The Effects of Landscape Heterogeneity on Brightness Temperature and Soil Moisture Retrieval

Soil moisture is a key variable to describe energy-water budgets at land surface. Passive remote sensing has played a crucial role in monitoring soil moisture from space. However, due to technical constrains and gaps in scientific understanding, the goal of 4% soil moisture accuracy are not obtained yet. With the advancement of technology and integration of radar/radiometer measurements, some of the measurement errors can be reduced. Nevertheless, the scientific understanding of the effects of landscape heterogeneity and its error contribution to soil moisture retrieval is lacking. In this paper, we have performed a synthetic study using tau-omega model, to understand the effects of within pixel heterogeneity in terms of different land cover types. This work focuses on understanding the effects of land cover type such as fresh/saline, vegetation density and type, percentage of clay on accuracy of soil moisture retrieval. Heterogeneous pixels cannot be characterized through simple averaging of contributing parameters, as these parameters exhibit non-linear behavior. For example, the brightness temperature observed for total VWC < 4.5 kg/m2 of mixed pixel with different vegetation types is far less than the average brightness temperature observed for individual vegetation types summing to total VWC. Such analysis is extended to different landcover types, to better address the effects of heterogeneity on soil moisture retrieval. Thus an attempt to develop an effective averaging technique to address the effect of nonlinear behavior on brightness temperature is made. The technique is tested by determining soil moisture accuracy obtained using retrieval algorithm.