[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2797":3,"related-2797":110},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":109},2797,"Generation of representative datasets of future Copernicus Sentinel Expansion Mission Data (hyperspectral, thermal and L-band) as basis for innovative agricultural products","https:\u002F\u002Fdoi.org\u002F10.62880\u002Frars26005","The Copernicus Sentinel Expansion Missions will provide new and unique remote sensing data. To enable rapid use of real data as soon as it becomes available, it is essential to generate comparable synthetic data in advance. This study proposes a novel data set for three of the upcoming sensors. CHIME hyperspectral data are generated by inverting multispectral reflectance data from Sentinel-2 time series by radiative transfer modelling to retrieve land surface parameters and subsequently forward-simulating bottom-of-atmosphere reflectance using expected CHIME sensor characteristics. Future LSTM land surface temperature data are derived from Sentinel-3 and Sentinel-2 data using the Sen-ET workflow with spatial data mining sharpening. L-band backscatter and coherence data for ROSE-L are simulated using SAOCOM-1 data, which are transformed to match the expected spatial and radiometric characteristics. The novel data set is available for three areas of interest (AOIs) defined by Sentinel-2 tiles located in Germany, Belgium, and Estonia. A validation of simulated CHIME data using existing comparable sensor data from EnMAP showed a high spectral correlation with an average RMSE of 6.154 [%] and a correlation of 0.924 for the German AOI in 2024. This publicly available, unique and well validated dataset already enables the preparation and development of future products and services across a wide range of application areas based on data from the Sentinel Expansion Mission. Due to the high data availability resulting from extensive two-year time series, as well as the various AOIs, future products can already be tested for their temporal and spatial transferability.","哥白尼哨兵扩展任务将提供新的独特遥感数据。为了在真实数据可用时尽快加以利用，必须提前生成可比的合成数据。本研究为其中三个即将发射的传感器提出了一个新的数据集。CHIME高光谱数据通过辐射传输建模对来自Sentinel-2时间序列的多光谱反射率数据进行反演，以获取地表参数，随后利用预期的CHIME传感器特征正向模拟大气底层反射率来生成。未来的LSTM地表温度数据利用Sen-ET工作流结合空间数据挖掘锐化方法，从Sentinel-3和Sentinel-2数据中导出。ROSE-L的L波段后向散射和相干性数据使用SAOCOM-1数据进行模拟，并将其转换为符合预期空间和辐射特征的形式。该新数据集可用于三个感兴趣区域（AOIs），分别位于德国、比利时和爱沙尼亚的Sentinel-2瓦片范围内。利用EnMAP现有可比传感器数据对模拟CHIME数据进行的验证表明，2024年德国AOI的光谱相关性较高，平均RMSE为6.154 [%]，相关系数为0.924。这一公开可用、独特且经过充分验证的数据集，已经能够支持基于哨兵扩展任务数据在广泛的应用领域中准备和开发未来产品与服务。由于两年广泛时间序列所带来的高数据可用性以及多个AOIs，未来产品已经可以测试其时间和空间可迁移性。",null,"Recent advances in remote sensing.","2026-09-16T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,9,1,"面向未来Sentinel扩展任务的高光谱、热红外与L波段合成数据集研究，方法新颖、验证充分且公开可用，对农业遥感产品预研具有实质价值，值得进入每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"农业遥感","高光谱","遥感","地表温度","哥白尼计划","合成数据",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46],{"code":34,"score":47,"supported":48,"title":49,"abstract":51,"tags":54},20,true,[50],"hyperspectral",[29,52,53,50],"remote sensing","multispectral",[27,30,29,28],[56,57],"哥白尼计划 农业遥感 合成数据 地表温度","哥白尼计划 农业遥感","哥白尼计划农业遥感合成数据地表温度-2797",2,"10.62880\u002Frars26005",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7213413531",[64,66,68,71,74,77,80,83,85,88,90,92,94,96,99],{"name":65,"orcid":9},"Christian Miesgang",{"name":67,"orcid":9},"Sandra Dotzler",{"name":69,"orcid":70},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":72,"orcid":73},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":75,"orcid":76},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":78,"orcid":79},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":81,"orcid":82},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":84,"orcid":9},"Louis Snyders",{"name":86,"orcid":87},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":89,"orcid":9},"Sven Kautlenbach",{"name":91,"orcid":9},"Catherine Odera",{"name":93,"orcid":9},"Tetiana Shtym",{"name":95,"orcid":9},"Tanel Tamm",{"name":97,"orcid":98},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":100,"orcid":9},"Melisa Soledad Heredia",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"生成CHIME高光谱、LSTM热红外和ROSE-L L波段模拟数据集，为未来Sentinel扩展任务","辐射传输模型反演、Sen-ET时空锐化、SAOCOM-1模拟，覆盖德比爱三区两年","模拟CHIME与EnMAP光谱相关性0.924，RMSE 6.154%，数据集公开且验证良好。","农业遥感与作物表型","可基于该模拟数据集提前开发高光谱、热红外与L波段融合的作物监测和表型反演新算法。","openalex","2026-09-17T23:30:37.404847Z",{"total":111,"page":22,"page_size":111,"items":112},6,[113,188,242,292,345,436],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":125,"tags":127,"primary_topic":131,"secondary_topics":132,"classification":138,"search_phrases":172,"slug":175,"view_count":101,"doi":176,"paper":177,"created_at":187},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":123,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":124},12,"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。",[126],{"name":119,"url":116},[27,29,128,129,130],"水资源管理","灌溉农业","盐渍化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[133],{"code":134,"slug":135,"name":136,"path":137,"parent_code":38},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":41,"locked":14,"version":42,"method":43,"evidence":139},{"candidates":140,"agricultural_evidence":48},[141,146,152,160,167],{"code":34,"score":142,"supported":48,"title":143,"abstract":144,"tags":145},16,[52],[29,52],[27,29],{"code":134,"score":147,"supported":48,"title":148,"abstract":150,"tags":151},11,[149],"GIS",[149],[],{"code":153,"score":154,"supported":48,"title":155,"abstract":156,"tags":159},"H2",7,[],[157,158],"土壤","soil",[130],{"code":161,"score":162,"supported":48,"title":163,"abstract":164,"tags":166},"H7",3,[],[165],"干旱",[],{"code":168,"score":59,"supported":14,"title":169,"abstract":170,"tags":171},"H3",[],[],[128,129],[173,174],"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":176,"openalex_id":178,"authors":179,"venue":119,"cited_by_count":101,"oa_url":116,"card":182,"direction":106,"ingested_from":108},"W7221819099",[180],{"name":181,"orcid":9},"Akbarov Sarvarbek",{"tldr":183,"method":184,"finding":185,"direction":106,"opportunity":186},"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":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":195,"score_detail":196,"sources":200,"tags":202,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":216,"slug":219,"view_count":162,"doi":220,"paper":221,"created_at":241},5007,"Kernel-Driven Model Correction of Thermal Directionality: Pixel-Level Analysis Using Airborne Multi-Angular Observations","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203525","Directional anisotropy (DA) in thermal infrared (TIR) remote sensing introduces significant variability in land surface temperature (LST) retrievals, particularly in high-resolution observations. This study presents the empirical pixel-level modelling of DA, by using multi-angular airborne data from the NETSense campaign over an agricultural area near Grosseto, Italy. Six kernel-driven models (KDMs) are evaluated for their ability to fit directional effects, with the Vinnikov–Roujean–Lagouarde (Vinnikov–RL) model achieving the highest accuracy (RMSE = 1.07 K). At 40 m resolution, modelled directional effects reach median deviations of +1.80 K at the hotspot position, −2.03 K opposite to the Sun, and −1.37 K perpendicular to the solar principal plane, with extremes up to ±6 K, highlighting their operational significance for missions such as LSTM, TRISHNA, and SBG. To improve operational feasibility, two simplified models—adapted RL (RLa) and Vinnikov–RLa—are introduced, leveraging a consistent inverse relationship between hotspot magnitude and width to reduce parameter complexity via a power-law formulation. The analysis reveals that directional effects vary with spatial resolution and are influenced by canopy structure and solar geometry, while biophysical parameters alone are insufficient for coefficient estimation. Validation using an independent flight day confirms the robustness of the simplified models under different acquisition geometries, surface types, and geophysical conditions. A tiered model strategy is proposed for future satellite missions, recommending model selection based on day or night observations and data availability. These findings support the integration of directional correction into operational pipelines, enabling consistent and accurate LST products across platforms, resolutions, and observation conditions.","热红外（TIR）遥感中的方向各向异性（DA）会导致地表温度（LST）反演出现显著变化，在高分辨率观测中尤为明显。本研究利用意大利格罗塞托附近农业区NETSense campaign的多角度机载数据，开展了像元尺度的DA经验建模。评估了六种核驱动模型（KDM）拟合方向效应的能力，其中Vinnikov–Roujean–Lagouarde（Vinnikov–RL）模型精度最高（RMSE = 1.07 K）。在40 m分辨率下，模拟的方向效应在热点位置达到中位偏差+1.80 K，背阳方向为−2.03 K，垂直于太阳主平面方向为−1.37 K，极值可达±6 K，凸显了其对LSTM、TRISHNA和SBG等任务的实际意义。为提高业务可行性，引入了两种简化模型——改进RL（RLa）和Vinnikov–RLa，利用热点幅度与宽度之间一致的负相关关系，通过幂律公式降低参数复杂度。分析表明，方向效应随空间分辨率变化，并受冠层结构和太阳几何影响，而仅凭生物物理参数不足以估计系数。利用独立飞行日的验证证实了简化模型在不同观测几何、地表类型和地球物理条件下的稳健性。提出了面向未来卫星任务的分层模型策略，建议根据白天或夜间观测及数据可用性选择模型。这些发现支持将方向校正集成到业务化流程中，从而在不同平台、分辨率和观测条件下实现一致且准确的LST产品。","Remote Sensing",83,{"impact":17,"substance":197,"depth":19,"authority":198,"freshness":21,"relevant":22,"comment":199},23,14,"基于航空多角度观测的热红外方向性建模研究，方法新颖、数据扎实，对LSTM、TRISHNA等农业遥感卫星任务的地表温度产品校正具有直接参考价值。",[201],{"name":194,"url":191},[203,29,30,204,205],"LSTM","热红外","TRISHNA",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":209},{"candidates":210,"agricultural_evidence":48},[211],{"code":34,"score":212,"supported":48,"title":213,"abstract":214,"tags":215},8,[],[29,52],[30,29],[217,218],"NETSense 航空 多角度 热红外","LSTM TRISHNA SBG 地表温度","NETSense航空多角度热红外-5007","10.3390\u002Frs18203525",{"doi":220,"openalex_id":222,"authors":223,"venue":194,"cited_by_count":101,"oa_url":191,"card":236,"direction":106,"ingested_from":108},"W7221818626",[224,226,227,230,233],{"name":84,"orcid":225},"https:\u002F\u002Forcid.org\u002F0000-0003-1093-2517",{"name":78,"orcid":79},{"name":228,"orcid":229},"J. León-Tavares","https:\u002F\u002Forcid.org\u002F0000-0002-6186-9936",{"name":231,"orcid":232},"Mary Langsdale","https:\u002F\u002Forcid.org\u002F0000-0002-6078-0702",{"name":234,"orcid":235},"Lorenzo Genesio","https:\u002F\u002Forcid.org\u002F0000-0001-9265-886X",{"tldr":237,"method":238,"finding":239,"direction":106,"opportunity":240},"基于机载多角度热红外数据，在像素尺度评估核驱动模型并简化，以校正地表温度方向性效应。","使用NETSense机载多角度热红外数据，评估六种核驱动模型并构建简化模型。","Vinnikov–RL模型精度最高，方向性偏差中值达+1.80K至-2.03K，极端达±6K。","可探索方向性校正与作物冠层结构参数耦合，提升卫星LST产品在农业监测中的一致性。","2026-10-15T23:30:29.753962Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":119,"source_url":245,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":253,"tags":255,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":274,"slug":277,"view_count":101,"doi":278,"paper":279,"created_at":291},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":212,"substance":17,"depth":251,"authority":123,"freshness":21,"relevant":22,"comment":252},15,"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。",[254],{"name":119,"url":245},[27,256,29,257,258],"Sentinel-2","乌兹别克斯坦","光谱波段",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":262},{"candidates":263,"agricultural_evidence":48},[264,269],{"code":34,"score":265,"supported":48,"title":266,"abstract":267,"tags":268},17,[52],[29,52],[256,27,29],{"code":134,"score":162,"supported":48,"title":270,"abstract":271,"tags":273},[],[272],"地理信息",[],[275,276],"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":278,"openalex_id":280,"authors":281,"venue":119,"cited_by_count":101,"oa_url":245,"card":286,"direction":106,"ingested_from":108},"W7221467289",[282,284],{"name":283,"orcid":9},"Xalilov Doniyor",{"name":285,"orcid":9},"Allambergenova Venera",{"tldr":287,"method":288,"finding":289,"direction":106,"opportunity":290},"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":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":9,"source_name":119,"source_url":295,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":308,"secondary_topics":309,"classification":310,"search_phrases":328,"slug":331,"view_count":22,"doi":332,"paper":333,"created_at":344},4677,"Geospatial Analysis of Climate-Driven Landscape Change and Urban Expansion: Assessing Human–Environment Interactions, Resource Pressure, and Sustainable Spatial Planning","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280111","The increasing demands and changing features of rapidly expanding cities and the impacts on land cover and the surface energy balance are evident. Yet, urban planners need to differentiate between the changes that have occurred and those which have been predicted. This study offers another geospatial analysis of the Delhi National Capital Region. This study complements previously published studies by the use of remote sensing techniques to retrieve land surface temperatures and studies of urban resource pressures. This study also incorporates land use and land cover studies of the years 2003, 2013 and 2023, which are based on the analysis of the Landsat imagery. The primary land use and land cover study analyzed an area of 13,754 km2 that includes the entirety of the Delhi National Capital Region and portions of the districts of Gurugram, Faridabad, Sonipat, Jhajjar, Ghaziabad, Gautam Buddha Nagar and Ghaziabad. This study also carried out a reanalysis of published land use and land cover maps to compute the absolute and relative changes and the annual growth rates. The area of land cover of built-up land increased from 1,893.64 km2 (13.77%) in 2003 to 3,386.76 km2 (24.62%) in the year 2023, which is an increase of 1,493.12 km (78.85%. CAGR 2.95%). Decreases were observed in the land cover of agricultural land, forests, water bodies and open land. The rate of land cover of built-up land slowed from an annual rate of 4.37% during 2003 to 2013, to an annual rate of 1.55% during the 2013 to 2023 period. The peripheral Shannon Entropy increased from a value of 0.24 to 0.36 during the same periods. The average land surface temperature of built-up land in Delhi, as reported independently of this study, increased by 5.23°C from 36.58°C in the year 2001 to 41.81°C in the year 2021. The integrated findings indicate that land that was previously agricultural and ecological cover has been extensively converted into built-up areas, intensifying surface warming and resource pressure across the urban periphery and underscoring the urgent need for sustainable spatial planning.","快速扩张的城市日益增长的需求和不断变化的特征，以及对土地覆盖和地表能量平衡的影响显而易见。然而，城市规划者需要区分已经发生的变化和预测的变化。本研究提供了对德里国家首都辖区的另一项地理空间分析。本研究通过利用遥感技术反演地表温度以及研究城市资源压力，对先前发表的研究进行了补充。本研究还纳入了基于Landsat影像分析的2003年、2013年和2023年的土地利用与土地覆盖研究。主要的土地利用与土地覆盖研究分析了13,754 km²的区域，涵盖德里国家首都辖区全境以及古尔冈、法里达巴德、索尼帕特、贾贾尔、加济阿巴德、乔达摩菩提那加尔和加济阿巴德等地区的部分区域。本研究还对已发表的土地利用与土地覆盖图进行了再分析，以计算绝对变化量、相对变化量及年增长率。建设用地土地覆盖面积从2003年的1,893.64 km²（13.77%）增加至2023年的3,386.76 km²（24.62%），增加了1,493.12 km²（78.85%，复合年增长率2.95%）。农业用地、森林、水体和裸地的土地覆盖均有所减少。建设用地土地覆盖的增长速率从2003年至2013年的年均4.37%放缓至2013年至2023年的年均1.55%。同期，边缘香农熵从0.24增加至0.36。据独立于本研究的报道，德里建设用地平均地表温度从2001年的36.58°C上升至2021年的41.81°C，升高了5.23°C。综合研究结果表明，原有的农业和生态用地已被大量转化为建设用地，加剧了城市边缘的地表增温和资源压力，凸显了可持续空间规划的迫切需求。","2026-10-10T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":212,"relevant":22,"comment":301},"基于Landsat遥感数据量化德里首都圈2003-2023年建设用地扩张与地表升温，方法扎实、数据具体，对农业信息化中的遥感监测与城乡空间规划有参考价值。",[303],{"name":119,"url":295},[29,305,30,306,307],"土地利用","城市扩张","空间规划",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":311},{"candidates":312,"agricultural_evidence":48},[313,317,323],{"code":34,"score":212,"supported":48,"title":314,"abstract":315,"tags":316},[],[29,52],[30,29],{"code":134,"score":318,"supported":48,"title":319,"abstract":320,"tags":322},4,[],[321],"空间分析",[307],{"code":324,"score":22,"supported":14,"title":325,"abstract":326,"tags":327},"H6",[],[],[305],[329,330],"城市扩张 地表温度 遥感","土地利用 地表温度 城市扩张 空间规划","城市扩张地表温度遥感-4677","10.5281\u002Fzenodo.23280111",{"doi":332,"openalex_id":334,"authors":335,"venue":119,"cited_by_count":101,"oa_url":295,"card":339,"direction":106,"ingested_from":108},"W7221308370",[336],{"name":337,"orcid":338},"Amit Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0008-5573-0525",{"tldr":340,"method":341,"finding":342,"direction":106,"opportunity":343},"基于Landsat遥感分析德里首都区2003-2023年土地利用变化、地表温度与城市扩张，评估资源压","Landsat影像解译、土地利用\u002F覆盖分类、地表温度反演、Shannon熵与增长","建设用地增78.85%，农林业与水体减少，地表升温5.23°C，城市外围资源压力加剧。","可结合多源遥感与作物模型，量化城市扩张对周边农地生产力与粮食安全的时空影响。","2026-10-11T23:30:30.090512Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":15,"score_detail":353,"sources":355,"tags":357,"primary_topic":361,"secondary_topics":365,"classification":367,"search_phrases":399,"slug":402,"view_count":22,"doi":403,"paper":404,"created_at":435},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":17,"substance":18,"depth":17,"authority":198,"freshness":21,"relevant":22,"comment":354},"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。",[356],{"name":351,"url":348},[358,359,28,29,360],"智慧农业","农业人工智能","土壤监测",{"code":153,"slug":158,"name":362,"path":363,"parent_code":364},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[366],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":368},{"candidates":369,"agricultural_evidence":48},[370,374,378,385,392],{"code":153,"score":198,"supported":48,"title":371,"abstract":372,"tags":373},[158],[157,158],[],{"code":34,"score":20,"supported":48,"title":375,"abstract":376,"tags":377},[50],[50],[29,28],{"code":379,"score":154,"supported":48,"title":380,"abstract":381,"tags":384},"I1",[],[382,383],"精准农业","precision agriculture",[358],{"code":386,"score":154,"supported":48,"title":387,"abstract":388,"tags":391},"I2",[],[389,390],"机器学习","machine learning",[359],{"code":393,"score":111,"supported":48,"title":394,"abstract":395,"tags":398},"F3",[],[396,397],"施肥","fertilization",[],[400,401],"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":403,"openalex_id":405,"authors":406,"venue":351,"cited_by_count":101,"oa_url":428,"card":429,"direction":434,"ingested_from":108},"W7220738771",[407,410,413,416,419,422,425],{"name":408,"orcid":409},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":411,"orcid":412},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":414,"orcid":415},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":417,"orcid":418},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":420,"orcid":421},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":423,"orcid":424},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":426,"orcid":427},"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":430,"method":431,"finding":432,"direction":106,"opportunity":433},"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。","智慧农业 \u002F 农业物联网","2026-10-07T23:30:09.147409Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":444,"score_detail":445,"sources":447,"tags":449,"primary_topic":452,"secondary_topics":456,"classification":458,"search_phrases":494,"slug":497,"view_count":498,"doi":499,"paper":500,"created_at":522},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":18,"depth":17,"authority":198,"freshness":212,"relevant":22,"comment":446},"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。",[448],{"name":442,"url":439},[358,27,450,28,451],"深度学习","作物表型",{"code":386,"slug":453,"name":454,"path":455,"parent_code":38},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[457],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":459},{"candidates":460,"agricultural_evidence":48},[461,466,470,477,484,490],{"code":386,"score":251,"supported":48,"title":462,"abstract":464,"tags":465},[463],"deep learning",[450,463],[450],{"code":34,"score":123,"supported":48,"title":467,"abstract":468,"tags":469},[],[29,52,50],[451,27,28],{"code":471,"score":111,"supported":48,"title":472,"abstract":473,"tags":476},"A1",[],[474,475],"水稻","rice",[],{"code":478,"score":111,"supported":48,"title":479,"abstract":480,"tags":483},"B1",[],[481,482],"番茄","tomato",[],{"code":485,"score":162,"supported":48,"title":486,"abstract":487,"tags":489},"I7",[],[488],"methodological framework",[],{"code":379,"score":22,"supported":14,"title":491,"abstract":492,"tags":493},[],[],[358],[495,496],"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",5,"10.1016\u002Fj.aiia.2026.08.014",{"doi":499,"openalex_id":501,"authors":502,"venue":442,"cited_by_count":101,"oa_url":439,"card":517,"direction":106,"ingested_from":108},"W7215067733",[503,505,507,510,513,515],{"name":504,"orcid":9},"Mengqi Zhang",{"name":506,"orcid":9},"Jiaren Zhou",{"name":508,"orcid":509},"Qingfeng Song","https:\u002F\u002Forcid.org\u002F0000-0003-4314-6897",{"name":511,"orcid":512},"Man Zhang","https:\u002F\u002Forcid.org\u002F0009-0007-4481-0514",{"name":514,"orcid":9},"Xinguang Zhu",{"name":516,"orcid":9},"Minjuan Wang",{"tldr":518,"method":519,"finding":520,"direction":106,"opportunity":521},"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"]