[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2664":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":66},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。",null,"Agronomy","2026-09-15T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","精准农业","遥感监测","土壤盐渍化",0,"10.3390\u002Fagronomy16181812",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":59,"direction":63,"ingested_from":65},"W7213244904",[37,39,41,43,45,47,49,51,54,56],{"name":38,"orcid":9},"Haiye Yu",{"name":40,"orcid":9},"Muyan Yu",{"name":42,"orcid":9},"Ranzhe Jiang",{"name":44,"orcid":9},"Xin Zhang",{"name":46,"orcid":9},"Zhu Guo",{"name":48,"orcid":9},"Yaohui Fu",{"name":50,"orcid":9},"Xingbang Liu",{"name":52,"orcid":53},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":55,"orcid":9},"Bingze Li",{"name":57,"orcid":58},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","农业遥感与作物表型","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","openalex","2026-09-16T23:30:29.016721Z"]