Land Surface Temperature Based Soil Moisture Dynamics Modeling for Chinese Mainland

Abstract

Since remotely sensed land surface temperature (LST) and LST-derived indexes such as surface-to-air temperature gradient (Δ T) and day-to-night LST gradient (Δ LST) all contain important soil moisture (SM) information, it is meaningful to utilize easily available and near-real-time LST data for modeling the spatiotemporal SM dynamics. However, the optimal LST-derived index to appropriately quantify SM dynamics on a large scale remains to be studied. Considering the complex and diverse climate conditions and land cover types in the Chinese mainland, this letter proposes to evaluate Z-score indexes from LST-based SM dynamic modeling for the Chinese Mainland. Monthly LST and SM during April-October in 2000-2019 years are derived from the MOD11C3 (MODIS LST product) and ERA5-Land (the global reanalysis dataset), respectively. The Pearson correlation coefficients (Rs) between ZSM (Z-score of SM) and ZLST (Z-score of LST), ZΔ T (Z-score of Δ T), as well as ZΔLST (Z-score of Δ LST) are calculated. The average R between ZSM and ZLST is 0.44 over the whole domain. It is up to 0.7 for cultivated land and grassland in semi-arid and semi-humid areas. The R between ZSM and ZLST is stronger than the ones between ZSM and ZΔ T and ZΔ LST. Overall, ZLST can be viewed as a relatively robust and easy-to-calculate indicator for modeling SM dynamics in a large region. Even if the approach used is simple, its results are encouraging because it makes sense to actually use LST to capture SM dynamics in the Chinese mainland.