| Soil moisture constitutes only a rather small part of the global water resources (about0.00122%), but it plays a key role in the global water and energy cycle. It has an important function in controlling the water and energy fluxes exchange between land and atmosphere. Soil moisture and snow cover constitute the most important meteorological memory for the climate system over land. And soil moisture is one of the most important parameters in the climate change, which is only next to the effect of ocean temperature. It also has influence on the rate of water absorption by vegetation, drainage, ground stability, and near-surface climate. As a result, knowledge of the soil moisture state and its spatial and temporal dynamics is therefore urgent essential for a wide range of disciplines and applications concerned with weather and climate, early warning of drought, flood control, irrigation scheduling, soil erosion and slope failure, crop yield forecasting, reservoir management, and water quality.Compared to the traditional in-situ soil moisture measurement which only offer the soil moisture information on one point, remote sensing methods have the advantage of performing an automatic averaging over entire area (or footprint). As remote sensing methods are much cheaper than traditional techniques, monitoring soil moisture information in continuous long-term way for large may come true by remote sensing methods. And Microwave retrieval methods are brought to the forefront for the advantages of slight influence by cloud and climate. Based on National Program on Key Basic Research Project (973Program) sub-project "Knowledge-based comprehensive remote sensing research on retrieving soil moisture and vegetation structure parameters", the paper explores the soil moisture retrieving methods for ASCAT instrument.The main contents of this work are as follows:1) Retrieve soil surface moisture from ASCAT data with SSM. The validation of the SSM model has already been proved for the old scatterometer instrument on board the ERS series satellite. For the first time, the error between the SSM and AIEM model is analyzed under different model parameter constrain conditions for ASCAT instrument. Simulation experiments show that SSM is identical with AIEM. SSM is feasible for ASCAT in retrieving soil moisture. Then, the experiment is taken place in Maqu area. Bare soil moisture is retrieved from ASCAT data with SSM. The experiments confirm the applicability of SSM for ASCAT.2) Some improvements are made for the classic TU-WIEN algorithm. TU-WIEN takes full advantages of the multi-viewing capabilities of the sensor, the availability of several years of backscatter data and high temporal sampling rate. Taking the roughness, inhomogeneity and vegetation cover of land surface into account, soil moisture is retrieved by analyzing long time mass data with statistical techniques. However, there is still some weakness in the algorithm. Two improvements are proposed in this paper:①Instead of experience functions in the old version, key model parameter s’(θ,t) is generated by regressing data in a moving time moving window. So the method could perform more precise and be transplant to different areas.①Besides, abnormal observations are removed from the long-term mass data to avoid fatal damage for the dry/wet references which are decisive factor for final output. The experiment shows that the improved algorithm is more stable and more regional transplantable. At lase, the retrieval result from ASCAT is fused with AMSR-E soil moisture product for absolute soil water content.3) Combining the advantages of the SSM and TU-WIEN algorithm, a novel soil moisture retrieval method for ASCAT instrument is proposed in this paper. The advantages of the novel method are as follows:①Make better use of the multi-viewing capabilities of the sensor. Observations are sorted into2groups by antennas, observations from fore and behind antennas are one group, observations from middle antenna are the other one.2sets of model parameters a’(40,t)>、σ"(40,t)'σ’(50,t)、σ"(50) are calculated independently and observations are normalized into2different reference angles (middle antenna int40°, fore and behind into50°)②Take full advantages of long time, high temporal sampling rate, continues observations. Regressing both model parameters and water content from mass data in the moving time window. The introduction of statistical techniques improved the stability of and accuracy of the retrieval result.③By introducing SSM, absolute water content is retrieved from equations to preclude the defect of TU-WIEN. Experiment in Maqu shows that, there is a good consistency between the retrieval result by the novel method and in-situ observations. At last, the novel method is applied in Tibet Plateau. Soil moisture maps in the whole Tibet Plateau region are produced for each month from January2008to December2011. And, time and special analysis in the aspects of climate and hydrology are made from these materials. |
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