Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China

作者：Chunxiao Wang, Lu Liu, Yuke Zhou, Xiaojuan Liu, Jiapei Wu, Wu Tan, Chang Xu, Xiaoqing Xiong · 发表于：Remote Sensing · 年份：2024 · DOI：10.3390/rs16101735 · 被引用次数：24 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Plant Water Relations and Carbon Dynamics

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data ...