ANNOUNCEMENTS
Accurate monitoring of soil moisture at a high spatial resolution is of utmost importance in hydrology, agriculture, and land management. However, it remains a challenging task that requires cost-effective and reliable approaches. In this study, a novel methodology is proposed to estimate soil moisture by integrating advanced machine learning (ML) models with multi-sensor data, including Sentinel 2, Modis, Chirps, and SMAP data. Linear regression ,Support Vector machine (SVM) and random forest (RF) algorithms are employed for soil moisture retrieval.
The objective of this approach is to enhance the accuracy and robustness of soil moisture prediction by integrating data from multiple sensors. These sensors provide complementary information on various land surface characteristics, vegetation dynamics, rainfall patterns, and satellite-based soil moisture measurements. By conducting a comprehensive analysis, a holistic understanding of soil moisture dynamics is achieved.To capture complex relationships and nonlinear patterns in soil moisture data, the study utilizes suitable ML algorithms such as linear regression, random forest, support vector machine, and artificial neural network. These algorithms offer the potential to improve the accuracy of soil moisture retrieval compared to traditional methods.
This research project contributes to the advancement of soil moisture monitoring by presenting a robust and cost-effective methodology that leverages the power of machine learning and integrates data from diverse sensors. The performance of different ML algorithms is assessed and compared to determine the most effective approach for high-resolution soil moisture prediction.
In conclusion, this study addresses the challenges associated with spatial soil moisture monitoring and proposes a promising methodology that has the potential to revolutionize soil moisture prediction in hydrology, agriculture, forestry, and land management applications. By employing advanced ML techniques and multi-sensor data integration, this research offers valuable insights into improving the accuracy and reliability of soil moisture estimation.
