SIF-Based GPP Is a Useful Index for Assessing Impacts of Drought on Vegetation: An Example of a Mega-Drought in Yunnan Province, China

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Abstract

The impact of drought on terrestrial ecosystem Gross Primary Productivity (GPP) is strong and widespread; therefore, it is important to study the response of terrestrial ecosystem GPP to drought. In this paper, we compared the correlations of Sun-induced Chlorophyll fluorescence (SIF), Enhanced Vegetation Index (EVI), and Normalized Differential Vegetation Index (NDVI) with the drought index sc_PDSI, estimated GPP in Yunnan Province, China, based on SIFTOTAL data (SIF data with canopy effects eliminated), and analyzed the response characteristics of GPP to drought for one mega-drought event (2009–2011) in combination with the sc_PDSI drought index. The results show that SIF is more sensitive to drought than the NDVI and EVI; the correlation between the GPP estimated based on SIF data (GPPSIF ) and the actual observed flux values (R2 = 0.83) is better than GPPGLASS and GPPLUE, and the RMSE is also lower than those two products. This drought has a serious impact on GPP, and the monthly average values of the effect of drought on GPP (GPPd) in Yunnan Province in 2009, 2010, and 2011 are −11.37 gC·m−2·month−1, −23.48 gC·m−2·month−1 and −17.92 gC·m−2·month−1, which are 8.6%, 17.48% and 13.85% of the monthly average in a normal year, respectively. The spatial variability of GPP response to drought is significant, which is mainly determined by the degree, and duration of the drought, the vegetation type, the topography, and anthropogenic factors. In conclusion, GPPSIF quickly and accurately reflects the process of this drought, and this study helps to elucidate the response of GPP to drought conditions and provides more scientific information for drought prediction and ecosystem management.

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Li, C., Peng, L., Zhou, M., Wei, Y., Liu, L., Li, L., … Wu, X. (2022). SIF-Based GPP Is a Useful Index for Assessing Impacts of Drought on Vegetation: An Example of a Mega-Drought in Yunnan Province, China. Remote Sensing, 14(6). https://doi.org/10.3390/rs14061509

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