A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery

Ilyas Nurmemet (), Yang Xiang, Aihepa Aihaiti, Yu Qin, Yilizhati Aili, Hengrui Tang and Ling Li
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Ilyas Nurmemet: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yang Xiang: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Aihepa Aihaiti: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yu Qin: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yilizhati Aili: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Hengrui Tang: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Ling Li: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

Agriculture, 2025, vol. 15, issue 13, 1-31

Abstract: Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks.

Keywords: soil salinization mapping; GF-3; polarimetric decomposition; deep learning; self-attention; CBAM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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