Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF.

Bibliographic Details
Title:	Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF.
Authors:	Hou, Xiao, Zhang, Bo, Chen, Jie, Zhou, Jing, He, Qian-Qian, Yu, Hui
Source:	Forests (19994907); Feb2024, Vol. 15 Issue 2, p339, 21p
Subject Terms:	DROUGHT management, DROUGHTS, NORMALIZED difference vegetation index, CHLOROPHYLL spectra
Abstract:	In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR² = 0.85), followed by NIRV (meanR² = 0.84), NDVI (meanR² = 0.76), and kNDVI (meanR² = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR² = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR² = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes. [ABSTRACT FROM AUTHOR]
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Database:	Complementary Index

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