Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China

Drought is one of the most damaging events in the grassland ecosystem. The detection and monitoring of drought are very important to maintain the balance of the grassland ecosystem. The potential of Sun-induced Chlorophyll Fluorescence (SIF) for drought detection and monitoring were explored in this study. Based on significant negative anomalies of self-calibrating Palmer drought severity index (scPDSI), precipitation (PPT), soil moisture (SM),surface water storage (SWS), and a significant positive anomaly of land surface temperature (LST), a severe drought event was accurately detected from June to August in 2016 over Hulun Buir Grassland. The far-red SIF was decomposed into its mechanical parts such as SIF, absorbed photosynthetically active radiation (APAR), normalized by APAR (SIFyield), physiological SIF emission yield (SIFpey), and total emitted SIF (SIFte), which were more sensitive to drought than the vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), modified soil adjusted vegetation index (MSAVI2), and near-infrared reflectance of vegetation (NIRV). SIF and NIRV represented the SIF indicators and the VIs, respectively, which were most affected by drought, with a decrease of -2.67% and 4.19% in June, 50.93% and 31.76% in July, and 55.58% and 39.44% in August. The correlations between anomalies of SIF indicators, VIs, and anomalies of LST, wind speed (WS) were a strong negative correlation, indicating that their reduction was caused by the anomalies of LST and WS. Moreover, the SIF indicators had a shorter lag time in response to meteorological drought than VIs. Besides, the correlations between SIF-based drought indices such as drought fluorescence monitoring index (DFMI), SIF health index (SHI), and SM were - 0.709 and - 0.783 (P < 0.01), respectively, higher than the conventional drought indices. Moreover, DFMI and SHI could reflect the changes of SM in advance, while the conventional drought indices mostly lagged behind the changes of SM. This study shows that SIF can enhance drought detection, and the SIF-based drought index can be well suitable for drought monitoring. (C) 2021 Elsevier B.V. All rights reserved.