[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3507":3,"related-3507":113},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":112},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。",null,"Geophysical Research Letters","2026-09-23T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,15,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","遥感","水资源利用","生态监测","碳汇",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":63},[38,45,53,58],{"code":39,"score":20,"supported":40,"title":41,"abstract":42,"tags":44},"I3",true,[],[27,43],"remote sensing",[26,27],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":52},"H7",6,[],[50,51],"干旱","drought",[],{"code":54,"score":21,"supported":14,"title":55,"abstract":56,"tags":57},"H3",[],[],[28],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"H6",[],[],[29],"农业相关性需要复核",[65,66],"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":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":103,"oa_url":104,"card":105,"direction":109,"ingested_from":111},"W7214122316",[73,76,79,82,85,88,91,94,97,100],{"name":74,"orcid":75},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":77,"orcid":78},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":80,"orcid":81},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":83,"orcid":84},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":86,"orcid":87},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":89,"orcid":90},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":92,"orcid":93},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":95,"orcid":96},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":98,"orcid":99},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":101,"orcid":102},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"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。","农业遥感与作物表型","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。","openalex","2026-09-25T23:30:33.881463Z",{"total":47,"page":21,"page_size":47,"items":114},[115,195,244,298,338,401],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":9,"source_name":121,"source_url":118,"published_at":122,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":123,"score_detail":124,"sources":129,"tags":131,"primary_topic":135,"secondary_topics":140,"classification":146,"search_phrases":179,"slug":182,"view_count":103,"doi":183,"paper":184,"created_at":194},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":125,"substance":18,"depth":17,"authority":126,"freshness":127,"relevant":21,"comment":128},12,13,9,"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。",[130],{"name":121,"url":118},[26,27,132,133,134],"水资源管理","灌溉农业","盐渍化",{"code":39,"slug":136,"name":137,"path":138,"parent_code":139},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[141],{"code":142,"slug":143,"name":144,"path":145,"parent_code":139},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":147,"locked":14,"version":34,"method":35,"evidence":148},"classified",{"candidates":149,"agricultural_evidence":40},[150,155,161,169,174],{"code":39,"score":151,"supported":40,"title":152,"abstract":153,"tags":154},16,[43],[27,43],[26,27],{"code":142,"score":156,"supported":40,"title":157,"abstract":159,"tags":160},11,[158],"GIS",[158],[],{"code":162,"score":163,"supported":40,"title":164,"abstract":165,"tags":168},"H2",7,[],[166,167],"土壤","soil",[134],{"code":46,"score":170,"supported":40,"title":171,"abstract":172,"tags":173},3,[],[50],[],{"code":54,"score":175,"supported":14,"title":176,"abstract":177,"tags":178},2,[],[],[132,133],[180,181],"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":183,"openalex_id":185,"authors":186,"venue":121,"cited_by_count":103,"oa_url":118,"card":189,"direction":109,"ingested_from":111},"W7221819099",[187],{"name":188,"orcid":9},"Akbarov Sarvarbek",{"tldr":190,"method":191,"finding":192,"direction":109,"opportunity":193},"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":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":121,"source_url":198,"published_at":201,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":205,"tags":207,"primary_topic":211,"secondary_topics":212,"classification":213,"search_phrases":226,"slug":229,"view_count":103,"doi":230,"paper":231,"created_at":243},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":20,"substance":17,"depth":19,"authority":125,"freshness":127,"relevant":21,"comment":204},"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。",[206],{"name":121,"url":198},[26,208,27,209,210],"Sentinel-2","乌兹别克斯坦","光谱波段",{"code":39,"slug":136,"name":137,"path":138,"parent_code":139},[],{"status":147,"locked":14,"version":34,"method":35,"evidence":214},{"candidates":215,"agricultural_evidence":40},[216,221],{"code":39,"score":217,"supported":40,"title":218,"abstract":219,"tags":220},17,[43],[27,43],[208,26,27],{"code":142,"score":170,"supported":40,"title":222,"abstract":223,"tags":225},[],[224],"地理信息",[],[227,228],"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":230,"openalex_id":232,"authors":233,"venue":121,"cited_by_count":103,"oa_url":198,"card":238,"direction":109,"ingested_from":111},"W7221467289",[234,236],{"name":235,"orcid":9},"Xalilov Doniyor",{"name":237,"orcid":9},"Allambergenova Venera",{"tldr":239,"method":240,"finding":241,"direction":109,"opportunity":242},"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":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":9,"secondary_topics":262,"classification":263,"search_phrases":275,"slug":278,"view_count":21,"doi":279,"paper":280,"created_at":297},4364,"Multi-model comparison of satellite remote sensing-driven GPP models and products in a temperate deciduous forest","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecoinf.2026.104077","Accurate estimation of terrestrial gross primary productivity (GPP) is essential for understanding forest carbon dynamics, yet satellite remote sensing-driven GPP products often differ in their ability to capture seasonal changes in ecosystem productivity. This study evaluated five GPP estimates, including MODIS MOD17, a site-calibrated light use efficiency (LUE) model, the Vegetation Photosynthesis Model (VPM), the Temperature-Greenness (TG) model, and SMAP Level 4 Carbon, against six years of eddy covariance-derived GPP from a temperate deciduous forest in the Daniel Boone National Forest, Kentucky, USA. MODIS and SMAP were evaluated as operational products without site-specific recalibration, while LUE, VPM, and TG were evaluated as site-calibrated diagnostic models using leave-one-year-out validation. Under common-row evaluation, SMAP, VPM, and TG showed the strongest overall agreement with EC-derived GPP, with SMAP producing the lowest RMSE, highest R 2 , and highest NSE. However, seasonal diagnostics revealed important differences in model behavior. All five estimates underestimated summer GPP during peak canopy activity, while all models overestimated GPP during the fall transition. Winter performance was weak across models, but bias direction was mixed because EC-derived winter GPP was close to zero. Residual-driver diagnostics indicated that summer underestimation was associated with high canopy activity and atmospheric demand, especially EVI and VPD gradients, while fall overestimation was associated with radiation, canopy, temperature, and phenological gradients during senescence. A supplementary MODIS-only FLUXNET assessment supported the broader relevance of summer MODIS underestimation across deciduous broadleaf forest sites. These findings indicate that improving satellite GPP estimates in temperate deciduous forests requires season-specific attention to peak-season stress responses, senescence-period downregulation, and low-GPP winter conditions.","准确估算陆地总初级生产力（GPP）对于理解森林碳动态至关重要，然而卫星遥感驱动的GPP产品在捕捉生态系统生产力季节性变化方面的能力往往存在差异。本研究评估了五种GPP估算结果，包括MODIS MOD17、站点校准的光能利用率（LUE）模型、植被光合作用模型（VPM）、温度-绿度（TG）模型以及SMAP Level 4 Carbon，并以美国肯塔基州丹尼尔·布恩国家森林一片温带落叶林六年涡度协方差（EC）衍生的GPP为参考。MODIS和SMAP作为业务化产品进行评估，未进行站点特异性重校准；而LUE、VPM和TG则作为站点校准的诊断模型，采用留一年交叉验证进行评估。在共同行评估下，SMAP、VPM和TG与EC衍生GPP的总体一致性最强，其中SMAP的RMSE最低、R²最高、NSE最高。然而，季节性诊断揭示了模型行为的重要差异。所有五种估算在冠层活动高峰期均低估了夏季GPP，而所有模型在秋季过渡期均高估了GPP。冬季表现普遍较弱，但偏差方向不一致，因为EC衍生的冬季GPP接近于零。残差驱动因子诊断表明，夏季低估与高冠层活动和大气需求相关，尤其是EVI和VPD梯度；而秋季高估则与衰老期的辐射、冠层、温度和物候梯度相关。一项仅针对MODIS的FLUXNET补充评估支持了夏季MODIS低估在落叶阔叶林站点中具有更广泛的普遍性。这些发现表明，改进温带落叶林的卫星GPP估算需要针对特定季节关注旺季胁迫响应、衰老期下调以及低GPP冬季条件。","Ecological Informatics","2026-10-05T00:00:00Z",70,{"impact":20,"substance":18,"depth":17,"authority":254,"freshness":20,"relevant":21,"comment":255},14,"多模型对比评估卫星遥感GPP产品在温带落叶林的表现，方法严谨、结论具体，对农业碳汇遥感监测有参考价值，但属细分领域学术进展，公共影响有限。",[257],{"name":250,"url":247},[27,30,259,260,261],"生态模型","GPP估算","落叶林",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":264},{"candidates":265,"agricultural_evidence":14,"reason":63},[266,270],{"code":39,"score":19,"supported":40,"title":267,"abstract":268,"tags":269},[43],[27,43],[27],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"H4",[],[],[30],[276,277],"Daniel Boone 落叶林 碳通量","生态模型 落叶林 GPP估算 碳汇","DanielBoone落叶林碳通量-4364","10.1016\u002Fj.ecoinf.2026.104077",{"doi":279,"openalex_id":281,"authors":282,"venue":250,"cited_by_count":103,"oa_url":291,"card":292,"direction":109,"ingested_from":111},"W7220461078",[283,285,287,289],{"name":284,"orcid":9},"Ife Familusi",{"name":286,"orcid":9},"Maheteme Gebremedhin",{"name":288,"orcid":9},"Buddhi Gyawali",{"name":290,"orcid":9},"Utkarsha Basnet","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1574954126004851\u002Fpdf",{"tldr":293,"method":294,"finding":295,"direction":109,"opportunity":296},"对比五种卫星遥感GPP模型在温带落叶林的表现，揭示季节偏差规律。","用6年涡度协方差GPP数据，评估MODIS、LUE、VPM、TG和SMAP模型。","所有模型低估夏季GPP、高估秋季GPP，SMAP整体表现最佳。","可开发季节特异性GPP模型，重点改进峰值期胁迫响应和衰老期下调机制。","2026-10-07T23:30:28.996554Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":9,"content":9,"source_name":303,"source_url":301,"published_at":304,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":308,"tags":310,"primary_topic":9,"secondary_topics":313,"classification":314,"search_phrases":321,"slug":324,"view_count":21,"doi":325,"paper":326,"created_at":337},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","Acta Geophysica","2026-10-06T00:00:00Z",59,{"impact":20,"substance":254,"depth":19,"authority":126,"freshness":127,"relevant":21,"comment":307},"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。",[309],{"name":303,"url":301},[26,27,311,312],"洪水监测","突尼斯",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":315},{"candidates":316,"agricultural_evidence":14,"reason":63},[317],{"code":39,"score":126,"supported":40,"title":318,"abstract":319,"tags":320},[43],[43],[26,27],[322,323],"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":325,"openalex_id":327,"authors":328,"venue":303,"cited_by_count":103,"oa_url":9,"card":9,"direction":109,"ingested_from":111},"W7220716147",[329,332,335],{"name":330,"orcid":331},"Ahmed Ezzine","https:\u002F\u002Forcid.org\u002F0000-0002-7497-8197",{"name":333,"orcid":334},"Taoufik Hermassi","https:\u002F\u002Forcid.org\u002F0000-0002-8782-6192",{"name":336,"orcid":9},"Emna Kochlef","2026-10-07T23:30:26.088231Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":349,"tags":351,"primary_topic":355,"secondary_topics":356,"classification":362,"search_phrases":383,"slug":386,"view_count":20,"doi":387,"paper":388,"created_at":400},3618,"Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no1.2026.pg131.154","Remote sensing image segmentation is essential to extract valuable information from satellite and aerial images to achieve significant applications such as urban planning and ecological monitoring. Yet, it is hard to accurately segment diverse and complicated features because of the constraints of conventional approaches and the requirement to process variable object sizes and complicated boundaries. This research aims to address the above difficulties through the use and assessment of advanced deep learning models specifically UNet, SegNet, and TransU-Net in multi target semantic segmentation. These networks have been applied in single-object segmentation tasks with a focus on structures and roads, while the UNet architecture was additionally utilized for multi-object segmentation comprising buildings, woods, grasslands, water bodies, and cultivated land. Using appropriate remote sensing datasets, the accuracy of the models was thoroughly evaluated using common evaluation metrics, including pixel accuracy, Intersection over Union (IoU), and F1-score. The experimental outcomes illustrate the capabilities of these deep learning methods to provide accurate identification of essential features. Notably, the maximum attainable accuracy for single-object building segmentation was 96.85%, whereas the overall accuracy for multi-object segmentation with the UNet was 84.2%. The experimental results show these deep learning algorithms can effectively separate different targets from remote sensing imagery, thus making them fundamental tools for geospatial analysis and related fields.","遥感图像分割对于从卫星和航空图像中提取有价值信息，以实现城市规划和生态监测等重要应用至关重要。然而，由于传统方法的局限以及处理可变目标尺寸和复杂边界的需要，准确分割多样且复杂的地物十分困难。本研究旨在通过使用和评估先进的深度学习模型，特别是UNet、SegNet和TransU-Net在多目标语义分割中的应用，来解决上述难题。这些网络已被应用于单目标分割任务，重点关注建筑物和道路，而UNet架构还被额外用于多目标分割，包括建筑物、林地、草地、水体和耕地。利用适当的遥感数据集，通过常用评价指标对模型的精度进行了全面评估，包括像素精度、交并比（IoU）和F1分数。实验结果表明了这些深度学习方法在准确识别关键地物方面的能力。值得注意的是，单目标建筑物分割的最高可达精度为96.85%，而使用UNet进行多目标分割的总体精度为84.2%。实验结果表明，这些深度学习算法能够有效地从遥感图像中分离出不同目标，从而使其成为地理空间分析及相关领域的基础工具。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-25T00:00:00Z",63,{"impact":125,"substance":17,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":348},"将UNet、SegNet、TransU-Net用于多目标遥感语义分割，建筑单目标精度96.85%、多目标84.2%，方法常规但结论对农业与生态遥感监测有参考价值。",[350],{"name":344,"url":341},[352,353,354,27,29],"农业人工智能","深度学习","语义分割",{"code":39,"slug":136,"name":137,"path":138,"parent_code":139},[357],{"code":358,"slug":359,"name":360,"path":361,"parent_code":139},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":147,"locked":14,"version":34,"method":35,"evidence":363},{"candidates":364,"agricultural_evidence":40},[365,369,374,379],{"code":39,"score":19,"supported":40,"title":366,"abstract":367,"tags":368},[43],[27,43],[27],{"code":358,"score":127,"supported":40,"title":370,"abstract":371,"tags":373},[],[353,372],"deep learning",[352,353,354],{"code":142,"score":170,"supported":40,"title":375,"abstract":376,"tags":378},[],[377],"空间分析",[],{"code":59,"score":21,"supported":14,"title":380,"abstract":381,"tags":382},[],[],[29],[384,385],"UNet 遥感 多目标分割","TransU-Net 城市生态 遥感","UNet遥感多目标分割-3618","10.56201\u002Fijgem.vol.12.no1.2026.pg131.154",{"doi":387,"openalex_id":389,"authors":390,"venue":344,"cited_by_count":103,"oa_url":393,"card":394,"direction":399,"ingested_from":111},"W7214298981",[391],{"name":392,"orcid":9},"Chibueze Favour Aririguzo","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 1 2026\u002FMulti-Target Remote Sensing Segmentation 131-154.pdf",{"tldr":395,"method":396,"finding":397,"direction":109,"opportunity":398},"评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。","使用UNet、SegNet、TransU-Net模型，在遥感数据集上以像素精度、","单目标建筑分割最高精度96.85%，UNet多目标分割总体精度84.2%。","可探索跨尺度注意力机制提升多目标分割精度，并迁移至耕地与作物精细分类。","智慧农业 \u002F 农业物联网","2026-09-27T23:30:28.366758Z",{"id":402,"title":403,"url":404,"summary":405,"summary_zh":406,"content":9,"source_name":407,"source_url":404,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":417,"secondary_topics":418,"classification":424,"search_phrases":458,"slug":461,"view_count":462,"doi":463,"paper":464,"created_at":482},3554,"Spatial Estimation of Carbon Sequestration Using Remote Sensing and Its Application in Carbon-credit Assessment: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125","Carbon sequestration is fundamental to climate-change mitigation because terrestrial and aquatic ecosystems store carbon in vegetation, biomass, soils, and the Earth’s crust. Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.","碳固存是减缓气候变化的基础，因为陆地和水生生态系统将碳储存在植被、生物量、土壤和地壳中。可靠估算碳储量及其时空变化，对于制定气候政策、生态系统管理、碳核算和碳信用评估至关重要。传统野外测量可提供详细信息，但受采样要求、成本、劳动力、空间异质性以及连续监测局限性等因素制约。遥感提供了一种互补方法，能够在不同时空尺度上对植被特征、土地覆盖变化、生物量及其他碳相关变量进行空间连续观测。多光谱、高光谱、合成孔径雷达、激光雷达、热红外和高分辨率卫星观测的进步，拓展了碳估算能力。这些技术与地理信息系统、野外观测、统计技术和机器学习算法的集成，使得对地上生物量、地下生物量、土壤有机碳、森林碳、农业碳、红树林碳和蓝碳资源进行精确空间评估成为可能。然而，与传感器特性、野外数据质量、模型选择、空间变异性、时间动态和尺度转换相关的不确定性仍是公认的局限。将空间碳估算转化为碳信用，需要考虑基线条件、额外性、永久性、泄漏、不确定性、核查以及测量、报告和核查要求。本综述批判性地审视了遥感技术、碳估算模型、空间制图方法、机器学习技术、碳核算框架及其在碳信用评估中的集成。特别强调了多源数据融合、新兴地球观测技术、人工智能、不确定性评估和数字化MRV系统。识别了主要的方法学、技术和政策差距，以及开发透明、空间明确、科学稳健且可扩展的碳信用评估框架的机遇。","Journal of Geography Environment and Earth Science International",67,{"impact":125,"substance":17,"depth":151,"authority":125,"freshness":127,"relevant":21,"comment":410},"系统综述遥感碳汇估算与碳信用评估的方法、不确定性与数字MRV进展，对农业碳汇与碳交易信息化有参考价值，但属综述类论文，公共影响有限。",[412],{"name":407,"url":404},[414,27,30,415,416],"机器学习","碳信用","数字MRV",{"code":39,"slug":136,"name":137,"path":138,"parent_code":139},[419],{"code":271,"slug":420,"name":421,"path":422,"parent_code":423},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":147,"locked":14,"version":34,"method":35,"evidence":425},{"candidates":426,"agricultural_evidence":40},[427,433,439,444,448,453],{"code":39,"score":17,"supported":40,"title":428,"abstract":429,"tags":432},[43],[27,43,430,431],"multispectral","hyperspectral",[27],{"code":271,"score":19,"supported":40,"title":434,"abstract":436,"tags":438},[435],"carbon sequestration",[437,435],"农业碳",[30],{"code":358,"score":163,"supported":40,"title":440,"abstract":441,"tags":443},[],[414,442],"artificial intelligence",[414],{"code":162,"score":47,"supported":40,"title":445,"abstract":446,"tags":447},[],[166,167],[],{"code":142,"score":47,"supported":40,"title":449,"abstract":450,"tags":452},[],[224,451],"geographic information",[],{"code":46,"score":170,"supported":40,"title":454,"abstract":455,"tags":457},[],[456],"气候变化",[],[459,460],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554",4,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":463,"openalex_id":465,"authors":466,"venue":407,"cited_by_count":103,"oa_url":404,"card":476,"direction":109,"ingested_from":111},"W7214392294",[467,469,472,474],{"name":468,"orcid":9},"Aishwarya Desai",{"name":470,"orcid":471},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":473,"orcid":9},"Sangita Shinde",{"name":475,"orcid":9},"Sachinkumar Nandgude",{"tldr":477,"method":478,"finding":479,"direction":480,"opportunity":481},"综述遥感碳储量估算技术及其在碳信用评估中的应用与挑战。","综述多光谱、高光谱、SAR、LiDAR等遥感与GIS、机器学习融合方法。","遥感可空间连续估算多类碳库，但不确定性及MRV要求制约碳信用转化。","农业绿色发展与碳","可研究多源数据融合与AI驱动的数字MRV，降低碳信用评估不确定性。","2026-09-26T23:30:30.002197Z"]