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Earth's land surface is characterized by tremendous natural heterogeneityand human-engineered modifications, both of which are challenging torepresent in land surface models. Satellite remote sensing is often the mostpractical and effective method to observe the land surface over largegeographical areas. Agricultural irrigation is an important human-inducedmodification to natural land surface processes, as it is pervasive acrossthe world and because of its significant influence on the regional and globalwater budgets. In this article, irrigation is used as an example of a human-engineered,often unmodeled land surface process, and the utility of satellite soilmoisture retrievals over irrigated areas in the continental US is examined.Such retrievals are based on passive or active microwave observations fromthe Advanced Microwave Scanning Radiometer for the Earth Observing System(AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the SoilMoisture Ocean Salinity (SMOS) mission, WindSat and the AdvancedScatterometer (ASCAT). The analysis suggests that the skill of theseretrievals for representing irrigation effects is mixed, with ASCAT-basedproducts somewhat more skillful than SMOS and AMSR2 products. The articlethen examines the suitability of typical bias correction strategies incurrent land data assimilation systems when unmodeled processes dominate thebias between the model and the observations. Using a suite of syntheticexperiments that includes bias correction strategies such as quantile mappingand trained forward modeling, it is demonstrated that the bias correctionpractices lead to the exclusion of the signals from unmodeled processes, ifthese processes are the major source of the biases. It is further shown thatnew methods are needed to preserve the observational information aboutunmodeled processes during data assimilation

hydrology and earth system sciences

Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes