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

Ying Liu; Chaoya Dang; Hui Yue; Chunguang Lyu; Xuehui Dang

doi:10.1016/j.scitotenv.2021.145271

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

Sci Total Environ. 2021 May 20:770:145271. doi: 10.1016/j.scitotenv.2021.145271. Epub 2021 Jan 22.

Authors

Ying Liu¹, Chaoya Dang¹, Hui Yue², Chunguang Lyu³, Xuehui Dang⁴

Affiliations

¹ College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.
² College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China. Electronic address: yuehui@xust.edu.cn.
³ Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276005, China.
⁴ First Crust Monitoring and Application Center, China Earthquake Administration, Tianjin 300000, China.

PMID: 33513493
DOI: 10.1016/j.scitotenv.2021.145271

Abstract

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 (SIF_yield), physiological SIF emission yield (SIF_pey), and total emitted SIF (SIF_te), 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 (NIR_V). SIF and NIR_V 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.

Keywords: Decomposing the far-red Sun-induced chlorophyll fluorescence; Redundancy analysis; SIF-based drought index; Vegetation index; Wavelet coherence.