A dryness index TSWDI based on land surface temperature, sun-induced chlorophyll fluorescence, and water balance

Abstract

Drought has a serious impact on the health of terrestrial ecosystems, socio-economy and human health. It is important to accurately monitor drought conditions. Soil moisture (SM) can directly characterize surface dryness and wetness. Precipitation (PPT), evapotranspiration (ET), land surface temperature (LST), and vegetation status indirectly relate to SM, and they influence each other. Therefore, using one index cannot comprehensively and objectively reflect the real situation of surface dryness and wetness. In this study, based on the Z-score data of LST, OCO-2-based solar-induced chlorophyll fluorescence product (GOSIF) and water balance (WB) from 2003 to 2018, a new three-dimensional (3D) drought index, namely temperature-SIF-Water Balance Dryness Index (TSWDI), was constructed using Euclidean distance method. The accuracy of TSWDI was verified by soil moisture (SM), standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI), and compared with other recognized drought indices. The results showed that TSWDI had a higher correlation with SM (R = 0.340) and SPEI (R = 0.533) than vegetation health index (VHI) (R was 0.201 and 0.207, respectively), temperature-vegetation-soil moisture dryness index (TVMDI) (R was −0.022 and −0.031, respectively), and temperature vegetation precipitation dryness index (TVPDI) (R was 0.123 and 0.132, respectively). Furthermore, taking the frequency of drought occurrences monitored by SPEI and the normalized anomaly of SM as the benchmark, the errors of drought frequency between TSWDI/TVMDI/TVPDI/VHI and SM/SPEI were obtained, respectively. The results showed that the mean error between TSWDI and SM, SPEI were 0.12 and 0.21, respectively, which were lower than that of VHI (0.19, 0.24), TVMDI (0.87, 0.63) and TVPDI (0.89, 0.69). These all indicated that TSWDI could better monitor regional drought conditions than VHI, TVMDI, and TVPDI in our study area. TSWDI had good applicability in monitoring meteorological and agricultural drought. In addition, the spatial variations and patterns of drought conditions in the study area were explored by the annual time series of TSWDI. The results showed that the study area became wetter at an average rate of 0.0064/year. The pixels reaching significant wetting (P < 0.05) accounted for 49.18%, and only 12.14% of the area reached significant drying (P < 0.05), which was consistent with that of SPEI. Finally, the results from the cross-correlation method indicated that the percentage of TSWDI which was ahead, synchronized, and lag of SM changes were 10.13%, 89.47%, and 0.4%, respectively. TSWDI was further employed to identify drought events in China. It demonstrated that TSWDI and VHI can more accurately capture the onset, evolution, and end of drought than TVMDI and TVPDI in the five southwestern provinces of China. Moreover, R (0.627) between TSWDI and the normalized anomaly of SM was higher than that of VHI (R = 0.619). It indicated that TSWDI can recognize more accurately than TVMDI, TVPDI, and VHI for drought events in the five southwestern provinces of China. Therefore, TSWDI can more accurately monitor regional drought conditions and respond in advance or synchronously to SM changes. TSWDI contains several key indicators (SIF, LST, and WB) of surface drought, in which SIF can reflect the physiological characteristics of vegetation, temperature can affect vegetation transpiration and WB affects vegetation absorbing water from the soil. When drought occurs, TSWDI can synthesize the changes in these three indicators. It can be concluded that TSWDI has great potential for evaluating drought conditions in China.