A practical SIF-based crop model for predicting crop yields by quantifying the fraction of open PSII reaction centers (qL)

Abstract

Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to drive the models, which are generally lacking at the regional level. Meanwhile, statistical crop models depend heavily on training data, but it is often difficult, or even impossible, to find high-quality training data on a large scale. Solar-induced chlorophyll fluorescence (SIF), a more physiologically based proxy for gross primary production (GPP), has shown good potential for estimating GPP and crop yield. We developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content. The key improvement of our research is to parameterize the fraction of open PSII reaction centers (q_L) for crops, and incorporate variations in q_L into the SIF-based estimation of crop GPP and yield. Using a leaf-level measurement system, we provided parameters for q_L in corn and soybean. We showed that the simulated q_L closely matches the measured q_L, with R² > 0.95 and RMSE <0.05, even under conditions of high light and/or high temperature, whereas the performance of SIF alone significantly decreased under stress. By using SIF and q_L within the mechanistic light response model, one can accurately estimate crop GPP without the need to parameterize various plant physiological processes or nutrient dynamics and management practices. This improvement substantially simplifies the model, reduces the need for driving variables and calibration data, and minimizes associated uncertainties. We applied the model to estimate corn and soybean yields in the U.S. Midwest for the period 2018–2023. A comparison with eddy covariance-based GPP measurements reveals that the simulated GPP accounts for 85 % of the variability in daily observed GPP for corn and 81 % for soybean. The model's performance at the regional scale was assessed by comparing it against county-level crop yield statistics. On average, the model captures 78 % of the county-level yield variability across more than 700 counties during the study period, achieving 76 % for corn and 81 % for soybean, with RMSE values of 14.47 Bu/Acre, and 4.09 Bu/Acre, respectively. The practical, yet mechanistic, SIF-based model introduced in this study represents a significant advance in regional and national crop yield estimation.