Soil moisture is one of the main factors in the water, energy and carbon cycles. It constitutes a major uncertainty in
climate and hydrological models. By now, passive microwave remote sensing and thermal infrared remote sensing
technology have been used to obtain and monitor soil moisture. However, as the resolution of passive microwave remote
sensing is very low and the thermal infrared remote sensing method fails to provide soil temperature on cloudy days, it is
hard to monitor the soil moisture accurately. To solve the problem, a new method has been tried in this research. Thermal
infrared remote sensing and passive microwave remote sensing technology have been combined based on the delicate
experiment. Since the soil moisture retrieved by passive microwave in general represents surface centimeters deep, which
is different from deeper soil moisture estimated by thermal inertia method, a relationship between the two depths soil
moisture has been established based on the experiment. The results show that there is a good relationship between the soil
moisture estimated by passive microwave and thermal infrared remote sensing method. The correlation coefficient is 0.78
and RMSE (root mean square error) is 0.0195 · . This research provides a new possible method to inverse soil
moisture.
Soil moisture is an important parameter in hydrological circulation. For the microwave signal at L-band is very sensitive
to the soil moisture, there have been many algorithms to retrieve soil moisture at L-band. The Soil Moisture and Ocean
Salinity (SMOS) mission is launched in 2009, and the surface soil moisture retrieving is based on the inversion of the Lband
Microwave Emission of the Biosphere (L-MEB) radiative transfer model. Due to the heterogeneity of the surface,
the capability of the model remains to be verified in some region. In the study, the brightness temperature at L-band in
Heihe River Basin is simulated by using the τ-ω model firstly. Secondly, the sensitivity analysis of the model on the
parameters is conducted to get the optimal results. At last, the simulated brightness temperature is calculated by using the
adjusted parameters, and the PLMR microwave brightness temperature is used to validate the simulation results. It turns
out that the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to
8K at H-polarization respectively at different angles, which proves the L-MEB model have an good capability in the of
China.
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