The Process-Cognizant Vegetation Drought Model (PCVDM): Analyzing vegetation drought dynamics in Hunan Province,...

The mounting frequency and severity of droughts in a warming climate pose a formidable threat to vegetation ecosystems, triggering widespread vegetation mortality. Accurate quantification of vegetation drought is paramount for understanding and regulating vegetation mortality during drought events, yet ongoing controversy surrounds precise quantitative vegetation drought. This study develops a feature space based on an improved multispectral vegetation index and land surface temperature, designing a Vegetation Drought Response (VDR) module to characterize the spatiotemporal features of vegetation's fluctuation response to drought. Taking into account the dynamic interaction between vegetation and soil moisture under drought stress, the study identifies the Vegetation Drought Process (VDP) and defines the Vegetation Drought Threshold (VDT). By integrating VDR, VDP, and VDT, a Process-Cognizant Vegetation Drought Model (PCVDM) is constructed, enabling the quantitative remote sensing retrieval of vegetation drought conditions. Using remote sensing to monitor vegetation changes in Hunan Province, China, from 2000 to 2023 during the summer season, the analysis reveals that high-altitude areas (>800 m) show increased greenness attributed to rising temperatures. In contrast, low-altitude regions (<200 m) face exacerbated vegetation drought due to heightened evapotranspiration. Medium-altitude areas (∼400 m) are affected by both factors, resulting in a coexistence of increased greenness and drought conditions.