On The Retrievals Of Surface Soil Moisture From Simulated Smos And Amsr Brightnesstemperatures

Shou-Fang Liu 1,2 , Yuei-An Liou 3 , and Wen-June Wang 1
1. Department of Electrical Engineering, National Central University, Chung-Li 320, Taiwan.
2. Department of Industrial Design, Oriental Institute of Technology, Taipei 220, Taiwan.
3. Center for Space and Remote Sensing Research, National Central University, Chung-Li 320,Taiwan.
Email: yueian@csrsr.ncu.edu.tw.
Tel: (03) 4227151 ext 7631. Fax: (03) 4254908.

Key Words: Soil Moisture, L-band Radiometry

Abstract
We present the retrievals of surface soil moisture (SM) by simulated Advanced Microwave Scanning Radiometer (AMSR) and Soil Moisture and Ocean Salinity (SMOS) "simulate" brightness temperatures. The nonlinear mapping from brightness temperatures onto SM is handled by an Error Propagation Learning Back Propagation (EPLBP) neural network (NN). AMSR is a space-borne microwave radiometer operating at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz to be launched in November 2001 by the National Space Development Agency of Japan (NASDA). It is designed to scan conically at an incidence angle of 55 degrees. SMOS mission is tentatively scheduled for launching by European Space Agency (ESA) in 2005. Its payload Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) measures brightness temperatures at a wide range of incidences, approximately from 0 to 50 degrees. The well-known Land Surface Process/Radiobrightness (LSPR) model is used to provide SM and AMSR brightness temperatures at both 6.9 and 10.7 GHz for an incidence angle of 55 degrees, and SMOS brightness temperatures at L-band for multiple incident angles of 0, 10, 20, 30, 40, and 50 degrees.

Introduction
Soil Moisture (SM) dominates the partitioning of net radiation energy into sensible and latent heat fluxes, and rainfall into runoff and root-zone storage at the land-air interface. Hence, it plays a crucial role in current hydrologic, climatic, agricultural, and biogeochemical models, and becomes a parameter of great interest in the field of remote sensing. It is linked to radiometric signatures through its influence on microwave emission of the land surfaces (Njoku and Li 1999; Liou et al. 1999a).

In this paper, the retrievals of surface SM from simulated AMSR and SMOS brightness temperatures by a newly developed EPLBP neural network are presented. AMSR is a space-borne microwave radiometer operating at 6.9, 10.7, 18.7, 23.8, 36.5, 50.3, 52.8, and 89.0 GHz to be launched in November 2001 by the National Space Development Agency of Japan (NASDA). It is designed to scan conically at an incident angle of 55 degrees. SMOS satellite is tentatively scheduled for launching by European Space Agency (ESA) in 2005. Its payload, L-band (1.4 GHz) 2D interferometric radiometer, measures brightness temperatures at a wide range of incident angles, approximately from 0 to 50 degrees. The LSP/R model is used to provide time series of SM and brightness temperatures at AMSR's 6.9 and 10.7 GHz channels for an incident angle of 55 degrees, and at L-band of the SMOS for multiple incident angles of.30 0, 10, 20, 30, 40, and 50 degrees. These multiple incident angles allow us to design a variety of observation modes based on the viewing and instrumental characteristics of the AMSR and SMOS. For example, L-band brightness temperature at any single look angle can be used to infer SM. Meanwhile, it can be combined with either the observation at the other angles to become an L-band 2D or multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become an integrated AMSR and SMOS observation mode.

LSP/R Model
We have studied radiometric characteristics of land surfaces for the purpose of sensing surface parameters by microwave radiometry for many years. Our approach is to improve the understanding and capability by incorporating land surface processes into microwave emission models. As a consequence, a series of LSP/R models for bare soils and prairie grassland have been developed (Liou and England 1996, 1998a, 1998b; Liou et al. 1999a). Each of these LSP/R models consists of two modules, an LSP module and an R module. The LSP module simulates land-air exchange of energy and moisture to predict temperature and moisture profiles of soil and, if any, vegetation. The R module utilizes predictions of temperature and moisture fields from the LSP module to compute brightness temperatures of the terrain.

The current study utilizes the LSP/R model developed and validated by Liou et al. (1999a) for prairie grassland in South Dakota. The LSP/R model was previously integrated into a Dynamic Learning Neural Network to demonstrate the capability of L-band radiometer in sensing SM (Liou et al. 1999b, 1999c). As the SMOS mission is likely to be carried out in the year of 2005 by ESA, European Space Agency (Kerr et al. 1998), it is of great interest to examine the capability of L-band radiometry in sensing SM based on the SMOS viewing design, and its combination with the viewing designs of the other space-borne sensors such as AMSR and AMSR-E.

The EPLBP Neural Network
Radiometric signatures of a vegetation-covered field reflect an integrated response of the soil and vegetation system to the observing microwave system. This allows one to link surface parameters to the radiometric signatures by

Y = ƒ (X)

where Y is a feature vector of surface parameters (variables of interest), X is an observation vector of radiometric signatures, and f is a mapping function.