Study on Rapid Inversion of Soil Water Content from Ground-Penetrating Radar Data Based on Deep Learning

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 7; p. 1906
• Main Authors: Li, Zhilian, Zeng, Zhaofa, Xiong, Hongqiang, Lu, Qi, An, Baizhou, Yan, Jiahe, Li, Risheng, Xia, Longfei, Wang, Haoyu, Liu, Kexin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Agricultural land; Agricultural production; Agricultural sciences; Antennas; Deep learning; Dielectric properties; Earth sciences; farmland; Geophysical methods; Ground penetrating radar; Moisture content; Neural networks; Nondestructive testing; Radar; Radar data; Remote sensing; Rice; Soil moisture; Soil water; Velocity; volumetric soil water content; Water content
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15071906

Ground-penetrating radar (GPR) is an efficient and nondestructive geophysical method with great potential for detecting soil water content at the farmland scale. However, a key challenge in soil detection is obtaining soil water content rapidly and in real-time. In recent years, deep learning methods have become more widespread in the earth sciences, making it possible to use them for soil water content inversion from GPR data. In this paper, we propose a neural network framework GPRSW based on deep learning of GPR data. GPRSW is an end-to-end network that directly inverts volumetric soil water content (VSWC) through single-channel GPR data. Synthetic experiments show that GPRSW accurately identifies different VSWC boundaries in the model in time depth. The predicted VSWC and model fit well within 40 ns, with a maximum error after 40 ns of less than 0.10 cm3 × cm−3. To validate our method, we conducted GPR measurements at the experimental field of the Academy of Agricultural Sciences in Gongzhuling City, Jilin Province and applied GPRSW to VSWC measurements. The results show that predicted values of GPRSW match with field soil samples and are consistent with the overall trend of the TDR soil probe samples, with a maximum difference not exceeding 0.03 cm3 × cm−3. Therefore, our study shows that GPRSW has the potential to be applied to obtain soil water content from GPR data on farmland.