[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4065":3,"related-4065":106},{"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,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":105},4065,"A new vegetation–thermal integrated approach (NNL-SM) for high-resolution soil moisture estimation","https:\u002F\u002Fdoi.org\u002F10.1080\u002F10106049.2026.2689867","High-resolution soil moisture (SM) information is essential for hydrological, agricultural and climate studies. This paper presents an NDMI–NDVI–LST-based downscaling technique, termed the NNL-SM model, to downscale SMAP SM from 9 to 1 km resolution. The model was validated in Varanasi, India, using ground SM measurements collected on fifteen dates and compared with the VMSMI and triangle optical-based models. By integrating the normalized NDMI, NDVI and LST while minimizing vegetation effects, the NNL-SM model achieved the best performance, with the lowest RMSE (0.028 m3\u002Fm3), ubRMSE (0.023 m3\u002Fm3), smallest bias (0.001 m3\u002Fm3) and highest correlation (R = 0.700). VMSMI showed moderate accuracy, whereas the Triangle model had weaker correlation. Seasonal analysis indicated better winter performance and reduced pre-monsoon accuracy due to vegetation and temperature variability. Overall, the NNL-SM approach showed promising performance for the selected heterogeneous agricultural region and can be further evaluated using hybrid indices and multi-region validation.","高分辨率土壤水分（SM）信息对水文、农业和气候研究至关重要。本文提出了一种基于NDMI–NDVI–LST的降尺度技术，称为NNL-SM模型，用于将SMAP土壤水分数据从9 km降尺度至1 km分辨率。该模型在印度瓦拉纳西使用15个日期的地面土壤水分实测数据进行验证，并与VMSMI模型和基于三角光学模型进行了比较。通过整合归一化NDMI、NDVI和LST，同时最小化植被影响，NNL-SM模型表现最佳，具有最低的RMSE（0.028 m³\u002Fm³）、ubRMSE（0.023 m³\u002Fm³）、最小偏差（0.001 m³\u002Fm³）和最高相关性（R = 0.700）。VMSMI表现出中等精度，而三角模型相关性较弱。季节性分析表明冬季表现较好，季风前由于植被和温度变异性导致精度降低。总体而言，NNL-SM方法在所选异质农业区域表现出良好性能，可进一步使用混合指数和多区域验证进行评估。",null,"Geocarto International","2026-10-01T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"提出NDMI-NDVI-LST融合降尺度模型，将SMAP土壤水分从9km提升至1km并验证精度，方法新颖、结论可靠，对农业旱情监测有实用价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","遥感","土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56,61],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},15,true,[33],[49,33],"土壤",[30],{"code":52,"score":22,"supported":14,"title":53,"abstract":54,"tags":55},"I1",[],[],[27],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I2",[],[],[28],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I3",[],[],[29],[67,68],"SMAP 土壤水分 降尺度","NNL-SM 土壤水分 遥感","SMAP土壤水分降尺度-4065",2,"10.1080\u002F10106049.2026.2689867",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":95,"oa_url":96,"card":97,"direction":103,"ingested_from":104},"W7215055402",[75,78,81,84,86,89,92],{"name":76,"orcid":77},"B H PRAJAPATI","https:\u002F\u002Forcid.org\u002F0009-0007-1840-9786",{"name":79,"orcid":80},"Rajendra Prasad","https:\u002F\u002Forcid.org\u002F0000-0002-3340-2853",{"name":82,"orcid":83},"Jyoti Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-9400-6635",{"name":85,"orcid":9},"Prashant Kumar Srivastava",{"name":87,"orcid":88},"Sonu Kumar","https:\u002F\u002Forcid.org\u002F0009-0002-2713-3295",{"name":90,"orcid":91},"Dileep Kumar Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4119-7319",{"name":93,"orcid":94},"Varun Narayan Mishra","https:\u002F\u002Forcid.org\u002F0000-0002-4336-8038",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F10106049.2026.2689867?needAccess=true",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"提出NNL-SM模型，融合NDMI、NDVI和LST将SMAP土壤水分从9km降尺度至1km。","基于NDMI-NDVI-LST的降尺度技术，用印度Varanasi地面数据验证。","NNL-SM精度最优，RMSE 0.028 m³\u002Fm³，R=0.700，冬季表现优于季风前。","农业遥感与作物表型","可探索混合指数与多区域验证，提升异质农业区及季风前土壤水分估算精度。","智慧农业 \u002F 农业物联网","openalex","2026-10-03T23:30:11.581927Z",{"total":107,"page":22,"page_size":107,"items":108},6,[109,183,262,321,404,454],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":124,"tags":126,"primary_topic":128,"secondary_topics":129,"classification":130,"search_phrases":158,"slug":160,"view_count":107,"doi":161,"paper":162,"created_at":182},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing","2026-09-22T00:00:00Z",81,{"impact":119,"substance":120,"depth":119,"authority":121,"freshness":122,"relevant":22,"comment":123},18,22,14,9,"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[125],{"name":115,"url":112},[27,28,127,29,30],"深度学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":131},{"candidates":132,"agricultural_evidence":46},[133,137,143,150,154],{"code":32,"score":45,"supported":46,"title":134,"abstract":135,"tags":136},[33],[49,33],[30],{"code":62,"score":138,"supported":46,"title":139,"abstract":140,"tags":142},7,[],[29,141],"remote sensing",[29],{"code":144,"score":145,"supported":46,"title":146,"abstract":147,"tags":149},"H3",3,[],[148],"水资源",[],{"code":57,"score":70,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[28,127],{"code":52,"score":22,"supported":14,"title":155,"abstract":156,"tags":157},[],[],[27],[67,159],"Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":161,"openalex_id":163,"authors":164,"venue":115,"cited_by_count":95,"oa_url":112,"card":177,"direction":101,"ingested_from":104},"W7208807695",[165,167,169,171,173,175],{"name":166,"orcid":9},"Yuyang Fan",{"name":168,"orcid":9},"Jianwei Ma",{"name":170,"orcid":9},"Mengmeng Li",{"name":172,"orcid":9},"Changqing Ke",{"name":174,"orcid":9},"Bin Cheng",{"name":176,"orcid":9},"Zheng Duan",{"tldr":178,"method":179,"finding":180,"direction":101,"opportunity":181},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":194,"tags":196,"primary_topic":199,"secondary_topics":204,"classification":210,"search_phrases":236,"slug":239,"view_count":95,"doi":240,"paper":241,"created_at":261},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":122,"relevant":22,"comment":193},"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[195],{"name":189,"url":186},[27,28,29,197,198],"土壤盐渍化","新疆农业",{"code":62,"slug":200,"name":201,"path":202,"parent_code":203},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[205,206],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":57,"slug":207,"name":208,"path":209,"parent_code":203},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":211},{"candidates":212,"agricultural_evidence":46},[213,217,221,226,232],{"code":62,"score":45,"supported":46,"title":214,"abstract":215,"tags":216},[141],[29,141],[29],{"code":32,"score":121,"supported":46,"title":218,"abstract":219,"tags":220},[33],[49,33],[],{"code":57,"score":122,"supported":46,"title":222,"abstract":224,"tags":225},[223],"deep learning",[],[28],{"code":227,"score":145,"supported":46,"title":228,"abstract":229,"tags":231},"H7",[],[230],"干旱",[],{"code":52,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[27],[237,238],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":240,"openalex_id":242,"authors":243,"venue":189,"cited_by_count":95,"oa_url":186,"card":256,"direction":101,"ingested_from":104},"W7221816183",[244,246,249,251,253],{"name":245,"orcid":9},"Mingjie Qian",{"name":247,"orcid":248},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":250,"orcid":9},"Peng Wang",{"name":252,"orcid":9},"Hangyuan Liu",{"name":254,"orcid":255},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":257,"method":258,"finding":259,"direction":101,"opportunity":260},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":281,"classification":283,"search_phrases":302,"slug":305,"view_count":95,"doi":306,"paper":307,"created_at":320},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z",69,{"impact":17,"substance":272,"depth":273,"authority":20,"freshness":21,"relevant":22,"comment":274},20,16,"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[276],{"name":268,"url":265},[27,28,278,30,279],"决策支持系统","作物选择",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[282],{"code":57,"slug":207,"name":208,"path":209,"parent_code":203},{"status":39,"locked":14,"version":40,"method":41,"evidence":284},{"candidates":285,"agricultural_evidence":46},[286,290,298],{"code":32,"score":45,"supported":46,"title":287,"abstract":288,"tags":289},[33],[49,33],[30],{"code":57,"score":291,"supported":46,"title":292,"abstract":293,"tags":297},11,[],[294,295,296],"机器学习","machine learning","decision support",[28,278],{"code":52,"score":22,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[27],[303,304],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":306,"openalex_id":308,"authors":309,"venue":268,"cited_by_count":95,"oa_url":265,"card":314,"direction":103,"ingested_from":104},"W7220991946",[310,312],{"name":311,"orcid":9},"C. SAI KUMAR",{"name":313,"orcid":9},"Prof. G SREENIVASULU",{"tldr":315,"method":316,"finding":317,"direction":318,"opportunity":319},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","农业人工智能与决策模型","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","2026-10-09T23:30:12.285129Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":117,"score_detail":329,"sources":331,"tags":333,"primary_topic":336,"secondary_topics":337,"classification":339,"search_phrases":368,"slug":371,"view_count":22,"doi":372,"paper":373,"created_at":403},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data","2026-10-06T00:00:00Z",{"impact":119,"substance":120,"depth":119,"authority":121,"freshness":122,"relevant":22,"comment":330},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[332],{"name":327,"url":324},[27,28,334,29,335],"高光谱","土壤监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[338],{"code":62,"slug":200,"name":201,"path":202,"parent_code":203},{"status":39,"locked":14,"version":40,"method":41,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346,351,357,361],{"code":32,"score":121,"supported":46,"title":343,"abstract":344,"tags":345},[33],[49,33],[],{"code":62,"score":20,"supported":46,"title":347,"abstract":349,"tags":350},[348],"hyperspectral",[348],[29,334],{"code":52,"score":138,"supported":46,"title":352,"abstract":353,"tags":356},[],[354,355],"精准农业","precision agriculture",[27],{"code":57,"score":138,"supported":46,"title":358,"abstract":359,"tags":360},[],[294,295],[28],{"code":362,"score":107,"supported":46,"title":363,"abstract":364,"tags":367},"F3",[],[365,366],"施肥","fertilization",[],[369,370],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":372,"openalex_id":374,"authors":375,"venue":327,"cited_by_count":95,"oa_url":397,"card":398,"direction":103,"ingested_from":104},"W7220738771",[376,379,382,385,388,391,394],{"name":377,"orcid":378},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":380,"orcid":381},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":383,"orcid":384},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":386,"orcid":387},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":389,"orcid":390},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":392,"orcid":393},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":395,"orcid":396},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":399,"method":400,"finding":401,"direction":101,"opportunity":402},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":9,"content":9,"source_name":409,"source_url":9,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":414,"tags":416,"primary_topic":417,"secondary_topics":418,"classification":420,"search_phrases":441,"slug":444,"view_count":433,"doi":9,"paper":445,"created_at":453},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":119,"substance":120,"depth":119,"authority":121,"freshness":107,"relevant":22,"comment":413},"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[415],{"name":409,"url":407},[27,28,294,29,335],{"code":57,"slug":207,"name":208,"path":209,"parent_code":203},[419],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":421},{"candidates":422,"agricultural_evidence":46},[423,428,432,437],{"code":57,"score":273,"supported":46,"title":424,"abstract":426,"tags":427},[425],"artificial intelligence",[127,294],[28,294],{"code":32,"score":291,"supported":46,"title":429,"abstract":430,"tags":431},[33],[49],[],{"code":62,"score":433,"supported":46,"title":434,"abstract":435,"tags":436},4,[],[29],[29],{"code":52,"score":22,"supported":14,"title":438,"abstract":439,"tags":440},[],[],[27],[442,443],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":446,"venue":9,"cited_by_count":95,"oa_url":9,"card":447,"direction":318,"ingested_from":452},[],{"tldr":448,"method":449,"finding":450,"direction":318,"opportunity":451},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":469,"secondary_topics":470,"classification":473,"search_phrases":502,"slug":505,"view_count":107,"doi":506,"paper":507,"created_at":538},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":17,"substance":119,"depth":273,"authority":20,"freshness":21,"relevant":22,"comment":464},"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[466],{"name":460,"url":457},[27,28,354,29,468],"土壤碳汇",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[471,472],{"code":57,"slug":207,"name":208,"path":209,"parent_code":203},{"code":62,"slug":200,"name":201,"path":202,"parent_code":203},{"status":39,"locked":14,"version":40,"method":41,"evidence":474},{"candidates":475,"agricultural_evidence":46},[476,480,484,488,492,498],{"code":32,"score":121,"supported":46,"title":477,"abstract":478,"tags":479},[33],[49,33],[],{"code":57,"score":13,"supported":46,"title":481,"abstract":482,"tags":483},[],[294,295,425],[28],{"code":62,"score":13,"supported":46,"title":485,"abstract":486,"tags":487},[],[29,141,348],[29],{"code":52,"score":21,"supported":46,"title":489,"abstract":490,"tags":491},[],[354,355],[27,354],{"code":493,"score":433,"supported":46,"title":494,"abstract":495,"tags":497},"H4",[],[496],"carbon sequestration",[468],{"code":227,"score":145,"supported":46,"title":499,"abstract":500,"tags":501},[],[230],[],[503,504],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":506,"openalex_id":508,"authors":509,"venue":460,"cited_by_count":95,"oa_url":457,"card":533,"direction":101,"ingested_from":104},"W7213561504",[510,513,516,519,522,525,527,529,531],{"name":511,"orcid":512},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":514,"orcid":515},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":517,"orcid":518},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":520,"orcid":521},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":523,"orcid":524},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":526,"orcid":9},"Almaha Jamal",{"name":528,"orcid":9},"Afra Rashed",{"name":530,"orcid":9},"Hamda Yousif",{"name":532,"orcid":9},"Sarah Sadeq",{"tldr":534,"method":535,"finding":536,"direction":101,"opportunity":537},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z"]