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Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m−2 d−1 (29.5%) and bias of –0.5 g C m−2 d−1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha−1 (7.7%) and bias of 242.1 kg ha−1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated.

Ecosystem conductance, which describes ecosystem regulation of water and carbon exchange and links plant functions with the environment, is a critical component in ecosystem and earth system models. However, the behaviors of ecosystem conductance at the ecosystem level and its responses to environmental conditions are still largely unclear. In this study, half-hourly data of 77 eddy-covariance sites from the FLUXNET2015 dataset were used to compare four ecosystem conductance models at the ecosystem level and determine the most consistent vapor pressure deficit (VPD) dependence across plant functional types for varying soil moisture stress levels at the subdaily time scale. We used leaf-level VPD (VPDl), a better indicator of atmospheric dryness at the leaf level, for canopy-level analysis instead of measured atmospheric VPD. Detection of the best-fitted exponent of VPDl indicates that ecosystem conductance responds to VPD between optimality-theory (i.e., VPD−0.5 dependence) and Leuning’s (i.e., VPD−1 dependence) models. Accounting for different soil moisture stress levels only affected minimum ecosystem conductance and did not affect the exponent and factor of VPDl, indicating limited diurnal soil moisture-VPDl interactions. These results indicate limited interaction between xylem and stomata at subdaily time scales and that soil moisture effects can be simplified as a regulation of minimum (soil plus canopy) conductance.

Sun‐induced fluorescence (SIF) has been found to be strongly correlated with gross primary production (GPP) in a quasi‐linear pattern at the scales beyond leaves. However, the causes of the GPP:SIF relationship deviating from a linear pattern remain unclear. In the current study conducted at two maize sites in Nebraska in 2017 summer growing season, we investigated the relationship between GPP and SIF at 760 nm (F₇₆₀) at two temporal scales and quantified the contributions of incoming photosynthetically active radiation (PAR_(in)), fraction of absorbed PAR (fPAR), light use efficiency (LUE), and F₇₆₀ yield (F₇₆₀,y, defined as F760/(PAR_(in)×fPAR)) to GPP and F₇₆₀ variabilities to further understand the linearity and deviations in the GPP:F₇₆₀ relationship. We found the following: (1) For individual growth stages when canopy structure and chlorophyll content were stable, GPP and F₇₆₀ were strongly controlled by PAR_(in), while LUE and F₇₆₀,y had much lower contributions to the GPP:F₇₆₀ relationship; during this period, LUE and F₇₆₀,y had either a slightly negative or no clear relationship, which explained some deviations in the GPP:SIF relationship. (2) At the seasonal scale, the contribution of LUE to GPP variability as well as the contribution of F₇₆₀,y to F₇₆₀ variability increased and was comparable to the contribution of PARin; the LUE:F₇₆₀,y relationship showed a strong linear relationship, which strengthened the linear GPP:F₇₆₀ relationship. Both maize sites showed similar patterns. A framework was applied to estimate LUE at individual stages and as a result, significantly improved the GPP estimation, thus enhancing the SIF potential for inferring photosynthesis.

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products.

The U.S. Corn Belt, the world's biggest production region for corn and soybean combined, is prone to droughts. Currently 92% of the U.S. Corn Belt croplands are rainfed, and thus are sensitive to interannual climate variability and future climate change. Most prior studies identify the lack of soil moisture as the primary cause of agricultural drought impacts, although water-related stresses are also induced by high atmospheric water demands (i.e., vapor pressure deficit, VPD). Here we empirically attributed the variability of canopy-level stomatal conductance (Gs) and gross primary productivity (GPP) to VPD and soil water supply (i.e. volumetric soil water content, SWC), using eddy-covariance data from seven AmeriFlux eddy covariance sites in maize and soybean fields across the U.S. Corn Belt, which are well represented for the current rainfed part of the Corn Belt croplands. We used three independent approaches, including two statistical models (i.e. a multiple-linear regression model and a semi-empirical, non-linear model) and information theory, to quantify the relationship of Gs (or GPP) with VPD and SWC. The attribution result from the two models shows that VPD explains most of Gs variability (91% and 89%, respectively), and mutual information also attributed 91% of GPP variability to VPD. This finding was robust over the gradients of rainfall and temperature, crop types (maize vs. soybean), and management practices (whether irrigated or not). We reconciled our finding with the previously emphasized importance of precipitation and SWC, by conducting a path analysis, which revealed the causal relationships between precipitation, air temperature (Ta), relative humidity (RH), VPD, SWC, and Gs. We find that precipitation impacts on Gs through reduced RH and Ta to VPD (rather than directly through SWC). With increased VPD robustly projected under climate change, we expect increased crop water stress in the future for the U.S. Corn Belt.