智慧农业（中英文）2025，Vol.7Issue(6)：136-148,13.DOI:10.12133/j.smartag.SA202508025
轻量可解释的大豆遥感识别模型构建与评估
Construction and Evaluation of Lightweight and Interpretable Soybean Remote Sensing Identification Model
摘要
Abstract
[Objective]Soybean stands as one of the most crucial global crops,serving as a vital source of plant-based protein and vege-table oil while playing an indispensable role in sustainable agricultural systems and global food security.Accurate and timely mapping of soybean cultivation areas is essential for agricultural monitoring,policy-making,and precision farming.However,existing remote sensing methods for soybean identification,such as threshold-based approaches,traditional machine learning,and deep learning,often face challenges related to model complexity,computational efficiency,and interpretability.These limitations collectively highlight the pressing need for a methodological solution that maintains classification accuracy while simultaneously offering computational effi-ciency,operational simplicity,and interpretable results,a balance crucial for effective agricultural monitoring and policy-making.To address these limitations,a lightweight and interpretable soybean mapping framework was proposed based on Sentinel-2 imagery and a binary logistic regression model in this method.[Methods]Six representative agricultural regions within the primary U.S.soybean production belt were selected to capture the diversity of cultivation practices and environmental conditions across this major produc-tion area.The analysis utilized the complete growing season(April-October)Sentinel-2 satellite imagery from 2021 to 2023.The USDA's cropland data layer served as reference data for model training and validation,benefiting from its extensive ground verifica-tion and statistical rigor.All Sentinel-2 images undergo rigorous preprocessing,including atmospheric correction,cloud and shadow masking with the scene classification layer,and spatial subsetting to the regions of interest.The Jeffries-Matusita distance was em-ployed as a quantitative metric to objectively identify the optimal temporal window for soybean discrimination.This statistical mea-sure evaluated the separability between soybean and other major crops across the growing season,with calculations performed on 10 d composite periods to ensure data quality and temporal consistency.The analysis revealed that late July to mid-September(Day of Year 210-260)provided maximum spectral separability,corresponding to the soybean's critical reproductive stages(pod setting and fill-ing)when its spectral signature becomes most distinct from other crops,particularly in short-wave infrared regions sensitive to canopy structure and water content.Within this optimally identified window,a binary logistic regression model was implemented that treated soybean identification as a probabilistic classification problem.The model was trained using spectral features from the optimal period through maximum likelihood estimation,creating a computationally efficient framework that required optimization of only a limited number of parameters while maintaining physical interpretability through explicit feature coefficients.[Results and Discussions]The comprehensive evaluation showed that the integrated approach balanced classification performance and operational practicality opti-mally.The temporal optimization identified late July to mid-September as the peak discriminative period,which matches soybean's re-productive phenological stages(when its canopy spectral characteristics differ most from other crops).This finding was consistent across three study years and multiple regions,verifying the robustness of the data-driven window selection.The binary logistic regres-sion model,trained on features from this optimal period,performed excellently:In the 2022 model construction region,it achieved 0.90 overall accuracy and 0.79 Kappa coefficient.When applied to independent validation regions in the same year,it maintained strong performance(0.88 overall accuracy,0.76 Kappa)without region-specific parameter adjustments,demonstrating outstanding spatial transferability.Temporal validation further confirmed the model's robustness:Across the 2021 to 2023 study period,it main-tained consistent performance across all regions,with an average accuracy of 0.87 and Kappa of 0.76.This inter-annual stability is no-table,despite potential variations in annual weather,management practices,and planting schedules,and highlights the advantage of basing the model on a stable phenological period rather than fixed calendar dates.The model's lightweight architecture offered practi-cal benefits:Compared with complex ensemble or deep learning methods,it only requires optimizing a limited number of parameters.This parsimonious structure enhances computational efficiency,enabling rapid training and deployment over large areas while reduc-ing reliance on extensive labeled datasets—a key advantage in regions lacking sufficient ground truth data.Beyond accuracy and effi-ciency,the model exhibited exceptional interpretability via its probabilistic framework and transparent feature weighting.Coefficient analysis provided quantifiable insights into feature contributions,revealing that short-wave infrared bands and specific vegetation indi-ces had the highest discriminative power during the optimal temporal window.[Conclusions]An effective soybean mapping approach that balances accuracy with operational practicality through the strategic combination of temporal optimization and binary logistic re-gression was proposed.The method offers a viable solution for operational agricultural monitoring,especially in resource-constrained environments.Future work can enhance the robustness of the model across multiple regional conditions through cross-regional valida-tion in different climate zones and cropping systems,or by integrating transfer learning with domain adaptation methods.This will im-prove its potential for global-scale application.Concurrently,integrating additional data,methodologies,and models to achieve end-to-end feature learning should be considered.关键词
Sentinel-2/二元Logistic模型/大豆/制图/遥感/作物识别/轻量化Key words
Sentinel-2/binary Logistic model/soybean/mapping/remote sensing/crop identification/ligthweight分类
信息技术与安全科学引用本文复制引用
WANG Yinhui,ZHAO Anzhou,LI Dan,ZHU Xiufang,ZHAO Jun,WANG Ziqing..轻量可解释的大豆遥感识别模型构建与评估[J].智慧农业（中英文）,2025,7(6):136-148,13.基金项目
国家重点研发计划(2023YFB3906201) National Key R&D Program of China(2023YFB3906201) （2023YFB3906201）
