Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: March 15, 2011
Publication Date: N/A
Technical Abstract: Several algorithms have been used to retrieve surface soil moisture from brightness temperature observations provided by low frequency microwave satellite sensors such as the Advanced Microwave Scanning Radiometer on NASA EOS satellite Aqua (AMSR-E). Most of these algorithms have originated from the same land surface microwave emission model, frequently referred to as the tau-omega equation, and can be categorized as Single-Channel Retrieval (SCR) and Multi-Channel Inversion algorithms (MCI). Although both SCR and MCI algorithms are based on the same low-frequency microwave radiation transfer equation and applied to the same AMSR-E observations, the soil moisture data products generated with these algorithms are often quite different and it is difficult for one to claim superiority for all conditions in term of retrieval accuracy evaluated against in situ soil moisture measurements. With new L-band satellite brightness temperature observations becoming available from the ESA Soil Moisture and Ocean Salinity (SMOS) and future NASA Soil Moisture Active/Passive (SMAP) missions, it is necessary to analyze the essential differences of these algorithms and find out how to develop a more robust/reliable one for the satellite sensors. This study implements the SCR algorithm by Jackson (1993)  and two MCI algorithms (one by Njoku & Li, 1999 and the other by de Jeu & Owe, 2003) with the brightness temperature observations of AMSR-E for the contiguous United States for two years, 2003 and 2004. The top layer soil moisture simulations from the Noah land surface model [reference] that are forced by the best available atmospheric data are used to inverse various unknown ancillary parameters of the soil moisture retrieval algorithm. The retrievals from the three algorithms are then analyzed for various settings of algorithm parameters or input data against the original soil moisture values. The spatial and temporal variations of performance of the algorithms demonstrate their advantages and disadvantages under various circumstances.  T.J. Jackson. Measuring surface soil moisture using passive microwave remote sensing. Hydrol. Process. Vol.7, pp.139-152, 1993.  E.G. Njoku and L. Li. 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. IEEE Trans. Geosci. Remote Sens. 37(1): 79-93.  M.R. Owe, R. de Jeu, and T. Holmes (2008), Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769.