Abstract
A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.
Highlights
The systematic and timely monitoring of land surface parameters that affect the hydrological cycle at a variety of spatial scales is of great importance in gaining a better understanding of geophysical processes and for the management of environmental resources and natural disasters
A comparison on a global scale was performed and the resulting soil moisture content (SMC) maps were in general agreement with the climatic and meteorological conditions of the different areas
Single Channel Algorithm (SCA) can be applied using either frequency (C or X); the X-band channel was preferred here in order to minimize the impact of RFI, which is present in the C-band observations
Summary
The systematic and timely monitoring of land surface parameters that affect the hydrological cycle at a variety of spatial scales is of great importance in gaining a better understanding of geophysical processes and for the management of environmental resources and natural disasters. A comparison on a global scale was performed and the resulting SMC maps were in general agreement with the climatic and meteorological conditions of the different areas. HydroAlgo has the advantage of not being dependent on dynamic ancillary data.
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