Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

Author:

Xu Chongbin1,Liu Qingli2,Wang Yinglin2,Chen Qian1,Sun Xiaomin1,Zhao He1,Zhao Jianhui2ORCID,Li Ning2ORCID

Affiliation:

1. Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China

2. College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

Abstract

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Funder

National Key Research and Development Program of China

Plan of Science and Technology of Henan Province

Publisher

MDPI AG

Reference52 articles.

1. Potential of a two-component polarimetric decomposition at C-band for soil moisture retrieval over agricultural fields;Wang;Remote Sens. Environ.,2018

2. A polarimetric decomposition and copula quantile regression approach for soil moisture estimation from Radarsat-2 data over vegetated areas;Zhang;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2023

3. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products;Brocca;Int. J. Appl. Earth Obs. Geoinf.,2018

4. Detecting global irrigated areas by using satellite and reanalysis products;Zohaib;Sci. Total Environ.,2019

5. Satellite-based global-scale irrigation water use and its contemporary trends;Zohaib;Sci. Total Environ.,2020

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