The successful Soil Moisture Active Passive (SMAP) mission provides operational soil moisture products of high quality; yet its impacts on global carbon and water cycle estimation are yet to be further investigated. Here we assimilated the SMAP enhanced Level-2 soil moisture product at 9 km resolution into a land surface scheme in order to study the soil moisture control on the functioning of terrestrial ecosystems. We found that SMAP significantly improves soil moisture simulations, especially in the spring. Extensive wetting signals were revealed over croplands in arid and semi-arid regions and could not be explained using reanalysis meteorological data, indicating an additional water input, for example, irrigation. Stronger impacts on gross primary production and evapotranspiration simulations are found in wetting adjustments than in drying adjustments after data assimilation. This study suggests that the performance of the land surface scheme benefits greatly from assimilating the SMAP soil moisture product.

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.