Global Optimization of Soil Texture Maps From Satellite-Observed Soil Moisture Drydowns and Its Implementation in Noah-MP Land Surface Model

Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large-scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in-situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite-estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite-optimized soil texture maps is improved at both grid and in-situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation-oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs). Soil moisture (SM) is important for weather and climate but is often poorly simulated by Land Surface Models (LSMs). One possible reason could be the inappropriate representation of soil texture maps utilized in LSMs since current gridded soil texture maps are often derived from a limited number of in-situ measurements. In this study, we leverage the benefits of modern satellite products and land surface theories to improve several major global soil texture maps, and use the calibrated soil maps to improve SM simulation in one example LSM. Results show increased sand content over arid areas while the results for clay content show the opposite pattern. The SOC result shows an overall increase over the entire globe but is more evident in dense vegetation land covers. The model simulated SM using the calibrated soil maps generally outperforms those with the baseline soil maps. The improvement is more significant in the experiment with soil maps considering SOC. Our results here provide successful evidence for constraining soil texture data from large-scale observations. We also show that observation-oriented calibration on soil texture maps is necessary for a better land surface simulation, which is critically important for the development of ESMs. Satellite Soil moisture (SM) data is used to improve the representativeness of two major soil texture data sets at the global scale SM simulations using the updated soil maps outperform those using the baseline soil texture products SM improvement is more significant in simulations considering soil organic carbon (SOC)