Vegetation drought monitoring in the Huang-Huai-Hai Plain of China using solar-inducedfluorescence and near-infrared reflectance

Drought is a complex and pervasive natural disaster that frequently exerts adverse effects onvegetation dynamics. Recent advancements in satellite-based solar-induced chlorophyllfluorescence (SIF) remote sensing offer unprecedented opportunities to monitor and understandvegetation responses to drought on a large scale. In this study, we utilized high-resolutionTROPOspheric Monitoring Instrument (TROPOMI) SIF data, Bidirectional Reflectance DistributionFunction and Albedo (BRDF/Albedo) Model Parameters dataset (MODIS MCD43C1), MODIS landcover data, and meteorological information to investigate the physiological responses of crops inthe Huang-Huai-Hai Plain of China during the drought period of 2019. Our results demonstratethat NIRv, SIF, and BRDF-adjusted SIF/PAR (SIFn) exhibited significant dynamic changes during thedrought period, outperforming traditional vegetation indices such as NDVI in sensitivity.Furthermore, a high correlation was observed between anomalies in precipitation and SIFn,elucidating the substantial impact of moisture availability on crop physiology. These findingsprovide essential insights into our understanding of plant responses to drought conditions atlarge spatial scales and underscore the unique value of high-resolution remote sensing SIFobservations in tracking the physiological responses of vegetation to water stress.