A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

作者：Oz Kira, Jiaming Wen, Jimei Han, Andrew J. McDonald, Christopher B. Barrett, Ariel Ortiz‐Bobea, Yanyan Liu, Liangzhi You, Nathaniel D. Mueller, Ying Sun · 发表于：Environmental Research Letters · 年份：2024 · DOI：10.1088/1748-9326/ad3142 · 被引用次数：36 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Climate change impacts on agriculture

Abstract Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India’s Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF s...