A Simulation Study of Compact Polarimetry for Radar Retrieval of Soil Moisture
Jeffrey D. Ouellette, Joel Tidmore Johnson, Seung-Bum Kim, Jakob J. van Zyl, Mahta Moghaddam, Michael W. Spencer, Leung Tsang, Dara Entekhabi
Abstract
Jeffrey D. Ouellette, Joel Tidmore Johnson, Seung-Bum Kim, Jakob J. van Zyl, Mahta Moghaddam, Michael W. Spencer, Leung Tsang, Dara Entekhabi
Abstract
A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.
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A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.
Key concepts: Polarimetry, Remote sensing, Radar, Environmental science, Water content, Vegetation (pathology), Radar imaging, Computer science