[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4358":3,"related-4358":65},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":45,"slug":48,"view_count":21,"doi":49,"paper":50,"created_at":64},4358,"Flood extent mapping using optical remote sensing indices: a case study of the upper Medjerda River basin (Northwestern Tunisia)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11600-026-02036-7","Flood extent mapping using optical remote sensing indices: a case study of the upper Medjerda River basin (Northwestern Tunisia)。Acta Geophysica",null,"Acta Geophysica","2026-10-06T00:00:00Z","论文",10,false,59,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,14,15,13,9,1,"发表于核心期刊的遥感洪水制图案例研究，方法有参考价值但属区域性应用，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29],"农业遥感","遥感","洪水监测","突尼斯",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":44},[37],{"code":38,"score":19,"supported":39,"title":40,"abstract":42,"tags":43},"I3",true,[41],"remote sensing",[41],[26,27],"农业相关性需要复核",[46,47],"农业遥感 洪水监测 突尼斯 遥感","农业遥感 洪水监测","农业遥感洪水监测突尼斯遥感-4358","10.1007\u002Fs11600-026-02036-7",{"doi":49,"openalex_id":51,"authors":52,"venue":9,"cited_by_count":61,"oa_url":8,"card":8,"direction":62,"ingested_from":63},"W7220716147",[53,56,59],{"name":54,"orcid":55},"Ahmed Ezzine","https:\u002F\u002Forcid.org\u002F0000-0002-7497-8197",{"name":57,"orcid":58},"Taoufik Hermassi","https:\u002F\u002Forcid.org\u002F0000-0002-8782-6192",{"name":60,"orcid":8},"Emna Kochlef",0,"农业遥感与作物表型","openalex","2026-10-07T23:30:26.088231Z",{"total":66,"page":21,"page_size":66,"items":67},6,[68,151,200,265,350,427],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":73,"content":8,"source_name":74,"source_url":71,"published_at":75,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":76,"score_detail":77,"sources":82,"tags":84,"primary_topic":88,"secondary_topics":93,"classification":99,"search_phrases":135,"slug":138,"view_count":61,"doi":139,"paper":140,"created_at":150},5008,"ASSESSING THE GEOECOLOGICAL IMPACT OF LARGE HYDRAULIC STRUCTURES IN ARID REGIONS USING REMOTE SENSING AND GIS TECHNOLOGIES (A CASE STUDY OF THE CENTRAL FERGANA RESERVOIR)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23359723","This article presents a comprehensive study of the geoecological and ameliorative impacts of large hydraulic structures built under arid subtropical conditions on adjacent agricultural landscapes, using the Central Fergana Reservoir (CFR) as a case study. The main objective of the study is to assess landscape changes, the spatial dynamics of vegetation cover, and secondary soil salinization processes across 29,330 hectares within the reservoir’s impact zone between 2010 and 2025 using remote sensing and GIS technologies. The study calculates the Normalized Difference Vegetation Index (NDVI) based on Landsat satellite imagery (Landsat 5 TM, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2) and analyzes long-term changes. The results show that the water surface area expanded from 1% (293.3 ha) in 2010 to 15% (4,399.5 ha) by 2025. Furthermore, analyses of 27 soil profiles and laboratory measurements of electrical conductivity (\\(EC_e\\)) and moisture content confirmed the formation of localized waterlogged areas and highly saline solonchaks in the 500-meter coastal buffer zone as a result of a rise in the groundwater table to 1.0–1.5 m. Environmental stress factors identified through comparative analyses with a control (background) site were scientifically substantiated, and practical recommendations for sustainable regional management were developed.","本文以中央费尔干纳水库（Central Fergana Reservoir, CFR）为例，对干旱亚热带条件下大型水利设施对邻近农业景观的地质生态与土壤改良影响进行了综合研究。研究的主要目标在于利用遥感与GIS技术，评估2010年至2025年间水库影响区内29330公顷土地上的景观变化、植被覆盖空间动态以及次生土壤盐渍化过程。研究基于Landsat卫星影像（Landsat 5 TM、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2）计算归一化植被指数（Normalized Difference Vegetation Index, NDVI），并分析其长期变化。结果表明，水域面积从2010年的1%（293.3公顷）扩大至2025年的15%（4399.5公顷）。此外，对27个土壤剖面的分析以及电导率（\\(EC_e\\)）和含水量的实验室测定证实，由于地下水位上升至1.0–1.5米，在500米沿岸缓冲带内形成了局部渍水区和重度盐化盐土。通过与对照（背景）样地的比较分析，科学地证实了所识别的环境胁迫因子，并提出了区域可持续管理的实践建议。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-14T00:00:00Z",74,{"impact":78,"substance":79,"depth":80,"authority":19,"freshness":20,"relevant":21,"comment":81},12,22,18,"基于Landsat长时序遥感与GIS评估干旱区大型水利工程对灌区植被与次生盐渍化的影响，方法规范、数据翔实，对农业遥感与灌区生态监测有参考价值，但属区域案例研究，公共影响有限。",[83],{"name":74,"url":71},[26,27,85,86,87],"水资源管理","灌溉农业","盐渍化",{"code":38,"slug":89,"name":90,"path":91,"parent_code":92},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[94],{"code":95,"slug":96,"name":97,"path":98,"parent_code":92},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":100,"locked":13,"version":33,"method":34,"evidence":101},"classified",{"candidates":102,"agricultural_evidence":39},[103,108,114,122,129],{"code":38,"score":104,"supported":39,"title":105,"abstract":106,"tags":107},16,[41],[27,41],[26,27],{"code":95,"score":109,"supported":39,"title":110,"abstract":112,"tags":113},11,[111],"GIS",[111],[],{"code":115,"score":116,"supported":39,"title":117,"abstract":118,"tags":121},"H2",7,[],[119,120],"土壤","soil",[87],{"code":123,"score":124,"supported":39,"title":125,"abstract":126,"tags":128},"H7",3,[],[127],"干旱",[],{"code":130,"score":131,"supported":13,"title":132,"abstract":133,"tags":134},"H3",2,[],[],[85,86],[136,137],"费尔干纳水库 遥感 盐渍化","Landsat NDVI 灌区 土壤盐渍化","费尔干纳水库遥感盐渍化-5008","10.5281\u002Fzenodo.23359723",{"doi":139,"openalex_id":141,"authors":142,"venue":74,"cited_by_count":61,"oa_url":71,"card":145,"direction":62,"ingested_from":63},"W7221819099",[143],{"name":144,"orcid":8},"Akbarov Sarvarbek",{"tldr":146,"method":147,"finding":148,"direction":62,"opportunity":149},"用遥感和GIS评估费尔干纳中部水库对周边农业景观的生态影响。","Landsat影像NDVI分析、27个土壤剖面及ECe与含水量测定。","水库水面从1%扩至15%，500米缓冲带地下水位升至1.0–1.5米，形成盐渍化。","可结合多源遥感与土壤盐分模型，构建干旱区水库周边盐渍化预警与灌溉调控方案。","2026-10-15T23:30:29.814507Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":74,"source_url":154,"published_at":157,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":158,"score_detail":159,"sources":161,"tags":163,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":182,"slug":185,"view_count":61,"doi":186,"paper":187,"created_at":199},4748,"Study of Territories Using Geoinformatics and Remote Sensing Technologies: Spectral Band Combinations Based on Sentinel-2 Data (A Case Study of Parkent District)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23297593","This scientific paper analyzes the combination of spectral bands based on data from the European Space Agency's (ESA) Sentinel-2 satellite, using geoinformatics and remote sensing technologies. The Parkent district of the Tashkent region, Republic of Uzbekistan, was selected as the object of study. The research primarily compares the characteristics of the NIR-RED-GREEN (Near-Infrared, Red, Green) and SWIR-NIR-GREEN (Shortwave Infrared, Near-Infrared, Green) band combinations, as well as their advantages in identifying agricultural lands, forest cover, and assessing moisture levels.","本文基于欧洲空间局（ESA）Sentinel-2卫星数据，运用地理信息学与遥感技术，对光谱波段组合进行了分析。研究选取乌兹别克斯坦共和国塔什干州帕尔肯特区作为研究对象。研究主要比较了近红外-红-绿（NIR-RED-GREEN）与短波红外-近红外-绿（SWIR-NIR-GREEN）波段组合的特征，以及二者在识别农业用地、森林覆盖和评估湿度水平方面的优势。","2026-10-11T00:00:00Z",62,{"impact":16,"substance":80,"depth":18,"authority":78,"freshness":20,"relevant":21,"comment":160},"基于Sentinel-2多光谱波段组合的遥感应用研究，方法具体、数据来源权威，但研究对象为乌兹别克斯坦地方区域，对国内三农信息化的直接参考价值有限。",[162],{"name":74,"url":154},[26,164,27,165,166],"Sentinel-2","乌兹别克斯坦","光谱波段",{"code":38,"slug":89,"name":90,"path":91,"parent_code":92},[],{"status":100,"locked":13,"version":33,"method":34,"evidence":170},{"candidates":171,"agricultural_evidence":39},[172,177],{"code":38,"score":173,"supported":39,"title":174,"abstract":175,"tags":176},17,[41],[27,41],[164,26,27],{"code":95,"score":124,"supported":39,"title":178,"abstract":179,"tags":181},[],[180],"地理信息",[],[183,184],"Sentinel-2 光谱波段 组合","Parkent 遥感 农业用地","Sentinel-2光谱波段组合-4748","10.5281\u002Fzenodo.23297593",{"doi":186,"openalex_id":188,"authors":189,"venue":74,"cited_by_count":61,"oa_url":154,"card":194,"direction":62,"ingested_from":63},"W7221467289",[190,192],{"name":191,"orcid":8},"Xalilov Doniyor",{"name":193,"orcid":8},"Allambergenova Venera",{"tldr":195,"method":196,"finding":197,"direction":62,"opportunity":198},"基于Sentinel-2数据比较NIR-RED-GREEN与SWIR-NIR-GREEN波段组合，用","使用ESA Sentinel-2影像与地理信息学、遥感技术，对比两种光谱波段组合","NIR-RED-GREEN和SWIR-NIR-GREEN组合在识别农业用地、森林覆盖和评估湿度上各有","可进一步量化不同波段组合在干旱区作物分类与土壤湿度反演中的精度差异，并构建自动化优选流程。","2026-10-12T23:30:24.415736Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":207,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":208,"score_detail":209,"sources":212,"tags":214,"primary_topic":8,"secondary_topics":218,"classification":219,"search_phrases":234,"slug":237,"view_count":124,"doi":238,"paper":239,"created_at":264},3789,"A Satellite-Based Framework for Flood Mapping: Enhancing Explainability Through Multispectral Indices and SHAP Analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41976-026-00311-1","While machine learning (ML) models have shown considerable promise for modelling complex environmental phenomena such as floods, their adoption in flood mapping is often constrained by limited interpretability. This study develops an integrated SHapley Additive exPlanations (SHAP)-enhanced framework for satellite-based flood extent mapping using multispectral and topographic variables. Spectral information derived from Landsat 7, including the Normalized Difference Water Index (NDWI), Automated Water Extraction Index (AWEI), Normalized Difference Vegetation Index (NDVI), and near- and shortwave-infrared reflectance, was combined with slope and elevation derived from SRTM. Historical flood extent labels were obtained from the Global Flood Database. Feature selection was performed in two stages by first reducing redundancy through correlation analysis and then applying SHAP-based attribution to quantify the contribution of the remaining variables. The final model retained four primary predictors: AWEI, Near-Infrared (NIR) reflectance, slope, and elevation. A supervised Light Gradient Boosting Machine (LightGBM) model achieved an accuracy of 92% and an AUC of 0.950, outperforming Logistic Regression and TabNet. SHAP analysis provided global and local explanations of the model output, while spatial comparison with the MODIS-derived flood extent showed broad agreement in the analysed event. The framework therefore focuses on event-based flood extent classification rather than long-term pre-event flood susceptibility prediction and demonstrates the value of combining predictive performance with model transparency for remote-sensing-based flood mapping.","尽管机器学习（ML）模型在洪水等复杂环境现象的建模中展现出相当大的潜力，但其在洪水制图中的采用往往受限于可解释性不足。本研究开发了一个集成的SHapley加法解释（SHAP）增强框架，用于基于多光谱和地形变量的卫星洪水范围制图。从Landsat 7获取的光谱信息，包括归一化差异水体指数（NDWI）、自动水体提取指数（AWEI）、归一化差异植被指数（NDVI）以及近红外和短波红外反射率，与从SRTM获取的坡度和高程相结合。历史洪水范围标签来自全球洪水数据库。特征选择分两个阶段进行：首先通过相关性分析减少冗余，然后应用基于SHAP的归因来量化剩余变量的贡献。最终模型保留了四个主要预测因子：AWEI、近红外（NIR）反射率、坡度和高程。监督式轻量梯度提升机（LightGBM）模型达到了92%的准确率和0.950的AUC，优于逻辑回归和TabNet。SHAP分析为模型输出提供了全局和局部解释，而与MODIS衍生洪水范围的空间比较显示，在分析事件中两者具有广泛一致性。因此，该框架侧重于基于事件的洪水范围分类，而非长期灾前洪水脆弱性预测，并展示了将预测性能与模型透明度相结合对于基于遥感的洪水制图的价值。","Remote Sensing in Earth Systems Sciences","2026-09-28T00:00:00Z",70,{"impact":78,"substance":210,"depth":173,"authority":19,"freshness":16,"relevant":21,"comment":211},20,"将SHAP可解释性引入多光谱遥感洪水制图，方法新颖、精度可靠，对农业灾害监测信息化有参考价值，但属细分领域技术论文，影响面有限。",[213],{"name":206,"url":203},[215,216,27,28,217],"农业人工智能","可解释AI","多光谱指数",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":220},{"candidates":221,"agricultural_evidence":13,"reason":44},[222,227],{"code":38,"score":18,"supported":39,"title":223,"abstract":225,"tags":226},[224],"multispectral",[27,224],[27],{"code":228,"score":16,"supported":39,"title":229,"abstract":230,"tags":233},"I2",[],[231,232],"机器学习","machine learning",[215,216],[235,236],"Landsat 7 NDWI 洪水制图","SHAP LightGBM 洪水范围","Landsat7NDWI洪水制图-3789","10.1007\u002Fs41976-026-00311-1",{"doi":238,"openalex_id":240,"authors":241,"venue":206,"cited_by_count":61,"oa_url":258,"card":259,"direction":62,"ingested_from":63},"W7214537961",[242,245,248,251,253,255],{"name":243,"orcid":244},"Nur Suhaili Mansor","https:\u002F\u002Forcid.org\u002F0000-0002-7780-3691",{"name":246,"orcid":247},"Sergio Molina-Palacios","https:\u002F\u002Forcid.org\u002F0000-0002-8909-6285",{"name":249,"orcid":250},"Hapini Awang","https:\u002F\u002Forcid.org\u002F0000-0001-6748-3893",{"name":252,"orcid":8},"Saw Wei Heng",{"name":254,"orcid":8},"Mou Leong Tan",{"name":256,"orcid":257},"George Hloupis","https:\u002F\u002Forcid.org\u002F0000-0001-7722-3112","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs41976-026-00311-1.pdf",{"tldr":260,"method":261,"finding":262,"direction":62,"opportunity":263},"构建SHAP增强的卫星洪水制图框架，用多光谱与地形变量训练LightGBM实现高精度分类。","Landsat 7多光谱指数与SRTM地形，两阶段特征选择，LightGBM建模","AWEI、NIR、坡度、高程为关键预测因子，LightGBM精度92%、AUC 0.950，优于对比","可迁移至农田洪涝监测，探索多时相与作物物候耦合的可解释洪水风险制图。","2026-09-29T23:30:28.337497Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":8,"source_name":271,"source_url":268,"published_at":272,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":273,"score_detail":274,"sources":276,"tags":278,"primary_topic":8,"secondary_topics":282,"classification":283,"search_phrases":304,"slug":307,"view_count":308,"doi":309,"paper":310,"created_at":349},3507,"OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations","https:\u002F\u002Fdoi.org\u002F10.1029\u002F2026gl124105","Abstract Terrestrial ecosystem regulation of carbon and water fluxes is critical for constraining climate–biosphere feedbacks but remains poorly quantified across space and time. Here, we evaluate whether co‐located observations from NASA's Orbiting Carbon Observatory‐3 (OCO‐3) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can reproduce ecosystem water use efficiency (WUE) dynamics observed at FLUXNET sites across temporal scales, vegetation types, climates, and drought conditions. We created the ECOCO3 data set, which harmonizes OCO‐3 and ECOSTRESS observations in space and time. ECOCO3 captures broad seasonal and diurnal carbon and water flux patterns including midday drought responses. Sampling sensitivity analysis shows that ECOCO3 is primarily limited by available sample size for distinguishing vegetation and climate driven differences in WUE. Our findings highlight both the promise and limitations of remote sensing for resolving sub‐daily carbon–water coupling.","陆地生态系统对碳通量和水通量的调节对于约束气候–生物圈反馈至关重要，但在空间和时间尺度上仍缺乏充分的量化。在此，我们评估了来自NASA轨道碳观测站-3（OCO-3）和空间站生态系统星载热辐射计实验（ECOSTRESS）的同位观测能否在时间尺度、植被类型、气候条件和干旱状况下重现FLUXNET站点观测到的生态系统水分利用效率（WUE）动态。我们创建了ECOCO3数据集，该数据集在空间和时间上协调了OCO-3和ECOSTRESS的观测。ECOCO3能够捕捉广泛的季节性和日间碳通量与水通量模式，包括正午干旱响应。采样敏感性分析表明，ECOCO3主要受限于可用样本量，难以区分植被和气候驱动的WUE差异。我们的研究结果既凸显了遥感在解析亚日尺度碳–水耦合方面的前景，也揭示了其局限性。","Geophysical Research Letters","2026-09-23T00:00:00Z",81,{"impact":80,"substance":79,"depth":80,"authority":18,"freshness":16,"relevant":21,"comment":275},"NASA两颗卫星协同观测提升生态系统碳水耦合监测能力，方法新颖、数据可靠，对农业遥感与水资源管理有参考价值。",[277],{"name":271,"url":268},[26,27,279,280,281],"水资源利用","生态监测","碳汇",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":284},{"candidates":285,"agricultural_evidence":13,"reason":44},[286,290,295,299],{"code":38,"score":16,"supported":39,"title":287,"abstract":288,"tags":289},[],[27,41],[26,27],{"code":123,"score":66,"supported":39,"title":291,"abstract":292,"tags":294},[],[127,293],"drought",[],{"code":130,"score":21,"supported":13,"title":296,"abstract":297,"tags":298},[],[],[279],{"code":300,"score":21,"supported":13,"title":301,"abstract":302,"tags":303},"H6",[],[],[280],[305,306],"OCO-3 ECOSTRESS 水分利用效率","ECOCO3 数据集 碳水通量","OCO-3ECOSTRESS水分利用效率-3507",5,"10.1029\u002F2026gl124105",{"doi":309,"openalex_id":311,"authors":312,"venue":271,"cited_by_count":61,"oa_url":343,"card":344,"direction":62,"ingested_from":63},"W7214122316",[313,316,319,322,325,328,331,334,337,340],{"name":314,"orcid":315},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":317,"orcid":318},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":320,"orcid":321},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":323,"orcid":324},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":326,"orcid":327},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":329,"orcid":330},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":332,"orcid":333},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":335,"orcid":336},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":338,"orcid":339},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":341,"orcid":342},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":345,"method":346,"finding":347,"direction":62,"opportunity":348},"融合OCO-3与ECOSTRESS观测评估生态系统日间水分利用效率动态。","构建ECOCO3数据集，结合FLUXNET站点验证与采样敏感性分析。","ECOCO3能捕捉季节与日间碳-水通量模式，但样本量限制其区分植被与气候差异。","可探索多源遥感融合提升亚日尺度碳水耦合估算精度，并扩展至农田生态系统。","2026-09-25T23:30:33.881463Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":8,"source_name":356,"source_url":353,"published_at":357,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":358,"score_detail":359,"sources":362,"tags":364,"primary_topic":368,"secondary_topics":372,"classification":377,"search_phrases":404,"slug":407,"view_count":308,"doi":408,"paper":409,"created_at":426},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":16,"substance":360,"depth":173,"authority":19,"freshness":20,"relevant":21,"comment":361},21,"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[363],{"name":356,"url":353},[26,365,27,366,367],"气候变化","土壤碳汇","数字土壤制图",{"code":115,"slug":120,"name":369,"path":370,"parent_code":371},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[373],{"code":123,"slug":374,"name":375,"path":376,"parent_code":371},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":100,"locked":13,"version":33,"method":34,"evidence":378},{"candidates":379,"agricultural_evidence":39},[380,384,389,395,399],{"code":115,"score":17,"supported":39,"title":381,"abstract":382,"tags":383},[120],[119,120],[],{"code":123,"score":12,"supported":39,"title":385,"abstract":386,"tags":388},[],[365,127,387],"climate change",[365],{"code":390,"score":124,"supported":39,"title":391,"abstract":392,"tags":394},"J1",[],[393],"农业规划",[],{"code":38,"score":131,"supported":13,"title":396,"abstract":397,"tags":398},[],[],[26,27],{"code":400,"score":21,"supported":13,"title":401,"abstract":402,"tags":403},"H4",[],[],[366],[405,406],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191","10.3389\u002Fsjss.2026.16733",{"doi":408,"openalex_id":410,"authors":411,"venue":356,"cited_by_count":61,"oa_url":353,"card":420,"direction":425,"ingested_from":63},"W7213971196",[412,414,416,418],{"name":413,"orcid":8},"Faroug A.H. Jadalla",{"name":415,"orcid":8},"Kolapo O. Oluwasemire",{"name":417,"orcid":8},"Abd Elmagid A. Elmobarak",{"name":419,"orcid":8},"Mohammed A. M. Mohammed Zein",{"tldr":421,"method":422,"finding":423,"direction":62,"opportunity":424},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":8,"source_name":433,"source_url":430,"published_at":434,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":435,"score_detail":436,"sources":438,"tags":440,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":457,"slug":460,"view_count":124,"doi":461,"paper":462,"created_at":488},3166,"Case Studies of Environmental Monitoring Based on the Integrated Use of Bioindicators and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183227","This narrative review examines how plant bioindication can be integrated with remote sensing to support the preliminary screening of soil contamination, framed as an environmental situational awareness problem addressed through multi-scale imagery and field validation. Seven case studies—illegal waste deposits, pipeline leaks, landfill impacts, industrial areas, agrochemical drift, oil-spills in coastal wetlands, and acid mine drainage—were selected based on joint documentation of a contamination source or exposure condition, a measurable plant response, and a remotely detected signal related to vegetation stress. This synthesis clarifies how vegetation response can serve as a proxy for large-scale contamination screening. A common contamination–vegetation–remote sensing logic emerges across the cases examined, linking contamination drivers and exposure pathways to canopy responses detectable as spectral, thermal, spatial, or temporal anomalies. Recurring spatial patterns of vegetation anomalies are identified as first-order interpretive cues rather than diagnostic signatures. Vegetation stress responses lack spectral specificity, making spectral equifinality the primary operational challenge for vegetation-based biomonitoring. Future workflows should integrate spectral, spatial, temporal, multi-sensor, and ancillary data validated against ground-truth. Future research should expand the evidence base through formal literature searches, multi-site validation, standardized protocols, and infrastructures integrating plant phenotyping and remote sensing to support quantitatively validated screening approaches.","本叙述性综述探讨了如何将植物生物指示与遥感相结合，以支持土壤污染的初步筛查，并将其构建为一个通过多尺度影像和实地验证来解决的环境态势感知问题。基于对污染源或暴露条件的联合记录、可测量的植物响应以及与植被胁迫相关的遥感信号，选取了七个案例研究——非法废物堆放、管道泄漏、垃圾填埋场影响、工业区、农用化学品飘移、沿海湿地溢油和酸性矿山排水。本综述阐明了植被响应如何能够作为大规模污染筛查的替代指标。在所考察的案例中，浮现出一种共同的污染—植被—遥感逻辑，将污染驱动因素和暴露途径与可被检测为光谱、热、空间或时间异常的冠层响应联系起来。植被异常的重复性空间格局被识别为一级解释线索，而非诊断性特征。植被胁迫响应缺乏光谱特异性，使得光谱等终性成为基于植被的生物监测面临的首要操作挑战。未来的工作流程应整合光谱、空间、时间、多传感器和辅助数据，并以地面真值进行验证。未来研究应通过正式文献检索、多站点验证、标准化协议以及整合植物表型分析与遥感的基础设施来扩展证据基础，以支持经过定量验证的筛查方法。","Remote Sensing","2026-09-19T00:00:00Z",77,{"impact":104,"substance":360,"depth":80,"authority":17,"freshness":16,"relevant":21,"comment":437},"综述性论文，系统梳理植被生物指示与遥感融合筛查土壤污染的七个案例，方法学与结论对农业环境遥感监测有参考价值，但非全国性政策或突破性成果。",[439],{"name":433,"url":430},[26,27,441,442,443],"环境监测","植被监测","土壤污染",{"code":38,"slug":89,"name":90,"path":91,"parent_code":92},[],{"status":100,"locked":13,"version":33,"method":34,"evidence":447},{"candidates":448,"agricultural_evidence":39},[449,453],{"code":38,"score":173,"supported":39,"title":450,"abstract":451,"tags":452},[41],[27,41],[26,442,27],{"code":115,"score":66,"supported":39,"title":454,"abstract":455,"tags":456},[],[119,120],[],[458,459],"植被生物指示 遥感 土壤污染","Remote Sensing 植被胁迫 遥感监测","植被生物指示遥感土壤污染-3166","10.3390\u002Frs18183227",{"doi":461,"openalex_id":463,"authors":464,"venue":433,"cited_by_count":61,"oa_url":430,"card":483,"direction":62,"ingested_from":63},"W7213930509",[465,468,471,474,477,480],{"name":466,"orcid":467},"Marco De Mizio","https:\u002F\u002Forcid.org\u002F0009-0009-6556-2606",{"name":469,"orcid":470},"Donato Amitrano","https:\u002F\u002Forcid.org\u002F0000-0002-2355-4503",{"name":472,"orcid":473},"Massimiliano Gargiulo","https:\u002F\u002Forcid.org\u002F0000-0002-6783-366X",{"name":475,"orcid":476},"Sara Parrilli","https:\u002F\u002Forcid.org\u002F0000-0002-7225-1269",{"name":478,"orcid":479},"Claudia Savarese","https:\u002F\u002Forcid.org\u002F0000-0003-0161-2407",{"name":481,"orcid":482},"Massimiliano Lega","https:\u002F\u002Forcid.org\u002F0000-0002-4842-6049",{"tldr":484,"method":485,"finding":486,"direction":62,"opportunity":487},"综述植物生物指示与遥感结合用于土壤污染初步筛查的七个案例，提出污染-植被-遥感逻辑。","叙事性综述，选取七类污染案例，整合多尺度影像与地面验证。","植被胁迫光谱缺乏特异性，光谱等终性是植被生物监测的主要操作挑战。","可构建标准化植物表型-遥感集成平台，开展多站点验证以量化污染筛查。","2026-09-22T23:30:20.917796Z"]