[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1532":3,"related-1532":116},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":83,"slug":86,"view_count":21,"doi":87,"paper":88,"created_at":115},1532,"Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727176","Crop residue cover (CRC) is a critical parameter for evaluating conservation tillage practices, mitigating soil erosion, and developing models for global carbon cycle monitoring. Traditional optical remote sensing index-based methods are significantly affected by variations in soil and crop residue moisture content as well as interference from emerging subsequent crops, resulting in low estimation accuracy and poor stability of CRC. Due to the presence of subsequent crops, the non-photosynthetic vegetation fraction (fNPV) derived from linear spectral mixture analysis is typically lower than the actual field CRC. This study proposes a novel CRC estimation method based on linear spectral mixture analysis. The workflow consists of three main components: (1) development of a Crop Residue Random Forest (CRRF) index using a random forest regression model to simulate the narrowband SINDRI index – which is less sensitive to moisture – from broadband multispectral reflectance; (2) construction of a two-dimensional triangular feature space combining the CRRF and NDVI indices, followed by linear spectral mixture analysis to simultaneously estimate field fNPV, photosynthetic vegetation fraction (fPV), and bare soil fraction (fBS); and (3) retrieval of the true CRC fraction at the sowing stage using the formula CRC = fNPV \u002F (1 − fPV). The proposed method was validated and applied regionally using multi-temporal Sentinel-2 MSI and MODIS imagery. Results demonstrate that: (1) the method effectively mitigates moisture interference and achieves high-accuracy fNPV estimation (R2 = 0.84, RMSE = 0.10); (2) the integration of Sentinel-2 and MODIS sensors enables stable estimation and spatio-temporal dynamic analysis of CRC across the North China Plain, with high temporal consistency between images acquired approximately 16 days apart (the majority of agricultural pixels showing absolute differences within ±0.15). This approach provides an efficient and robust technical solution for remote sensing monitoring of CRC fraction in complex agricultural environments and offers a valuable methodological reference for the assessment of conservation tillage practices.","作物残茬覆盖度（CRC）是评估保护性耕作措施、减缓土壤侵蚀以及构建全球碳循环监测模型的关键参数。传统光学遥感指数方法受土壤和作物残茬含水量变化及新生后茬作物干扰的影响显著，导致CRC估算精度低、稳定性差。由于后茬作物的存在，基于线性光谱混合分析提取的非光合植被覆盖度（fNPV）通常低于田间实际CRC值。本研究提出了一种基于线性光谱混合分析的CRC估算新方法。该方法流程主要包括三个部分：（1）利用随机森林回归模型模拟对水分敏感性较低的窄带SINDRI指数，进而构建作物残茬随机森林（CRRF）指数，该指数可由宽带多光谱反射率计算得到；（2）结合CRRF与NDVI指数构建二维三角特征空间，通过线性光谱混合分析同时估算田间fNPV、光合植被覆盖度（fPV）和裸土覆盖度（fBS）；（3）利用公式CRC = fNPV \u002F (1 − fPV)反演播种期真实CRC值。该方法利用多时相Sentinel-2 MSI和MODIS影像进行了验证及区域应用。结果表明：（1）该方法能有效削弱水分干扰，实现高精度fNPV估算（R² = 0.84，RMSE = 0.10）；（2）Sentinel-2与MODIS传感器的联合应用可实现华北平原CRC的稳定估算及时空动态分析，且间隔约16天获取的影像间具有较高时间一致性（绝大多数农业像元的绝对差异在±0.15以内）。该方法为复杂农业环境下CRC覆盖度的遥感监测提供了高效、稳健的技术方案，也为保护性耕作措施评估提供了有价值的方法参考。",null,"International Journal of Remote Sensing","2026-09-02T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,13,7,1,"提出基于随机森林与线性光谱混合分析的秸秆覆盖度估算新方法，在华北平原多源遥感数据上验证有效，对保护性耕作监测有方法论参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业遥感","机器学习","保护性耕作","秸秆覆盖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,58,64,72,78],{"code":32,"score":50,"supported":51,"title":52,"abstract":55,"tags":57},26,true,[53,54],"remote sensing","multispectral",[56,53,54],"遥感",[27],{"code":39,"score":59,"supported":51,"title":60,"abstract":62,"tags":63},9,[61],"machine learning",[],[28],{"code":65,"score":66,"supported":51,"title":67,"abstract":68,"tags":71},"H2",6,[],[69,70],"土壤","soil",[],{"code":73,"score":74,"supported":51,"title":75,"abstract":76,"tags":77},"H8",4,[],[29],[29],{"code":79,"score":21,"supported":14,"title":80,"abstract":81,"tags":82},"I1",[],[],[26],[84,85],"保护性耕作 农业遥感 智慧农业 机器学习","保护性耕作 农业遥感","保护性耕作农业遥感智慧农业机器学习-1532","10.1080\u002F01431161.2026.2727176",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":107,"oa_url":9,"card":108,"direction":112,"ingested_from":114},"W7205007595",[91,94,97,99,102,105],{"name":92,"orcid":93},"Jibo Yue","https:\u002F\u002Forcid.org\u002F0000-0001-9766-5313",{"name":95,"orcid":96},"Ranran Yang","https:\u002F\u002Forcid.org\u002F0000-0003-1937-2042",{"name":98,"orcid":9},"Yinghao Lin",{"name":100,"orcid":101},"Nianxu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-0152-8750",{"name":103,"orcid":104},"Qingjiu Tian","https:\u002F\u002Forcid.org\u002F0000-0003-0986-6479",{"name":106,"orcid":9},"Jia Tian",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。","随机森林模拟窄带SINDRI，结合NDVI构建三角特征空间，线性光谱混合分析估算","方法有效缓解水分干扰，fNPV估算R²=0.84，RMSE=0.10，多源影像时间一致性高。","农业遥感与作物表型","可探索将方法扩展至其他作物类型或区域，或结合高光谱影像提高精度，并研究残茬覆盖度与土壤碳动态的关系。","openalex","2026-09-03T23:30:30.109393Z",{"total":66,"page":21,"page_size":66,"items":117},[118,214,286,367,462,532],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":141,"classification":144,"search_phrases":167,"slug":170,"view_count":128,"doi":171,"paper":172,"created_at":213},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":128,"substance":17,"depth":129,"authority":19,"freshness":59,"relevant":21,"comment":130},8,16,"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[132],{"name":124,"url":121},[26,27,134,28,135],"水稻","高光谱遥感",{"code":137,"slug":138,"name":134,"path":139,"parent_code":140},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[142,143],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":145},{"candidates":146,"agricultural_evidence":51},[147,153,158,163],{"code":32,"score":148,"supported":51,"title":149,"abstract":151,"tags":152},27,[53,150],"hyperspectral",[56,53,150],[27,135],{"code":39,"score":154,"supported":51,"title":155,"abstract":156,"tags":157},15,[61],[28,61],[28],{"code":137,"score":159,"supported":51,"title":160,"abstract":161,"tags":162},12,[138],[138],[134],{"code":79,"score":21,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[26],[168,169],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":171,"openalex_id":173,"authors":174,"venue":124,"cited_by_count":107,"oa_url":9,"card":208,"direction":112,"ingested_from":114},"W7214027889",[175,178,181,184,187,190,192,194,197,200,202,204,206],{"name":176,"orcid":177},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":179,"orcid":180},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":182,"orcid":183},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":185,"orcid":186},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":188,"orcid":189},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":191,"orcid":9},"Ying Luo",{"name":193,"orcid":9},"Jiaqian Zhang",{"name":195,"orcid":196},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":198,"orcid":199},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":201,"orcid":9},"Chaoliang Peng",{"name":203,"orcid":9},"Duan Tian",{"name":205,"orcid":9},"Weihao Wang",{"name":207,"orcid":9},"Jiaxin Liu",{"tldr":209,"method":210,"finding":211,"direction":112,"opportunity":212},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":221,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":225,"tags":227,"primary_topic":231,"secondary_topics":232,"classification":234,"search_phrases":258,"slug":261,"view_count":21,"doi":262,"paper":263,"created_at":285},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":159,"substance":18,"depth":129,"authority":159,"freshness":59,"relevant":21,"comment":224},"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[226],{"name":220,"url":217},[26,27,228,229,230],"深度学习","油棕","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[233],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":235},{"candidates":236,"agricultural_evidence":51},[237,241,247,254],{"code":32,"score":154,"supported":51,"title":238,"abstract":239,"tags":240},[53],[56,53],[27],{"code":39,"score":159,"supported":51,"title":242,"abstract":244,"tags":246},[243],"deep learning",[245],"YOLO",[228],{"code":248,"score":66,"supported":51,"title":249,"abstract":250,"tags":253},"B5",[],[251,252],"蘑菇","mushroom",[],{"code":79,"score":21,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[26],[259,260],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":262,"openalex_id":264,"authors":265,"venue":220,"cited_by_count":107,"oa_url":217,"card":280,"direction":112,"ingested_from":114},"W7222626135",[266,269,272,274,277],{"name":267,"orcid":268},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":270,"orcid":271},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":273,"orcid":9},"Jin Yuan Chan",{"name":275,"orcid":276},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":278,"orcid":279},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":281,"method":282,"finding":283,"direction":112,"opportunity":284},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":299,"tags":301,"primary_topic":304,"secondary_topics":305,"classification":311,"search_phrases":338,"slug":341,"view_count":107,"doi":342,"paper":343,"created_at":366},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":129,"substance":296,"depth":297,"authority":19,"freshness":128,"relevant":21,"comment":298},21,17,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[300],{"name":292,"url":289},[26,27,302,228,303],"可解释AI","土壤盐渍化",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[306,307],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":65,"slug":70,"name":308,"path":309,"parent_code":310},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":44,"locked":14,"version":45,"method":46,"evidence":312},{"candidates":313,"agricultural_evidence":51},[314,318,322,327,334],{"code":39,"score":129,"supported":51,"title":315,"abstract":316,"tags":317},[243],[228,243],[302,228],{"code":32,"score":154,"supported":51,"title":319,"abstract":320,"tags":321},[53],[56,53],[27],{"code":65,"score":323,"supported":51,"title":324,"abstract":325,"tags":326},14,[70],[69,70],[],{"code":328,"score":329,"supported":51,"title":330,"abstract":331,"tags":333},"H7",3,[],[332],"干旱",[],{"code":79,"score":21,"supported":14,"title":335,"abstract":336,"tags":337},[],[],[26],[339,340],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":342,"openalex_id":344,"authors":345,"venue":292,"cited_by_count":107,"oa_url":289,"card":361,"direction":112,"ingested_from":114},"W7221715654",[346,349,352,354,357,359],{"name":347,"orcid":348},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":350,"orcid":351},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":353,"orcid":9},"Junchi Ma",{"name":355,"orcid":356},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":358,"orcid":9},"Jiahao Qi",{"name":360,"orcid":9},"Yuan Jing",{"tldr":362,"method":363,"finding":364,"direction":112,"opportunity":365},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":373,"source_url":370,"published_at":374,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":380,"tags":382,"primary_topic":386,"secondary_topics":387,"classification":393,"search_phrases":436,"slug":439,"view_count":440,"doi":441,"paper":442,"created_at":461},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":17,"substance":377,"depth":378,"authority":323,"freshness":128,"relevant":21,"comment":379},22,19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[381],{"name":373,"url":370},[26,383,28,384,385],"粮食安全","遥感监测","农业干旱",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[388,392],{"code":328,"slug":389,"name":390,"path":391,"parent_code":310},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":394},{"candidates":395,"agricultural_evidence":51},[396,400,405,410,416,420,426,432],{"code":32,"score":154,"supported":51,"title":397,"abstract":398,"tags":399},[53],[56,53],[384],{"code":328,"score":323,"supported":51,"title":401,"abstract":403,"tags":404},[402],"drought",[332,402],[],{"code":39,"score":13,"supported":51,"title":406,"abstract":407,"tags":409},[],[228,28,61,243,408],"artificial intelligence",[28],{"code":411,"score":20,"supported":51,"title":412,"abstract":413,"tags":415},"A8",[],[383,414],"food security",[383],{"code":65,"score":66,"supported":51,"title":417,"abstract":418,"tags":419},[],[69,70],[],{"code":421,"score":329,"supported":51,"title":422,"abstract":423,"tags":425},"D",[],[424],"水产",[],{"code":427,"score":329,"supported":51,"title":428,"abstract":429,"tags":431},"H6",[],[430],"农业生态",[],{"code":79,"score":21,"supported":14,"title":433,"abstract":434,"tags":435},[],[],[26],[437,438],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":441,"openalex_id":443,"authors":444,"venue":373,"cited_by_count":107,"oa_url":454,"card":455,"direction":460,"ingested_from":114},"W7221081979",[445,448,451],{"name":446,"orcid":447},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":449,"orcid":450},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":452,"orcid":453},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":456,"method":457,"finding":458,"direction":112,"opportunity":459},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:14.933078Z",{"id":463,"title":464,"url":465,"summary":466,"summary_zh":467,"content":9,"source_name":468,"source_url":465,"published_at":469,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":470,"sources":472,"tags":474,"primary_topic":478,"secondary_topics":483,"classification":486,"search_phrases":508,"slug":511,"view_count":512,"doi":513,"paper":514,"created_at":531},4433,"PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191971","Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R2 of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R2 values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.","准确、高效地估算叶片叶绿素含量（leaf chlorophyll content，LCC）对作物生理监测和精准农业至关重要。当前遥感估算LCC的方法主要依赖机器学习和深度学习方法，这类方法具有较强的非线性建模能力，但需要充足且具有代表性的样本。辐射传输模型（radiative transfer models，RTMs）能够基于物理机制模拟冠层光谱响应并生成大规模数据集，从而克服纯数据驱动方法的局限性。然而，由于模型参数化带来的不确定性以及模拟数据与观测数据之间的差异，其在不同环境条件下的应用仍面临挑战。本研究提出了一种混合框架PROSAIL-DNN-TL，将PROSAIL辐射传输模型与全网络微调迁移学习相结合，用于冬小麦LCC估算。首先，利用PROSAIL生成的85 680个模拟样本预训练深度神经网络（deep neural network，DNN），随后利用田间观测数据（n = 270）进行全网络微调。同时建立了仅使用田间数据训练的传统DNN模型以及基于不同特征选择策略的机器学习模型作为对比。结果表明：（1）UVE选择的波段分布在可见光、红边和近红外区域；CARS在可见光区域保留了更多波段，而SPA主要选择近红外波段；（2）RR和SVR的精度高于KNN，全光谱和CARS特征集表现较优，SPA精度最低；（3）PROSAIL-DNN-TL取得了可靠的LCC估算性能，在500次重复实验中平均测试R²为0.79，且相比传统机器学习模型表现出更优的性能；（4）利用2024年和2025年生长季采集的数据集进行评估时，PROSAIL-DNN-TL均表现出可靠的预测性能，R²分别为0.75和0.92。总体而言，所提出的混合框架通过迁移学习将辐射传输模型的物理知识与数据驱动建模相结合，为作物叶绿素监测提供了一种潜在途径。然而，本研究中使用的SPAD到LCC的转换并未针对仪器和作物进行专门校准，因此绝对LCC值应谨慎解读。","Agronomy","2026-10-07T00:00:00Z",{"impact":17,"substance":377,"depth":17,"authority":323,"freshness":59,"relevant":21,"comment":471},"将PROSAIL辐射传输模型与深度神经网络迁移学习结合，用8万余模拟样本预训练、270个田间样本微调，冬小麦叶绿素反演精度可靠，方法新颖且数据规模扎实，对作物生理监测与精准农业有参考价值。",[473],{"name":468,"url":465},[26,27,475,476,477],"冬小麦","迁移学习","叶绿素监测",{"code":479,"slug":480,"name":481,"path":482,"parent_code":140},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[484,485],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":487},{"candidates":488,"agricultural_evidence":51},[489,494,498,502],{"code":479,"score":50,"supported":51,"title":490,"abstract":492,"tags":493},[491,480],"winter wheat",[481,491,480],[475],{"code":32,"score":154,"supported":51,"title":495,"abstract":496,"tags":497},[150],[56,53],[27],{"code":39,"score":13,"supported":51,"title":499,"abstract":500,"tags":501},[],[228,28,61,243],[476],{"code":79,"score":20,"supported":51,"title":503,"abstract":504,"tags":507},[],[505,506],"精准农业","precision agriculture",[26],[509,510],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433",5,"10.3390\u002Fagronomy16191971",{"doi":513,"openalex_id":515,"authors":516,"venue":468,"cited_by_count":107,"oa_url":465,"card":526,"direction":112,"ingested_from":114},"W7220844556",[517,519,521,524],{"name":518,"orcid":9},"Xueqing Zhu",{"name":520,"orcid":9},"Ying Nian",{"name":522,"orcid":523},"Jikai Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2365-5221",{"name":525,"orcid":9},"Xinwei Li",{"tldr":527,"method":528,"finding":529,"direction":112,"opportunity":530},"提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶","PROSAIL辐射传输模型生成85680样本预训练DNN，270个田间样本全网络","500次重复实验平均测试R²为0.79，2024和2025年生长季验证R²分别为0.75和0.92，","SPAD到LCC转换未针对仪器和作物标定，可研究跨传感器、跨作物、跨生长季的迁移学习泛化与绝对叶绿素标定。","2026-10-08T23:30:29.383957Z",{"id":533,"title":534,"url":535,"summary":536,"summary_zh":537,"content":9,"source_name":538,"source_url":535,"published_at":539,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":540,"score_detail":541,"sources":543,"tags":545,"primary_topic":548,"secondary_topics":549,"classification":551,"search_phrases":583,"slug":586,"view_count":512,"doi":587,"paper":588,"created_at":610},4079,"Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.08.014","Hyperspectral remote sensing holds promise for estimating crop physiological and biochemical traits, yet conventional approaches are often constrained by small sample sizes, limited accuracy in multi-trait retrieval, and poor robustness. To alleviate these challenges, we present an integrated framework that combines a pixel-wise dataset construction strategy with a multi-task deep learning architecture for estimating photosynthetic phenotyping parameters (PPPs)—a suite of traits encompassing gas exchange, pigment composition, and structural properties. Specifically, under controlled experimental conditions, we developed MTI-Net, a lightweight multi-task inversion network that simultaneously estimates multiple PPPs in rice and tomato with high precision ( R 2 > 0.90). Since physiological ground-truth measurements were obtained at the leaf level, the pixel-wise predictions should be interpreted as weakly supervised, model-derived spatial estimates or relative distributions rather than directly validated pixel-level physiological measurements. This methodological framework demonstrated stable performance across data partitions and random initializations, and maintained stable performance when applied to pixel-averaged spectra. A complementary differentiable band selection module (DRS-Net) further reduced spectral dimensionality to 40 informative bands while maintaining retrieval accuracy above 0.75, suggesting potential pathways for simplified sensor design. Importantly, the estimated PPPs revealed biologically interpretable patterns in tomato, including stage-dependent dynamics, irradiance-driven shifts, and canopy-layer differentiation—where middle leaves dominated early photosynthesis and upper leaves prevailed at later stages. Collectively, this study provides a methodological foundation for proximal hyperspectral phenotyping under specific environments. While further validation in diverse field conditions is required, the framework demonstrates potential for high-throughput multi-parameter estimation and provides a methodological basis for future cost-effective hyperspectral systems.","高光谱遥感在估算作物生理生化性状方面具有潜力，但传统方法常受限于样本量小、多性状反演精度有限以及鲁棒性差等问题。为缓解这些挑战，我们提出了一种集成框架，将像元级数据集构建策略与多任务深度学习架构相结合，用于估算光合表型参数（photosynthetic phenotyping parameters, PPPs）——一组涵盖气体交换、色素组成和结构特性的性状。具体而言，在受控实验条件下，我们开发了MTI-Net，一种轻量级多任务反演网络，可同时高精度估算水稻和番茄的多个PPPs（R² > 0.90）。由于生理真值测量是在叶片尺度获得的，像元级预测应被解释为弱监督的、模型导出的空间估计或相对分布，而非直接验证的像元级生理测量。该方法框架在数据划分和随机初始化下表现出稳定性能，并在应用于像元平均光谱时保持稳定性能。一个互补的可微分波段选择模块（DRS-Net）进一步将光谱维度降至40个信息波段，同时保持反演精度高于0.75，提示了简化传感器设计的潜在路径。重要的是，估算的PPPs在番茄中揭示了具有生物学可解释性的模式，包括阶段依赖性动态、辐照度驱动的变化以及冠层分层分化——其中中层叶片在早期光合作用中占主导，而上层叶片在后期阶段占优势。总体而言，本研究为特定环境下的近地高光谱表型分析提供了方法学基础。尽管需要在多样化田间条件下进一步验证，但该框架展示了高通量多参数估算的潜力，并为未来经济高效的高光谱系统提供了方法学依据。","Artificial Intelligence in Agriculture","2026-10-01T00:00:00Z",80,{"impact":17,"substance":377,"depth":17,"authority":323,"freshness":128,"relevant":21,"comment":542},"方法新颖、结论可靠的高光谱作物表型多任务反演研究，对智慧农业遥感监测有参考价值。",[544],{"name":538,"url":535},[26,27,228,546,547],"高光谱","作物表型",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[550],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":552},{"candidates":553,"agricultural_evidence":51},[554,558,562,566,573,579],{"code":39,"score":154,"supported":51,"title":555,"abstract":556,"tags":557},[243],[228,243],[228],{"code":32,"score":159,"supported":51,"title":559,"abstract":560,"tags":561},[],[56,53,150],[547,27,546],{"code":137,"score":66,"supported":51,"title":563,"abstract":564,"tags":565},[],[134,138],[],{"code":567,"score":66,"supported":51,"title":568,"abstract":569,"tags":572},"B1",[],[570,571],"番茄","tomato",[],{"code":574,"score":329,"supported":51,"title":575,"abstract":576,"tags":578},"I7",[],[577],"methodological framework",[],{"code":79,"score":21,"supported":14,"title":580,"abstract":581,"tags":582},[],[],[26],[584,585],"MTI-Net 高光谱 作物表型","水稻 番茄 光合表型 高光谱","MTI-Net高光谱作物表型-4079","10.1016\u002Fj.aiia.2026.08.014",{"doi":587,"openalex_id":589,"authors":590,"venue":538,"cited_by_count":107,"oa_url":535,"card":605,"direction":112,"ingested_from":114},"W7215067733",[591,593,595,598,601,603],{"name":592,"orcid":9},"Mengqi Zhang",{"name":594,"orcid":9},"Jiaren Zhou",{"name":596,"orcid":597},"Qingfeng Song","https:\u002F\u002Forcid.org\u002F0000-0003-4314-6897",{"name":599,"orcid":600},"Man Zhang","https:\u002F\u002Forcid.org\u002F0009-0007-4481-0514",{"name":602,"orcid":9},"Xinguang Zhu",{"name":604,"orcid":9},"Minjuan Wang",{"tldr":606,"method":607,"finding":608,"direction":112,"opportunity":609},"提出像素级多任务深度学习框架，从高光谱数据高通量估算作物光合表型参数。","构建像素级数据集，设计轻量多任务网络MTI-Net及可微波段选择模块DRS-Ne","MTI-Net对水稻和番茄多个光合参数估算R²>0.90，DRS-Net降至40波段仍保持精度>0.","可探索弱监督像素级预测的田间验证，以及低成本高光谱传感器与多任务模型的联合优化。","2026-10-03T23:30:24.577846Z"]