Estimation of soil moisture and soil temperature over India using the Noah multi-parameterisation land surface model

Soil moisture (SM) and soil temperature (ST) are critical state variables for characterizing the land surface, among which SM is recognized as an Essential Climate Variable necessary to understand changes to the Earth's systems. Remote sensing-based maps of SM and ST over India lack in temporal and spatial scales, which can be addressed through Land Surface Models (LSMs). This study examines the performance of the Noah Multi-Parameterisation LSM to estimate multi-level SM and ST within domains at 5 and 10 km spatial resolutions and 3-hourly frequency over India. Results indicate that among the inputs for precipitation forcing, viz. CHIRPS, GDAS and IMERG, the best performance is obtained with CHIRPS and IMERG for the 5 and 10 km resolutions, respectively. Incorporating a dynamic Greenness Vegetation Fraction (GVF) along with IMERG intensified post-precipitation dry downs in predicted SM and improved the accuracy of SM and ST by up to 25.21% (0.029 m(3)/m(3)) and 8.36% (0.2 K), respectively. Better performance was also observed over Clay, Loam and Sandy Clay Loam soils, which extend over 67% of India's land area, compared to other soil types. The accuracy of model predictions at 10 km resolution is about 0.095 m(3)/m(3) for surface-level SM and about 4.22 K for ST. Performance metrics indicate a correlation of 0.74; a root mean square error of 0.048 m(3)/m(3) and a bias of 0.004 m(3)/m(3) in surface-level SM against the satellite-based SM product from ESA C3S. These results indicate the potential for LSMs to obtain information on SM and ST over India.