Publications

Liu, YJ; You, CH; Zhang, YG; Chen, SP; Zhang, ZY; Li, J; Wu, YF (2021). Resistance and resilience of grasslands to drought detected by SIF in inner Mongolia, China. AGRICULTURAL AND FOREST METEOROLOGY, 308, 108567.

Abstract
Currently, stability of grassland ecosystem is facing serious challenges due to the occurrence of global warming and the increased frequency and intensity of extreme droughts. The stability of grassland ecosystem has two distinct components: 'resistance' and 'resilience'. However, the evaluation of resistance and resilience mechanisms is hindered by the lack of tools for accurately monitoring the response of grasslands to drought across spatio-temporal scales. In this study, the resistance and resilience of grassland ecosystem are investigated by combining multi-source data, including space-borne solar-induced chlorophyll fluorescence (SIF), Gross Primary Productivity (GPP) estimated by flux towers, and MODIS visible near-infrared (VNIR) data. The characteristics of grassland ecosystem resistance and resilience to drought are obtained from the grasslands of Inner Mongolia, China, which suffered from a prolonged scarcity of rainfall in 2009. Space-borne SIF is found to be a beneficial tool for monitoring the seasonal dynamics of vegetation productivity compared to MODIS VNIR data. Space-borne SIF features a higher accuracy for monitoring the recovery time, resistance and resilience of grasslands to drought compared to MODIS VNIR data. Spatially, the resilience of Inner Mongolia grasslands to drought measured by SIF is greater than that measured by MODIS VNIR data, while the resistance of Inner Mongolia grasslands to drought measured by SIF is less than that measured by MODIS VNIR data. Moreover, areas with high resistance are located at the interlaced zone of grassland ecosystem and farmland or forest, where the resilience is low. These results demonstrate that space-borne SIF has great potential for monitoring the resistance and resilience of grasslands in response to drought over large regions.

DOI:
10.1016/j.agrformet.2021.108567

ISSN:
0168-1923