Tibetan Plateau Joint Inversion Of Passive Microwave Remote Sensing Of Soil Moisture

Soil moisture is an important quantity required to study partitioning of energy, runoff, radiance balance, and moisture movement in a watershed. The accurate estimation of soil moisture provides the fundament to study bio-geophysical processes in land surface. However, soil moisture in a field generally varies dramatically in space and time. The establishment of a dense ground observation network is almost unrealistic. Remote sensing especially microwave remote sensing is the most fittes for rapidly monitoring a large-scale soil moisture so far. Microwave remote sensing can work at anytime and under any weather conditions, and it also has high sensitivity to soil moisture, which makes it have been wildly used in the soil moisture study. However, microwave signal is also affected by the other landsurface parameters (e.g. surface roughness and vegetation cover of soil and soil temperature) and observation conditions (e.g. sensors) and precipitation, which leads to many problems that should be solved in the soil moisture retrieval using microwave instruments.Researches show that radar is likely to be more sensitive to the surface feature such as surface roughness and plant structure and a passive microwave radiometer is likely to be more sensitive to the near surface soil moisture. On the other hand, the active and passive sensors are not always on the same satellite platform. Therefore, researchers pay attention to how to do the soil moisture retrieval using a combined active/passive microwave observations.The primary goal of this research is to develop a retrieval approach for soil moisture using combined of AMSR-E and Quikscat/seawinds in Maqu city of Tibetan Plateau. The main contents of the study can be separated into five parts.Part one is the introduction. Described the background, significance and research status, proposed the research's content and technical routes.Part two is the basic theory of retrieving soil moisture using the active/passive microwave remote sensing observations. Included the study related knowledge, which is basic concepts, radiation and scattering model.Part three is the satellite data and study area. Introduced the study area's (Maqu city in Tibetan Plateau) geolocation, topography, land cover, precipitation, climate and ground observation network, and based on comprehensive analysis of the study area's ground data to achieve a better understanding of information; and then introduced the selected active and passive sensors (AMSR-E and Quikscat/seawinds).Part four is the analysis of the model and algorithm. Based on the study purpose, analyzing the shortcoming of the past studies's retrieval approach, taking account of the characteristics of the satellite data and study area, mainly using the model simulation method, exploring the suitable active and passive microwave models and model parameters, and then carring out the forward simulation of the active and passive observations, and then analyzing the sensitivity of the various surface parameters to active and passive observations, and in the end giving a conclusion about the improved retrieval approach. The key steps of this approach are:(1) combined with the study area information and the empirical formula for the vegetation index to estimate the effect of vegetation on the Ku-band backscattering coefficient, got a statistical regression formula for the two polarized backscattering coefficients'difference and the roughness parameters; (2) imported a newly developed Qp model instead of the traditional Q/H model, and eliminated the term of vegetation optical depth by calculating the dual polarization value's difference in the same frequency, and thus translating the radiation model formula; (3) inputted the roughness came from the backscattering coefficient to the translated radiaton model formula, and used retrieval algorithm to retrive the soil moisture. Additionally, combined the microwave remote sensing retrieval principles, elaborated two inversion algorithms, LM and EnKF.Part five is the soil moisture retrieval and the validation. Retrieved soil moisture using the proposed approach, and then compared with the ground measurements.Part six is the conclusion of the paper. Summarized this paper's achievements and shortcomings.The initiatives of this dissertation are:1 Analized the disadvantages of the past study work, and imported to the new proposed Qp model instead of the tranditional Q/H model, and then developed a modified retrieval approach using the combined active/passive microwave remote sensing observations.2 Studied the algorithm of EnKF, tried to apply it in soil moisture retrieval.