Combined Application Of Active And Passive Microwave Remote Sensing In Monitoring Soil Moisture

As an important component of the farmland ecosystem, soil moisture is one of the essential requirements of crop growth. It also plays a unique role in the chemical, biological and physical processes within soil that influences the soil fertility. In agricultural science, soil moisture is a vital index of crop water stress, drought supervision and yield estimation researches. Therefore, soil moisture monitoring has significant value for the water cycle law research, water resource utilization, field irrigation and drought forecast, etc. Monitoring soil moisture by remote sensing can be considered as macroscopically, efficiently and economically. Compared with optical and infrared remote sensing, microwave method has obvious superiorities in soil moisture estimation. Active and passive microwave remote sensing are advantage complementary. It implies that the combined application of them would contribute to the maximization of their strengths and therefore simplify the process of soil moisture monitoring and improve the accuracy of soil moisture estimation, especially in the vegetation covered area.In most cases passive microwave remote sensing is used to retrieve soil moisture content while active microwave remote sensing is used to estimate soil moisture variation, which is decided by their characteristics. The effect of surface roughness and vegetation cover must be considered and managed to eliminate in soil moisture estimation no matter to the active or passive remote sensing. This dissertation devotes to the combined application of active and passive remote sensing in soil moisture monitoring for the vegetation covered area with the support of SMEX02PALS data. On the research and analysis of the soil moisture content retrieve method using PALS radiometer data and the soil moisture variation estimate method using PALS radar data, soil moisture monitoring method using the combined application of active and passive remote sensing is proposed. At last, the new method is applied and verified by using the SMEX02PALS and in situ experimental data. The results and innovations are mainly in the following aspects:(1) On the settings of PALS radiometer and conditions of SMEX02test region, a simulated radiation database is built by using the AIEM model. The applicability of the parameterized surface model Qp model and the soil moisture retrieve method developed on in the soil texture conditions of SMEX02test region and parameter settings of PALS radiometer it is tested by analyzing the database. With the " ω-τ " model to describe the effect of vegetation, the Qp model and the soil moisture retrieve method developed from the C-band data of AMSR-E are successfully applied to the L-band data of PALS radiometer.(2) The relationship between L-band (1.26GHz) backscattering coefficients and Fresnel reflectivities on the soil surface in different roughness conditions is simulated by using the AIEM model on the settings of PALS radar. The results show that the relationship can be fitted by a zero axial straight line, and a linear relationship also exists between the soil moisture variation (specific value) and the corresponding Fresnel reflectivity variation. The contact between backscattering coefficients variation and soil moisture variation is established through the Fresnel reflectivity. Therefore, the soil moisture variation estimate method developed from the L-band (1.2GHz) data of AirSAR is also successfully applied to the L-band data of PALS radar.(3) On the foundation of researching and analyzing of the soil moisture content retrieve and variation estimate method using passive and active microwave remote sensing respectively, the soil moisture monitoring method using the combined application of active and passive remote sensing is proposed. The strengths are maximized by the combination of them. Estimation of soil moisture and variation monitoring in the vegetation covered area can be done without massive ancillary data. The combined method propose in this dissertation not only simplifies the process of soil moisture monitoring but also improves it's accuracy, which provides a new idea for the combination of active and passive remote sensing.