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 on
vegetation dynamics. Recent advancements in satellite-based solar-induced chlorophyll
fluorescence (SIF) remote sensing offer unprecedented opportunities to monitor and understand
vegetation responses to drought on a large scale. In this study, we utilized high-resolution
TROPOspheric Monitoring Instrument (TROPOMI) SIF data, Bidirectional Reflectance Distribution
Function and Albedo (BRDF/Albedo) Model Parameters dataset (MODIS MCD43C1), MODIS land
cover data, and meteorological information to investigate the physiological responses of crops in
the Huang-Huai-Hai Plain of China during the drought period of 2019. Our results demonstrate
that NIRv, SIF, and BRDF-adjusted SIF/PAR (SIFn) exhibited significant dynamic changes during the
drought 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 findings
provide essential insights into our understanding of plant responses to drought conditions at
large spatial scales and underscore the unique value of high-resolution remote sensing SIF
observations in tracking the physiological responses of vegetation to water stress.