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An experiment of measuring soil moisture was carried out by using dual-frequency microwave radiometer designed by the authors. The measured data were analyzed by using statistical regression method and the empirical regression model of retrieving soil moisture in L-band and C-band was developed. The soil moisture in a rainfall event was retrieved using the experiential regression model, which is consistent well with the field sampling value. The results show that when soil moisture is lower than 75%, the brightness temperature is linear with soil moisture. However, when soil moisture is higher than 75%, the brightness temperature is not linear with soil moisture, so it is difficult for microwave radiometer to measure the changes of soil moisture. The experiment verifies the effectiveness and feasibility of microwave remote sensing soil moisture. Although this method for linear regression based on the data measured with the radiometer is simple, and has strong adaptability, generally it has only local application value, and lacks universal applicability for different areas and different conditions.
An experiment of measuring soil moisture was carried out by using dual-frequency microwave radiometer designed by the authors. The measured data were analyzed by using statistical regression method and the empirical regression model of retrieving soil moisture in L-band and C-band was developed . The soil moisture in a rainfall event was retrieved using the experiential regression model, which is consistent well with the field sampling value. The results show that when soil moisture is lower than 75%, the brightness temperature is linear with soil moisture. However, when soil moisture is higher than 75%, the brightness temperature is not linear with soil moisture, so it is difficult for microwave radiometer to measure the changes of soil moisture. The experiment verifies the effectiveness and feasibility of microwave remote sensing soil moisture. method for linear regression based on the data measured with the radiometer is simple, and has strong adaptability, generally it has only local application value, and lacks universal applicability for different areas and different conditions.