Abstract
The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.
Highlights
Soil moisture plays an important role in controlling the energy and water cycles in the land-atmosphere system [1,2,3]
Results compare with Phased Array-type L-band synthetic aperture radars (SARs) (PALSAR) observations which are sensitive to deeper soil depth
The surface moisture products are shown in green circles
Summary
Soil moisture plays an important role in controlling the energy and water cycles in the land-atmosphere system [1,2,3]. Soil moisture exhibits a high spatiotemporal variability, mainly dictated by soil texture, vegetation, meteorological forcing, and topography [4,5,6]. Due to the high variability, it is impractical to fully capture the soil moisture behavior in time and space by sparsely distributed ground-based point measurements. There has been a compelling need to quantify soil moisture with high spatial and temporal resolutions for successful hydrological, meteorological, and agricultural applications [7]. Satellite remote sensing can measure soil moisture on a large spatial scale with consistent accuracy at a constant revisit interval. Major passive remotely sensed soil moisture products, such as the Advanced
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