The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change

The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because its strong drought-monitoring capabilities and ease implementation. However, temporal errors in land surface (LST) normalized difference (NDVI) can affect warm cold edges, thus determining quality TVDI, especially regions affected by climate change, such as Shandong Province. This paper explores this issue region 2011, using daily MODIS MOD09GA MOD11A1 data products. For each image acquisition time, edges NDVI–LST were extracted based on NDVI, derived from red near-infrared reflectance data, LST, dataset. Then, variations with LST NDVI analyzed. Subsequently, influence values temperature, precipitation during observed period, was assessed a linear regression. soil moisture (SM) obtained Global Land Data Assimilation System (GLDAS) datasets crop water stress (CWSI) MOD16A2 products assessment. spatial Province 2011 to 2020 measured Theil–Sen median trend analysis Mann–Kendall test. results show that apparently random evident evolution slope edge, indicating appropriate determine boundary edge. Daily also edge similar way. Additionally, change had negative correlation positive intercept. validation there significant between TVDI GLDAS (R2 > 0.62) 12 scatter plots. Therefore, we deduced monthly yearly product produced has higher precision than 8-day change. slight moderate droughts first increased then decreased, and, particular, some areas presented severe 2015. study are important scheduling irrigation warnings.