Drought Dynamics and Drivers Across Wheat Fields in the Huaihe Basin: Improved Temperature Vegetation Drought Index Using Reinforcement Learning

Regional drought monitoring based on the Temperature Vegetation Drought Index (TVDI) holds significant potential in efforts to ensure food safety. However, its empirical determination of dry and wet edges introduces subjectivity and uncertainty, limiting its accuracy and applicability. An improved TVDI (iTVDI) was developed by optimizing boundary parameters using reinforcement learning, based on maximizing the correlation between the TVDI and the ERA5-Land soil moisture dataset. The findings are as follows: (1) The enclosed area and the absolute value of dry edge slope of iTVDI was 34.83–39.97% and 0.79–33.75% larger than TVDI, indicating that the iTVDI can be used to achieve better representation of drought conditions. (2) The iTVDI showed stronger correlations with ERA5 soil moisture (r: −0.416 to −0.174), with average |r| values 17.25% higher than TVDI; its correlations with Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Vegetation Condition Index (VCI) were also 12.69–75.43% higher. (3) From 2005 to 2024, the spring drought in the Huaihe Basin intensified, with the annual iTVDI increasing by 0.008–0.011, primarily driven by rising temperature, potential evapotranspiration, and vapor pressure deficit. Overall, the iTVDI is proved to be more accurate and reliable for monitoring drought dynamics and driving factors.