LAUSR

Abstract Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature (ST) measured at different depths (0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature (LST) and normalized difference vegetation index (NDVI) derived from AQUA/TERRA MODIS data, and solar declination (Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination (R2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors (RMSEs) and R2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.