Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2012
Publication Date: 11/16/2012
Publication URL: http://handle.nal.usda.gov/10113/58434
Citation: Hain, C., Crow, W.T., Anderson, M.C., Mecikalski, J. 2012. An EnKF dual assimilation of thermal-infrared and microwave satellite observations of soil moisture into the Noah land surface model. Water Resources Research. DOI: 10.1029/2011WR011268. Interpretive Summary: Estimates of soil water availability are vital for a range of agricultural applications including: irrigration scheduling, fertilizer application optimization and operational drought monitoring. USDA scientists have pioneered the use of both microwave and thermal remote sensing technologies for measuring soil moisture over large areas. This work describes the use of data assimilation tools to simultaneously integrate both microwave and thermal-based remote sensing retreivals into a land surface model. By considering all possible sources of information regarding soil moisture (i.e. model-based, microwave remote sensing-based and thermal remote sensing-based), such integration maximizes the subsequent accuracy of modeled soil moisture estimates and therefore the utility of these estimates for subsequent agricultural applications. Key results in this manuscript demonstrate the feasibility of the data assimilation approach and verifies that the approach effectively minimizes error in soil moisture predictions.
Technical Abstract: Studies which have attempted to assimilate remotely-sensed soil moisture (SM) into land surface models have mainly focused on the application of retrievals from microwave (MW) sensors. However, SM retrievals from thermal (TIR) sensors have been shown to add unique information especially in areas where MW sensors are not able to provide accurate retrievals (i.e. moderate to dense vegetation). In this study, the TIR product is based on a SM methodology associated with surface flux estimates from the Atmosphere Land Exchange Inverse (ALEXI) model, while the MW product is provided by Vrijie Universiteit Amsterdam (VUA)-NASA surface SM product based on Land Surface Parameter (LPRM) model. A series of data assimilation experiments using an ensemble Kalman filter (EnKF) are proposed to quantify the impact of assimilating TIR and MW SM retrievals in isolation and within a dual assimilation framework. The relative skill of each assimilation methodology is assessed through a data-denial design, where the LSM is forced with an inferior precipitation dataset. The ability of each assimilation configuration to correct for precipitation errors is quantified through the comparison of SM with a LSM simulation forced with a high-quality precipitation dataset. Finally, the entire assimilation framework is repeated using a new technique based on the estimation of triple collocation error as an estimate of retrieval error in the EnKF. These results are compared to the assimilation simulations that employ a constant, ad-hoc specification of model and retrieval error in the EnKF to assess the impact of a more sophisticated representation of error.