Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),whic...Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),which is a classical remote-sensing-based drought index,was proposed to capture typical drought characteristics across diverse regions and land-cover types.Relative soil moisture data obtained from the China Meteorological Administration Land Data Assimilation System(CLDAS)and vegetation net primary productivity(NPP)from the Atmosphere-Vegetation Interaction Model were further utilized to quantify TVDI sensitivity and evaluate its impacts.It was clear that TVDI successfully extracted the most severe drought events in southern China,exhibiting a significantly higher correlation coefficient with the minimum value of relative soil moisture than that obtained from the average value.In general,TVDI was negatively correlated with relative soil moisture and NPP,with the strength of these correlations gradually weakening as soil depth increased.Among land-cover types,TVDI performed best in depicting drought in cropland,followed by grassland and forest.These results can promote our acknowledgement of the typical drought characteristics and their impacts on vegetation,thereby providing valuable guidance for drought prevention strategies.展开更多
基金Innovation and Development Special Project of the China Meteorological Administration(CXFZ2024J051)National Key R&D Program of China(2022YFD2300200)。
文摘Drought is one of the most catastrophic natural disasters and can be effectively monitored via remote sensing.Derived from Fengyun-3D(FY3D)products from 2021 to 2023,the Temperature Vegetation Dryness Index(TVDI),which is a classical remote-sensing-based drought index,was proposed to capture typical drought characteristics across diverse regions and land-cover types.Relative soil moisture data obtained from the China Meteorological Administration Land Data Assimilation System(CLDAS)and vegetation net primary productivity(NPP)from the Atmosphere-Vegetation Interaction Model were further utilized to quantify TVDI sensitivity and evaluate its impacts.It was clear that TVDI successfully extracted the most severe drought events in southern China,exhibiting a significantly higher correlation coefficient with the minimum value of relative soil moisture than that obtained from the average value.In general,TVDI was negatively correlated with relative soil moisture and NPP,with the strength of these correlations gradually weakening as soil depth increased.Among land-cover types,TVDI performed best in depicting drought in cropland,followed by grassland and forest.These results can promote our acknowledgement of the typical drought characteristics and their impacts on vegetation,thereby providing valuable guidance for drought prevention strategies.
