Spatial and temporal variations of drought in Sichuan Province from 2001 to 2020 based on modified temperature vegetation dryness index (TVDI)

Drought indicators based on remote sensing include the temperature vegetation dryness index (TVDI) and the crop water stress index (CWSI). The data was processed using TVDI, which was calculated by parameterizing the MODIS EVI and LST data connection. For drought monitoring, we compared the efficiency of TVDI with that of CWSI, which is obtained from the MOD16A2 products. The study's findings revealed that drought conditions measured by TVDI and CWSI had a number of differences and similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity in this region. High TVDI values were mainly concentrated on the northwestern Sichuan Plateau and mountainous areas of southwestern Sichuan, corresponding to extreme drought. The Panzhihua and the mountainous area of southwestern Sichuan had relatively high CWSI values. Spring had the highest TVDI values, followed by autumn and winter. TVDI and CWSI have different patterns, showing moderate and severe drought conditions in different areas. Overall, CWSI values showed a significant decreasing trend (P < 0.05) from 2001 to 2020. The overall trend change of TVDI was insignificant, mainly based on an insignificant increase and an insignificant decrease. An extremely significant decreasing trend, mainly concentrated in the eastern Sichuan basin plain, accounted for 15.54% of the entire province. Spring, summer, autumn, and winter account for 74.33%, 59.15%, 68.28%, and 64.87% of Sichuan Province, respectively, in total area change. The eastern Sichuan basin plain showed a significant increasing trend, accounting for 1.69% of the province in winter. TVDI correlates positively with Yearly maximum value of daily minimum temperature (TNx), Yearly maximum value of daily maximum temperature (TXx), and Yearly maximum consecutive one-day precipitation (PX1), and negatively with Yearly minimum value of daily maximum temperature (TXn), Yearly minimum value of daily minimum temperature (TNn), and Yearly mean temperature (YMT)