Mapping crop phenophases in reproductive growth period by satellite solar-induced chlorophyll fluorescence: A case study in mid-temperate zone in China

Extracting crop phenophases from satellite remote sensing data is crucial for managing agricultural activities and estimating crop yield over large scales. The traditional Vegetation Indices (VIs), such as the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), primarily indicate changes in vegetation greenness, which characterize crop phenophases well in vegetative growth period but may be difficult to associate with phenophases in reproductive growth period that are more related with physiology status. In this study, we investigated the potential of the satellite Solar-Induced chlorophyll Fluorescence (SIF) on extracting the crop phenophases in reproductive growth period, i.e., milk-ripe phase and maturity phase, using single-season cropland in mid-temperate zone in China as a test bed. We found that SIF outperformed EVI and NDVI in extracting milk-ripe phase and maturity phase of maize, rice and wheat using double logistic method. In particular, SIF-derived maturity phase were closer to field-observed phenophases (Mean Bias = 0.73 days) with higher R2 (0.87) than that from EVI and NDVI. At a regional scale, the milk-ripe phase and maturity phase were observed from August to mid-September, and from mid-September to mid-October, respectively, which varied with crop types and in spatial distribution. Out of all crops in mid-temperate zone in China, 65% experienced a delay in the milk-ripe phase, whereas 77% exhibited a delay in the maturity phase from 2001 to 2019. In addition to the adjustment of human managements under climate warming, we further found that crop phenophases in reproductive growth period exhibited the strongest correlation with preseason water-related environmental variables, particularly vapor pressure deficit and total precipitation. This work highlighted the potential of satellite SIF in identifying the crop-specific milk-ripe and maturity phases, and improving our understanding for spatio-temporal variations of crop phenophases in reproductive growth period as well as their responses to preseason environmental variables, which will in turn promote SIF applications in agriculture.