High-Accuracy Cotton Field Mapping and Spatiotemporal Evolution Analysis of Continuous Cropping Using Multi-Source Remote Sensing Feature Fusion and Advanced Deep Learning

Xiao Zhang, Zenglu Liu, Xuan Li, Hao Bao, Nannan Zhang () and Tiecheng Bai ()
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Xiao Zhang: College of Information Engineering, Tarim University, Alar 843300, China
Zenglu Liu: College of Information Engineering, Tarim University, Alar 843300, China
Xuan Li: College of Information Engineering, Tarim University, Alar 843300, China
Hao Bao: College of Information Engineering, Tarim University, Alar 843300, China
Nannan Zhang: College of Information Engineering, Tarim University, Alar 843300, China
Tiecheng Bai: Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Alar 843300, China

Agriculture, 2025, vol. 15, issue 17, 1-32

Abstract: Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km 2 . By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km 2 , representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management.

Keywords: multi-source remote sensing; cotton; image fusion; random forest; U-Net; area extraction (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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