Integrating Active and Passive Microwave Satellite Data Yields More Precise and Reliable Global Soil Moisture Mapping
Jul 08, 2024
Researchers from the Aerospace Information Research Institute (AIR) at the Chinese Academy of Sciences (CAS), in partnership with international colleagues, have made a stride in mapping surface soil moisture across the globe. By combining data from two advanced satellite systems—the Soil Moisture Active Passive (SMAP) and the Advanced Scatterometer (ASCAT)—they aim to provide more precise and reliable soil moisture data.
The study was published in the journal Remote Sensing of Environment.
Soil moisture plays a key role in understanding and predicting various environmental conditions, including droughts, floods, and crop yields. Accurate monitoring of soil moisture is essential for understanding and managing agricultural dynamics, and water resource monitoring.
Traditionally, scientists have used either passive microwave measurements, which capture the natural emissions of the Earth's surface, or active microwave measurements, which involve bouncing signals off the surface and measuring the backscattering. Each method has its advantages and limitations.
By integrating passive measurements from SMAP and active measurements from ASCAT, as well as various auxiliary data that are highly related to soil moisture, the researchers have developed a more robust and accurate method to map soil moisture. They tested four machine learning models—Random Forest (RF), Long-Short Term Memory (LSTM), Support Vector Machine (SVM), and Cascaded Neural Network (CNN)—to determine the best approach. The Random Forest method proved to be the most effective.
The new method was tested against in situ measurements from different soil moisture networks worldwide. The results were impressive: the integrated data achieved an unbiased root mean squared error of 0.042 m3/m3 and a temporal correlation of 0.756. This means the new approach not only reduced errors significantly but also provided more reliable data over time compared to using SMAP or ASCAT data alone.
The study found that the integrated approach greatly improved the temporal resolution of soil moisture retrievals, providing data more frequently than the individual satellite systems. This high temporal resolution is crucial for real-time monitoring and applications in hydrology and ecology.
This new active-passive microwave retrieval algorithm offers a promising solution for generating highly accurate soil moisture data products on a global scale, which represents a major step forward in environmental monitoring.
Contact: luyq@aircas.ac.cn
News & Events