Nowadays, there are many dryness indices based on vegetation indices (VI_s)-land surface temperature (T_s) feature space. Which dryness index should we use in dryness monitoring? The differences and capabilities for dryness monitoring among seven indices were evaluated in this study. These dryness indices were based on VI_s-T_s or Albedo feature space. For instance, Temperature Vegetation Dryness Index (TVDI) from a triangle NDVI (Normalized Difference Vegetation Index)-T_s feature space (TVDI_t), TVDI from bi-parabolic NDVI-T_s feature space (TVDI_c), TFDI from FPAR (Fraction of Absorbed Photosynthetically Active Radiation)-T_s feature space, TVDI from LAI (Leaf Area Index)-T_s feature space (TLDI), TVDI from EVI (Enhanced Vegetation Index)-T_s feature space (TEDI), TVDI from SAVI (Soil Adjusted Vegetation Index)-T_s feature space (TSDI) and Vegetation Condition Albedo Dryness Index (VCADI) from NDVI-Albedo feature space. In this study, the assimilated 5/10 cm depth soil moisture, field measured 10 cm depth soil moisture, precipitation, and the assimilated land surface temperature data were selected as the indicators to test the performance of each dryness index in two periods of 2013/2015 and three periods of 2018. The results showed that the spatial distributions of dryness from TVDI_c, TVDI_t, TFDI, TLDI, TEDI, and TSDI were similar, except for VCADI. The ability of TSDI and TVDI_t in dryness monitoring was not as good as TVDI_c and TEDI, however, the robustness of them was stable and can be an alternative dryness index. TFDI can be applied to evaluate dryness conditions, but its robustness was not stable and its monitoring performance was not as good as other indices in Shaanxi province, China. Although TLDI can detect dryness conditions when NDVI reached saturation, its robustness was worse than TEDI. When NDVI did not reach saturation, TVDI_c had the best ability in dryness monitoring. TEDI was the optimal dryness index when NDVI was approaching or reaching saturation. This study determined that NDVI reached saturation (LAI = 3) when its average value was >0.5368.

目前，基于植被指数（VI _s）-地表温度（T _s）特征空间的干旱指数有很多。我们应该在干燥监测中使用哪种干燥指数？本研究评估了七个指标之间干燥监测的差异和能力。这些干燥指数基于 VI _s -T _s或反照率特征空间。例如，来自三角形 NDVI（归一化差异植被指数）-T _s特征空间（TVDI _t）的温度植被干度指数（TVDI）、来自双抛物线 NDVI-T _s特征空间的 TVDI（TVDI _c）、来自 FPAR 的 TFDI（吸收光合有效辐射的分数)-T_s特征空间，来自 LAI（叶面积指数）的 TVDI-T _s特征空间（TLDI），来自 EVI（增强植被指数）的 TVDI-T _s特征空间（TEDI），来自 SAVI（土壤调整植被指数）的 TVDI-T _s NDVI-Albedo 特征空间中的特征空间 (TSDI) 和植被状况反照率干燥指数 (VCADI)。本研究以同化5/10 cm深度土壤水分、野外实测10 cm深度土壤水分、降水、同化地表温度数据为指标，对2013/2013年两个时期各干旱指数的表现进行检验。 2015 年和 2018 年三个时期。 结果表明，从 TVDI _c、TVDI _t、TFDI、TLDI、TEDI 和 TSDI 相似，但 VCADI 除外。TSDI和TVDI _t在干度监测方面的能力不如TVDI _c和TEDI，但其稳健性稳定，可作为替代干度指标。TFDI可用于评价干旱条件，但其稳健性不稳定，监测性能不如陕西省其他指标。虽然 TLDI 可以检测到 NDVI 达到饱和时的干燥情况，但其鲁棒性不如 TEDI。当 NDVI 未达到饱和时，TVDI _c干度监测能力最强。当 NDVI 接近或达到饱和时，TEDI 是最佳干燥指数。本研究确定 NDVI 在其平均值 >0.5368 时达到饱和 (LAI = 3)。