radiation (PAR) region is significantly influenced by plant biochemistry, structural properties, and photosynthetic pathway. To understand and quantify the effects of these traits on absorbed PAR it is necessary to develop practical and reliable tools that are sensitive to these traits. Using a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation on the relationship between the absorption coefficient in the PAR region (αpar) with canopy characteristics such as the fraction of PAR absorbed by photosynthetically active vegetation (fAPARgreen) and chlorophyll (Chl) content. The reflectance dataset used in the study included simulated data obtained from a canopy reflectance model (PROSAIL) and empirical data on three diverse crop species with different leaf structures, canopy architectures and photosynthetic pathways (rice, maize and soybean) acquired at proximal (i.e., using field spectroradiometers) and remote (i.e., Landsat TM and ETM+) distances. Results show the usefulness of αpar derived from reflectance data for assessing not only the photosynthetic status of vegetation, but also the effects of different functional, structural and biochemical traits on plant performance. Furthermore, these assessments can be made using data acquired by satellite sensor systems such as the Landsat series, which are available since the 1980s, thus facilitating the analysis of the photosynthetic status of terrestrial ecosystems throughout the world with a high temporal depth.

The supply of soil resources is critical for the establishment and long-term competitive success of a plant species. Although there is considerable research on the effects of water supply on crop growth and productivity, there is little published research on the comparative response of crops and weeds to limiting soil water supply. The objective of this research was to determine the growth and transpiration efficiency of corn and velvetleaf at three levels of water supply. One corn or velvetleaf plant was grown in a large pot lined with plastic bags. When seedlings reached 10 cm, bags were sealed around the base of the plant, so the only water loss was from transpiration. Daily transpiration was measured by weighing the pots at the same time each day. The experiment was conducted in the fall of 2007 and in the spring of 2008. Four replicates of each species–water treatment were harvested periodically to determine biomass accumulation and leaf area. The relationship between cumulative aboveground biomass and water transpired was described using a linear function in which the slope defined the transpiration efficiency (TE). Corn TE was greater than velvetleaf TE in all treatments during both trials. In the fall trial, corn TE was 6.3 g kg–1, 47% greater than that of velvetleaf TE. In the spring trial, TEs of both species were lower overall, and corn TE increased with declining water supply. Corn produced more biomass and leaf area than velvetleaf did at all water-supply levels. Velvetleaf partitioned more biomass to roots compared with shoots during early growth than corn did. The ability of corn to generate more leaf area and its investment in a greater proportion of biomass into root growth at all levels of water supply may enable it to more-effectively avoid velvetleaf interference under all levels of soil-water supply.

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE = 5.45 wt %, R2 = 0.68), soil bulk density (RMSE = 0.173 g cm−3, R2 = 0.203), and soil organic carbon (RMSE = 1.47 wt %, R2 = 0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE ∼ 0.035 cm3 cm−3 at a SWC = 0.40 cm3 cm−3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE −2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.

One of the main factors affecting vegetation productivity is absorbed light, which is largely governed by chlorophyll. In this paper, we introduce the concept of chlorophyll efficiency, representing the amount of gross primary production per unit of canopy chlorophyll content (Chl) and incident PAR. We analyzed chlorophyll efficiency in two contrasting crops (soybean and maize). Given that they have different photosynthetic pathways (C3 vs. C4), leaf structures (dicot vs. monocot) and canopy architectures (a heliotrophic leaf angle distribution vs. a spherical leaf angle distribution), they cover a large spectrum of biophysical conditions. Our results show that chlorophyll efficiency in primary productivity is highly variable and responds to various physiological and phenological conditions, and water availability. Since Chl is accessible through non-destructive, remotely sensed techniques, the use of chlorophyll efficiency for modeling and monitoring plant optimization patterns is practical at different scales (e.g., leaf, canopy) and under widely-varying environmental conditions. Through this analysis, we directly related a functional characteristic, gross primary production with a structural characteristic, canopy chlorophyll content. Understanding the efficiency of the structural characteristic is of great interest as it allows explaining functional components of the plant system.

Light absorption in the photosynthetically active (400 – 700 nm) spectral region is necessary for plant CO2 fixation via photosynthesis. Light absorption in excess of that which can be used for pho...

Green leaf area index (LAI) provides insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast and nondestructive estimation of green LAI. A number of methods have been used for the estimation of green LAI; however, the specific spectral bands employed varied widely among the methods and data used. Our objectives were (i) to find informative spectral bands retained in three types of methods, neural network (NN), partial least squares (PLS) regression and vegetation indices (VI), for estimating green LAI in maize (a C4 species) and soybean (a C3 species); (ii) to assess the accuracy of the algorithms estimating green LAI using a minimal number of bands for each crop and generic algorithms for the two crops combined. Hyperspectral reflectance and green LAI of irrigated and rainfed maize and soybean were taken during eight years of observations (altogether 24 field-years) in very different weather conditions. The bands retained in the best NN, PLS and VI methods were in close agreement. The validity of these bands was further confirmed via the uninformative variable elimination PLS technique. The red edge and the NIR bands were selected in all models and were found the most informative. Identifying informative spectral bands across all four techniques provided insight into spectral features of reflectance specific for each species as well as those that are common to species with different leaf structures, canopy architectures and photosynthetic pathways. The analyses allowed development of algorithms for estimating green LAI in soybean and maize with no re-parameterization. These findings lay a strong foundation for the development of generic algorithms which is crucial for remote sensing of vegetation biophysical parameters.

Land surface models (LSMs) are often applied to predict the one-way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground-based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one-way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground-based observations. Results suggest that LSMs do not generally overestimate the strength of one-way surface SM/LH coupling.

This study utilized a leaf color chart (LCC) to characterize the variation in leaf chlorophyll and estimate canopy chlorophyll in maize (Zea mays). The LCC consisted of four levels of greenness and was used to sort maize leaves in 2011 for three fields near Mead, Nebraska, USA. Leaf chlorophyll content for each color chart class was determined using two leaf-level sensors. The variation within each LCC class was reasonable (CV < 56%). The darkest color class predominated and indicated adequate fertilization rates using a Minolta SPAD-502 meter. Canopy chlorophyll content was estimated using destructively measured leaf area index (LAI) and the LCC. This approach was verified with a method utilizing canopy reflectance collected by both satellite imagery and a four-band radiometer. The error between the two methods was reasonable (RMSE = 0.55–0.88 g m−2; CV = 25.6–50.4%), indicating that both leaf and canopy chlorophyll can be estimated cheaply without a wet lab or field-based sensors.

Vegetation indices (VIs) have been used previously for estimating green leaf area index (green LAI). However, it has not been verified how characteristics of the relationships between these indices and green LAI (i.e., slope, intercept, standard error) vary for different crops and whether one universal algorithm may be applied for accurate estimation of green LAI. By analyzing the data from four different crops (maize, soybean, wheat, and potato) this study aimed at: (1) determining if the previously used VIs for estimating green LAI in maize and soybean may be applicable for potato and wheat and vice versa; and (2) finding a robust algorithm for green LAI estimation that does not require re-parameterization for each crop. Spectral measurements of wheat and potato were obtained in Israel from 2004 to 2007 and of maize and soybean in the USA from 2001 to 2008, and various VIs calculated using measured reflectance were compared with green LAI measured in the field. For all four crops, ten different VIs were examined. Similarities in relationships between VIs and green LAI were found. Among the examined VIs, two variants of the chlorophyll index and wide dynamic range vegetation index with the green and red edge bands were the most accurate in estimating green LAI in all four crops. Hyperspectral reflectance data were used to determine optimal diagnostic bands for estimating green LAI in four crops using a universal algorithm. The green (530–570 nm) and red edge (700–725 nm) regions were identified for both the wide dynamic range vegetation index and chlorophyll index as having the lowest errors estimating green LAI. Since the Landsat 8 – OLI has a green spectral band and the forthcoming Sentinel-2, Sentinel-3 and VENμS have both green and red edge bands, it is expected that these VIs can be used to monitor green LAI in multiple crops using a single algorithm by means of near future satellite missions.

Annual variation of U.S. corn production is an important matter of world concern. To assure immediate response to large-scale harvest failure in crop exporting regions, as was the case during the severe U.S. drought in 2012, and to enhance global food security, a practical crop growth monitoring system based on satellite data is required. This study developed a practical method for near real-time prediction of U.S. corn yields using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Wide Dynamic Range Vegetation Index (WDRVI) taken 7 days before the corn silking stage. We incorporated two algorithms into the MODIS-based corn yield prediction method; namely, (1) a MODIS-based crop classification algorithm in consideration of differences in emergence dates between corn and soybean, and (2) a simple bias correction algorithm for correcting region-dependent yield prediction errors. The method is able to predict the annual variation of national and state level corn grain yields with high accuracy in early August and detect corn yield reductions and poor-harvest regions due to drought damage, as in 2002 and 2012, on a near real-time basis in advance of those provided by the U.S. Department of Agriculture's National Agricultural Statistics Service. The method also predicted a national-level U.S. corn grain yield for 2013, which was 3.8% lower than the NASS-statistical data.

NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture Ocean Salinity (SMOS) carry satellite L-band radiometers whose primary missions are to measure soil moisture. However, they also allow retrieving of vegetation optical depth (VOD), the degree to which vegetation attenuates microwave radiation. Because attenuation is primarily a function of the total amount of liquid water in a vegetation canopy that is contained within vegetation tissue, VOD could be used to monitor seasonal changes in this quantity, which we call crop water, in major agricultural regions such as the US Corn Belt. There are two main advantages of L-band VOD: it observes the entire canopy volume because soil moisture sensitivity is maintained throughout the growing season; and since it is unaffected by cloud cover there are close to daily measurements. To show its value, we compare SMAP and SMOS VOD to satellite-scale estimates of crop productivity created using the Agricultural Integrated BIosphere Simulator (Agro-IBIS) and observed weather at the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa. We find that SMAP and SMOS VOD are directly proportional to crop water. New empirical models that relate crop water to crop dry mass were required to make this finding. We created these models with in situ data spanning multiple years and stages of crop development. The value of the proportionality constant (or “b-parameter”) relating VOD to crop water at the satellite scale is about half as large as previous estimates. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis.

The light absorption coefficient of vegetation is related to the content and composition of pigments in the plant canopy. It is a useful metric for understanding the spatial and temporal dynamics of the absorbed solar radiation, photosynthetic capacity, and productivity of vegetation. Still, its estimation in vivo is challenging due to the large variability induced by numerous features: canopy-related factors, including biochemical and structural characteristics, and factors external to the canopy such as soil background, solar irradiation, and sun-target-sensor geometry conditions. Here we revisit a semi-analytical modeling framework for deriving the light absorption coefficient of plant canopies from reflectance data. The proposed approach is based on the partition of the total light absorption coefficient into photosynthetic and non-photosynthetic pigment components in the canopy, and canopy backscattering. The model-derived absorption coefficient of chlorophyll was compared with field matchups of total canopy chlorophyll content in three crops with contrasting leaf structures, canopy architectures and photosynthetic pathways: maize, soybean, and rice. The model allows for the derivation of absorption coefficient spectra across the photosynthetically active radiation and the red edge spectral regions, as well as accurate estimations of canopy chlorophyll content, both of which are necessary for analyzing the physiological and phenological status, and the canopy-level photosynthetic capacity, of plants.

Winter annual weeds are becoming prolific in agricultural fields in the midwestern United States. The objectives of this research were to understand the roles of soil temperature (daily average and fluctuation) and moisture on the emergence of nine winter annual weed species and dandelion and to develop predictive models for weed emergence based on the accumulation of modified thermal/hydrothermal time (mHTT). Experiments were established at Lincoln, NE; Mead, NE; and at two sites (irrigated and rainfed) near Clay Center, NE, in 2010 and 2011. In July of each year, 1,000 seeds of each species were planted in 15 by 20 by 6-cm mesh baskets installed between soybean rows. Soil temperature and water content were recorded at the 2-cm depth. Emerged seedlings were counted and removed from the baskets on a weekly basis until no additional emergence was observed in the fall, resumed in late winter, and continued until emergence ceased in late spring. Weather data were used to accumulatemHTT beginning on August 1. A Weibull function was selected to fit cumulative emergence (%) on cumulativemHTT (seven base temperature [Tbase] by six base water potential [Ψbase] by three base temperature fluctuation [Fbase] candidate threshold values = 126 models); it was also fit to days after August 1 (DAA1), for a total of 127 candidate models per species. The search for optimal base thresholds was based on the theoretic-model comparison approach (Akaike information criterion [AIC]). All three components (Tbase, Ψbase, andFbase) were only important for Virginia pepperweed. For downy brome and purslane speedwell, includingTbaseand Ψbaseresulted in the best fit, whereas for dandelion includingTbaseandFbaseresulted in the best fit. A model including onlyTbaseresulted in the best fit for most species included in this study (Carolina foxtail, shepherd's-purse, pinnate tansymustard, henbit, and field pansy). For field pennycress, the model based on DAA1 resulted in the best fit. Threshold values were species specific. Soil temperature was the major environmental factor influencing winter annual weed emergence. Even though soil moisture and often temperature fluctuation are essential for seed germination, ΨbaseandFbasewere not as critical in the predictive models as initially expected. Most seedlings (> 90%) of downy brome, pinnate tansymustard, Carolina foxtail, henbit, and field pansy emerged during the fall. Virginia pepperweed, purslane speedwell, dandelion, shepherd's-purse, and field pennycress seedlings emerged during both fall and spring. The results of this research provide robust information on the prediction of the time of winter annual weed emergence, which can help growers make better management decisions.

Real-time monitoring of crop vegetation fraction and identification of development stages provides useful information for crop management. Using sensors at close range makes it possible to collect data with very high temporal resolution. This study used four-band radiometers with green, red, red edge, and near infrared spectral bands for daily monitoring of maize ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] reflectance during the growing season in three fields over 3 yr. Two fields were continuous irrigated maize and the third was managed under a maize/soybean rotation. The objectives were (i) determine diurnal and seasonal temporal behavior of reflectance in two contrasting crops, maize and soybean, (ii) remotely estimate the crop developmental stage using spectral spaces and vegetation indices, and (iii) estimate vegetation fraction using spectral spaces. Diurnal reflectance behavior indicated that the median reflectance measured within 2.5 h of solar noon was reliable for examining the daily behavior of reflectance and vegetation indices. Since the information content of reflectance in different spectral bands varies over the course of a season, spectral spaces (reflectance in one band vs. reflectance in another band) were constructed and shown to be a useful tool for identifying crop developmental stages. Five distinct stages of vegetation status (soil/residue, green-up, vegetative, senescence, and soil/stover/residue) were accurately identified using the red vs. green reflectance spectral space. Vegetation fraction was estimated using spectral spaces in maize with estimation error below 0.071 and in soybean below 0.064. Thus, high temporal resolution sensors can be a reliable tool for monitoring vegetation for a variety of applications.

A crop yield estimation model using time-series MODIS WDRVI was developed. The main feature of the proposed model is the incorporation of crop phenology detection using MODIS data, called the “Shape-Model Fitting Method”. MODIS WDRVI taken 7–10 days before the corn silking stage had strong linear correlation with corn final grain yield at both field and regional scales. The model revealed spatial patterns of corn final grain yield all over the U.S. from 2000 to 2011. State-level corn yield was estimated accurately with coefficient of variation below 10% especially for the 18 major corn producing states including Iowa, Illinois, Delaware, Minnesota, Ohio, West Virginia, Wisconsin, Michigan, Indiana, Nebraska, Kentucky, New York, South Dakota, Missouri, Pennsylvania, Tennessee, New Jersey and Maryland. The results corresponded well with the spatial pattern of high-yield regions derived from the USDA/NASS data. However, the model tended to underestimate corn grain yield in three irrigated regions: the Midwestern region depending on the Ogallala Aquifer, the downstream basin of the Mississippi, and the southwestern region of Georgia. In contrast, it tended to overestimate corn grain yield around the outlying regions of the U.S. Corn Belt, specifically, the East Coast, North Dakota, Minnesota, Wisconsin, and Missouri. The estimation accuracy of the proposed model differed depending on the region. However, the annual variation of state level corn grain yield could be detected with high accuracy, especially in the major corn producing states.

Canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in three irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm reparameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.

Quantifying actual evapotranspiration (ETa) of riparian zones is important for more robust water balance analyses that will enable better planning, managing, and allocating of water resources as well as developing strategies to protect delicate riparian ecosystem functions. The ETa, sensible heat flux (H), net radiation (Rn), soil heat flux (G), meteorological variables (air temperature, Ta; incoming shortwave radiation, Rs; wind speed, u3; relative humidity, RH; vapor pressure deficit, VPD; precipitation, etc.), and albedo were measured on an hourly time step, and leaf area index (LAI) and plant height were measured on a weekly basis for a common reed (Phragmites australis) dominated cottonwood (Populus deltoides) and peach-leaf willow (Salix amygdaloides) riparian plant community in 2009 and 2010 through extensive field campaigns conducted in the Platte River basin in central Nebraska. The two growing seasons were contrasted by warmer air temperatures, higher precipitation, and presence of flood water on the surface during the 2010 season. The seasonal variations of daily average ETa were mainly controlled by Rn and air temperature. In 2009, total ETa and precipitation were 679 mm and 280 mm, respectively, and the values were substantially greater in 2010 (982 mm and 508 mm, respectively). The seasonal daily ETa for the mixed plant community ranged from 0.5 to 8.5 mm d-1 with a seasonal average of 3.7 mm d-1 in 2009 and from 0.5 to 11 mm d-1 with a seasonal average of 5.5 mm d-1 in 2010. In 2010, ETa varied widely with meteorological conditions and in response to variations in phenology of the vegetation to flooding. In 2009, on a seasonal average basis, a total of 77% and 14% of the available energy was partitioned into ETa and H, respectively. In 2010, over 90% and -12% (negative due to flooding) of the available energy was partitioned into ETa and H, respectively. The research results presented here provide valuable ETa data and information for enhancing the understanding of the interactions between the surface/vegetation conditions and the surrounding microclimate and surface energy balance for mixed riparian vegetation. The results of this research should aid water managers and decision/policy makers in accounting for water use rates of phragmites-dominated cottonwood and peach-leaf willow riparian plant communities in water balance analyses to make better-informed water resources planning and management decisions.

The JULES-crop model (Osborne et al., 2015) is a parameterisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of crop-lands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2, US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties. The implications of our results on the choice of parameters and settings to be used in global runs of JULES-crop are also discussed.

Optical reflectance from leaf surfaces has been known for decades to increase with decreasing leaf water content. Experimental results show this increase consistently in maize in the visible (photosynthetically active radiation—PAR) and middle infrared (MIR) spectral regions, and with weaker correlation in the near infrared (NIR) region. Changes in chlorophyll concentration have been shown to be too small to substantially contribute to increasing reflectance during the duration of these experiments. Therefore, the reflectance responses to water deficit are perplexing since reflectance in the PAR region is dominated by pigment absorption, while reflectance in the MIR region is dominated by water molecule absorption. We report on recent experiments with maize that indicate that the reflectance changes during water stress in the PAR and MIR regions are due to changing optical absorption, whilst those in the NIR region are due to changing optical scatter. In addition, reflectance in PAR and MIR appears to be ...

Infrared thermography is a useful technology for examining water status in terres- trial vegetation. This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images. The plants were subjected to a range of moisture stress treatments in order to evaluate the water content in sampled leaves. The plants were irrigated with 8 different treat- ment levels (control (i.e., fully irrigated) and 1 to 7 days of water being withheld). One specific trifoliate was segmented from each of the thermal images for every plant sample, and both mean temperature and Crop Water Stress Index (CWSI) were computed for each plant. Leaf discs were taken from the same trifoliate to gravi- metrically measure relative water content (RWC). RWC had statistically significant correlation coefficients with both CWSI (r = -0.92, n = 56; p�< 0.001) and raw mean temperature (r = -0.84, n = 56; p�< 0.001). Two separate regression models were developed to predict RWC using mean raw trifoliate temperature and CWSI. Our results document that a CWSI-based regression model was better in predicting RWC than a model based on mean raw trifoliate temperature.

After harvest, the litter-C pool contributes 20 to 23% of the total C present in maize (Zea mays L.)-based agricultural ecosystems. Therefore, understanding litter-C pool dynamics is important in determining the overall C dynamics of the system and its potential to sequester C. We examined litter-C production and in situ decomposition of maize and soybean [Glycine max (L.) Merr.] litter using four annual litter cohorts (2001–2004) in three no-till management regimes: irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation. Litter inputs, i.e., litter-C production, was 20 to 30% higher in irrigated fields than the rainfed field, and maize produced approximately twice as much litter C as soybean. Litter losses, i.e., decomposition, were highly variable, but overall, after 3 yr of decomposition, only 20% litter C remained on average. We fit decomposition models to our data to predict litter-C accretion after 10 yr of management. While management and annual variation were important in fitting the model, tissue type increased model fit most, suggesting a strong role of litter physical structure in decomposition. The predicted 10-yr standing litter pool was 15 and 35% higher in the irrigated maize field than the irrigated or rainfed maize–soybean rotations, respectively. Our data clearly show that the litter-C pool is highly dynamic, with as much as a 60% increase within 1 yr. Thus, short-term C sequestration estimates in agricultural ecosystems largely reflect litter-C pool changes, which are primarily driven by litter inputs and not decomposition differences.

Published in Agron. J. 104:1336–1347 (2012) Posted online 29 June 2012 doi:10.2134/agronj2012.0065 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T leaf area index (LAI), the ratio of leaf area to ground area, typically reported as square meters per square meter, is a commonly used biophysical characteristic of vegetation (Watson, 1947). The LAI can be subdivided into photosynthetically active and photosynthetically inactive components. The former, the gLAI, is a metric commonly used in climate (e.g., Buermann et al., 2001), ecological (e.g., Bulcock and Jewitt, 2010), and crop yield (e.g., Fang et al., 2011) models. Because of its wide use and applicability to modeling, there is a need for a nondestructive remote estimation of gLAI across large geographic areas. Various techniques based on remotely sensed data have been utilized for assessing gLAI (see reviews by Pinter et al., 2003; Hatfield et al., 2004, 2008; Doraiswamy et al., 2003; le Maire et al., 2008, and references therein). Vegetation indices, particularly the NDVI (Rouse et al., 1974) and SR (Jordan, 1969), are the most widely used. The NDVI, however, is prone to saturation at moderate to high gLAI values (Kanemasu, 1974; Curran and Steven, 1983; Asrar et al., 1984; Huete et al., 2002; Gitelson, 2004; Wu et al., 2007; Gonzalez-Sanpedro et al., 2008) and requires reparameterization for different crops and species. The saturation of NDVI has been attributed to insensitivity of reflectance in the red region at moderate to high gLAI values due to the high absorption coefficient of chlorophyll. For gLAI below 3 m2/m2, total absorption by a canopy in the red range reaches 90 to 95%, and further increases in gLAI do not bring additional changes in absorption and reflectance (Hatfield et al., 2008; Gitelson, 2011). Another reason for the decrease in the sensitivity of NDVI to moderate to high gLAI values is the mathematical formulation of that index. At moderate to high gLAI, the NDVI is dominated by nearinfrared (NIR) reflectance. Because scattering by the cellular or leaf structure causes the NIR reflectance to be high and the absorption by chlorophyll causes the red reflectance to be low, NIR reflectance is considerably greater than red reflectance: e.g., for gLAI >3 m2/m2, NIR reflectance is >40% while red reflectance is