Soil Moisture Retrieval Based On Improved Change Detection Method Using Sentinel-1 Data

Soil moisture is an important basic information in agricultural production, as it directly affects the growth and development of crops and can also reflect the soil water status, playing an important role in preventing natural disasters such as droughts and floods. Using change detection methods for soil moisture inversion is an effective technical means that can monitor soil moisture changes in real-time and with high accuracy, improving the accuracy and timeliness of soil moisture monitoring. Traditional change detection methods only consider the impact of vegetation on soil moisture, ignoring the impact of temperature, which reduces the accuracy of soil moisture inversion. This paper proposes an improved method that adds consideration to environmental temperature. Related research has shown that compared with traditional soil moisture inversion methods, the improved method improves the prediction accuracy of soil moisture. Compared with the methods proposed by Zribi and Wagner, the accuracy of curve fitting has been significantly improved. Therefore, taking into account the factor of temperature can enhance the precision of soil moisture inversion.