[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2660":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":22,"tags":24,"view_count":30,"doi":31,"paper":32,"created_at":62},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。",null,"Remote Sensing","2026-09-16T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,22,14,1,"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","遥感","作物监测","植被指数",0,"10.3390\u002Frs18183180",{"doi":31,"openalex_id":33,"authors":34,"venue":10,"cited_by_count":30,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213344870",[35,38,40,43,46,49,52],{"name":36,"orcid":37},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":39,"orcid":9},"Nikolas Hoskin",{"name":41,"orcid":42},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":44,"orcid":45},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":47,"orcid":48},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":50,"orcid":51},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":53,"orcid":54},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","农业遥感与作物表型","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","openalex","2026-09-16T23:30:28.719858Z"]