基于多源数据融合的大豆产量估测方法研究

doi:10.12133/j.smartag.SA202512004

摘要/Abstract

摘要：

【目的/意义】 单一遥感平台难以兼顾大豆产量估算所需的精细空间细节与区域覆盖广度，为解决无人机观测范围受限，卫星影像又受云污染与重访周期制约的问题，将无人机与Sentinel-2数据进行跨平台融合，建立兼顾精度与尺度的估产方案。 【方法】 以黑龙江尖山农场为试验区，获取2022—2023年大豆全生育期（5—9月）的127景Sentinel-2卫星影像和47景无人机影像。采用萨维茨基-戈莱滤波对两类数据进行半月、月度尺度合成，缓解时相错位与云污染问题；设计三种空间特征融合策略，并与三种改进卷积神经网耦合，在时序多特征数据集上逐次训练、验证。 【结果和讨论】 循环网络融合表现最优：半月至尺度下CNN-LSTM的R²达到0.93；引入空间注意力与门控循环单元后，SA-GRU-CNN模型将R²进一步提升至0.94，且2023年独立验证仍保持0.93，说明结果跨年份稳定。消融实验表明，萨维茨基-戈莱时序重建与GRU时序建模对精度提升贡献最大。 【结论】 无人机-Sentinel-2融合可将区域大豆产量估算精度提升，所提融合策略与网络结构为大面积作物产量监测提供了可复用的技术路径。

关键词: 无人机, Sentinel-2, 多源数据融合, 产量, 大豆

Abstract:

[Objective] The aim is to develop a high-precision, large-scale soybean yield estimation framework by integrating multi-source remote sensing data, addressing the critical need for accurate and timely crop production monitoring, and to support precision agriculture and food security decision-making at both field and regional scales. [Methods] Based on multi-source data fusion theory, a soybean yield precision prediction method integrating Unmanned Aerial Vehicle (UAV) and Sentinel-2 multi-source data was proposed. Jianshan Farm was chosen as the study area, and a total of 127 Sentinel-2 images and 47 UAV images covering the whole soybean growth period (May-September) in 2022-2023 were collected. According to the image acquisition time, the Savitzky-Golay filter was adopted to construct half-monthly and monthly synthetic images for UAV and Sentinel-2 data, which guaranteed temporal consistency of multi-source data and reduced the influence of cloud contamination. Meanwhile, three multi-scale spatial feature fusion strategies for UAV and satellite imagery were designed, and three improved Convolutional Neural Network models were constructed by coupling the above fusion methods. These models were subsequently trained and evaluated on the constructed time-series multi-feature datasets. [Results and Discussions] The Long Short-Term Memory-based fusion method obtained the optimal accuracy with R² of 0.93 among three spatial feature fusion schemes. Furthermore, the Spatial Attention-Gated Recurrent Unit-Convolutional Neural Network model exhibited the best soybean yield estimation capability, with its R² reaching 0.94. [Conclusions] The UAV-Sentinel-2 multi-source data fusion framework can effectively improve the accuracy of regional soybean yield estimation, and the optimized fusion algorithm and improved model in this study can provide a reliable technical reference for large-area and high-precision crop yield monitoring.

Key words: UAV, Sentinel-2, multi-source data fusion, yield, soybean

中图分类号:

S127

尹祈玮, 贺燕, 王宗莉, 饶元. 基于多源数据融合的大豆产量估测方法研究[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202512004.

YIN Qiwei, HE Yan, WANG Zongli, RAO Yuan. Soybean Yield Estimation Method Based on Multi-Source Data Fusion[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202512004.

图/表 23

参考文献 32

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Band number	Band	Central wavelength/μm	Bandwidth/nm	Spatial resolution/m
1	Coastal aerosol	0.443	20	60
2	Blue	0.490	65	10
3	Green	0.560	35	10
4	Red	0.665	30	10
5	Vegetation red edge	0.705	15	20
6	Vegetation red edge	0.740	15	20
7	Vegetation red edge	0.783	20	20
8	Near Infrared	0.842	115	10
8A	Vegetation red edge	0.865	20	20
9	Water vapour	0.945	20	60
10	Short-Wave Infrared -Cirrus	1.375	20	60
11	SWIR	1.610	90	20

Texture feature parameters	Abbreviation	Central band/μm
Mean texture	Mean	Describe the brightness and darkness of an image
Homogeneity texture	Hom	Evaluate the local grayscale uniformity of the image
Dissimilarity texture	Dis	Characterize the differences in texture features between pixels
Entropy texture	Ent	Indicate the size of the amount of information
Second Moment texture	Sm	Describe the uniformity and thickness of texture features
Correlation texture	Corr	Predict the main trend of texture

Model	MAE/（kg/hm²）	RMSE/（kg/hm²）	MAPE	R ²
CNN-UAV-15	22.294 1	30.850 5	0.106 6	0.39
CNN-Sentinel-15	26.461 6	32.930 7	0.111 6	0.32
CNN-US-15	21.827 5	28.974 6	0.095 7	0.44
CNN-UAV-30	24.895 8	31.450 9	0.105 8	0.37
CNN-Sentinel-30	27.005 5	34.359 5	0.120 5	0.21
CNN-US-30	22.177 5	29.737 7	0.096 8	0.40

Model	MAE/（kg/hm²）	RMSE/（kg/hm²）	MAPE	R ²
CNN-US-15	21.827 5	28.974 6	0.095 7	0.44
CNN-TCA-15	15.172 2	18.633 1	0.064 3	0.88
CNN-GRU-15	12.526 8	17.254 5	0.055 5	0.90
CNN-LSTM-15	10.093 1	14.137 4	0.045 0	0.93
CNN-US-30	22.177 5	29.737 7	0.096 8	0.40
CNN-TCA-30	21.568 8	27.953 3	0.092 6	0.71
CNN-GRU-30	19.322 2	26.188 7	0.083 2	0.79
CNN-LSTM-30	18.030 7	25.131 2	0.080 7	0.80

Model	MAE/（kg/hm²）	RMSE/（kg/hm²）	MAPE	R ²
SVM-US-CNN	42.304 6	55.776 7	0.191 5	0.38
SVM-TCA-CNN	35.184 3	45.623 3	0.168 3	0.66
SVM-GRU-CNN	31.577 1	38.886 2	0.153 2	0.73
SVM-LSTM-CNN	26.336 9	32.266 6	0.132 6	0.80
SA-US-CNN	10.730 5	14.740 8	0.050 4	0.85
SA-TCA-CNN	9.488 0	13.941 9	0.048 9	0.87
SA-GRU-CNN	8.403 8	12.283 8	0.037 8	0.94
SA-LSTM-CNN	10.219 8	14.569 3	0.0454	0.91
SE-US-CNN	14.983 6	12.230 6	0.104 2	0.70
SE-TCA-CNN	11.693 5	13.983 2	0.0963	0.71
SE-GRU-CNN	11.707 1	12.191 7	0.053 0	0.90
SE-LSTM-CNN	8.520 9	12.609 2	0.036 8	0.92