Active Passive Remote Sensing of Soil Moisture

1.0 INTRODUCTION Numerous studies have shown the influence of soil moisture on the feedbacks between landsurface and climate, which in turn affect the dynamics of the atmosphere boundary layer and have a direct relationship to weather and global climate (Shukla and Mintz, 1982). Chang, et al., 1991 have shown the influence of spatial variations of soil moisture and vegetation on the development and intensity of severe storms, whereas Engman, 1997, has demonstrated the ability of soil moisture to influence surface moisture gradients, and to partition incoming radiative energy into sensible and latent heat. Better understanding of the processes involved in the forcing of, and responses to, Earth’s changing environment is needed in order to accurately assess, predict and evaluate the global hydrologic cycle and weather and climate change. In order for this to be accomplished, it is necessary to intimately understand the relationship of soil moisture to these phenomena on small and large-scales. Unfortunately, complicating these overall goals is our inability to completely observe largescale hydrologic land-surface interactions. Remote sensing enables us to estimate largescale soil moisture for the purpose of modeling the two-way interaction between land and atmosphere, making it possible to understand the nature of global climate. This paper examines a multiple techniques used to retrieve land surface parameters using microwave remote sensing. In general, past studies (Li and Islam, 1999, Mattikalli et al., 1998; Laymon et al. 1999; Jackson et al. 1995 and Schmugge et al. 1988) have focused on either regression between observed remotely sensed observations and surface soil moisture or limited comparisons between aircraft/ satellite retrievals and in-situ observations. However, in general, field experiments gather limited data, and exhaustive comparisons are generally not possible. Therefore, in this paper we attempt to combine; a) observations from PALS, b) statistical regressions c) physically based forward modeling of the sensor and d) retrievals using combinations of b and c. The above has been accomplished in three vegetation regimes; low(<0.25 kg m ), med(0.25-3.0 kg m), high( >3.0 kg m). The dataset used in this study is derived from the Southern Great Plains (SGP) 1999 experiment. This wide spectrum of land surface conditions helps to recognize the advantages and disadvantages of carrying out passive and active remote sensing under varying vegetation, soil moisture and roughness conditions.