Soil temperature estimation at different depths, using remotely-sensed data

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators necessary for ecological modeling and precision agricultural activities. In this paper, using a 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), using 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 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 nutrients and microbial activities favoring crop growth are most pronounced. 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.