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Parcel-scale summer crop classification based on multi-source remote sensing data and deep learning
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AI
DOI：10.1080/01431161.2026.2612819.png)
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Abstract
En 中文
Accurate crop structure information is fundamental for precision agriculture, particularly in regions with highly fragmented farmland. This study focuses on Jiangsu Province, China, and proposes a multi-source remote sensing approach to address the classification challenges posed by complex agricultural landscapes. Optical Sentinel-2, synthetic aperture radar (SAR) Sentinel-1, and high-resolution Gaofen-2 (GF-2) imagery were integrated to exploit their spatial and temporal complementarities. A modified multi-scale attention U-Net (MSA-UNet) was developed to extract cropland parcels from GF-2 imagery, which served as the basic classification units. Time-series spectral and polarimetric features derived from Sentinel-1/2 data were used to train a convolutional neural network – bidirectional long short-term memory (CNN – BiLSTM) model for parcel-level summer crop classification. The proposed MSA-UNet achieved an intersection over union (IoU) of 73.89% and an F1-score of 84.66%, outperforming the baseline U-Net by 5.87 and 4.07% points, respectively. Incorporating both optical and SAR features improved the precision to 91.28%, representing gains of 2.37 and 13.87% points compared to models using single-source inputs. The CNN – BiLSTM architecture effectively captured both intra-seasonal dynamics and long-term dependencies in the time series. These results demonstrate that the proposed method significantly improves the accuracy of crop mapping in fragmented agricultural regions and offers a practical solution for large-scale agricultural monitoring and decision support.
Keywords:
Sentinel-1/2
GF-2
plot scale
crop classification
MSA-UNet model
CNN-BiLSTM model
Journal
IF:
2.6
Papers:
1.2W
Citations:
2.7W
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