[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-Landsat":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"Landsat","tag",[4],4,1,100,[11,129,181,247],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":14,"published_at":19,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":23,"score_detail":24,"sources":31,"tags":33,"primary_topic":38,"secondary_topics":44,"classification":56,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":128},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。",null,"Eurasian Soil Science","2026-10-15T00:00:00Z","论文",10,false,69,{"impact":25,"substance":26,"depth":27,"authority":28,"freshness":29,"relevant":8,"comment":30},12,20,16,13,8,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[32],{"name":18,"url":14},[34,35,36,4,37],"机器学习","遥感","土壤碳","数字土壤制图",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[45,51],{"code":46,"slug":47,"name":48,"path":49,"parent_code":50},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":52,"slug":53,"name":54,"path":55,"parent_code":50},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":57,"locked":22,"version":58,"method":59,"evidence":60},"classified","2026-10-22.2","rules",{"candidates":61,"agricultural_evidence":64},[62,69,75],{"code":39,"score":63,"supported":64,"title":65,"abstract":66,"tags":68},14,true,[40],[67,40],"土壤",[],{"code":46,"score":70,"supported":64,"title":71,"abstract":73,"tags":74},9,[72],"machine learning",[],[34],{"code":52,"score":29,"supported":64,"title":76,"abstract":77,"tags":79},[],[35,78],"remote sensing",[4,35],[81,82],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178",0,"10.1134\u002Fs1064229326601228",{"doi":85,"openalex_id":87,"authors":88,"venue":18,"cited_by_count":84,"oa_url":17,"card":121,"direction":125,"ingested_from":127},"W7222107492",[89,92,95,98,101,104,107,110,112,115,118],{"name":90,"orcid":91},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":93,"orcid":94},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":96,"orcid":97},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":99,"orcid":100},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":102,"orcid":103},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":105,"orcid":106},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":108,"orcid":109},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":111,"orcid":17},"A. E. Shatunov",{"name":113,"orcid":114},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":116,"orcid":117},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":119,"orcid":120},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","农业遥感与作物表型","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","openalex","2026-10-17T23:30:31.485949Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":17,"source_name":135,"source_url":132,"published_at":136,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":137,"score_detail":138,"sources":140,"tags":142,"primary_topic":17,"secondary_topics":147,"classification":148,"search_phrases":158,"slug":161,"view_count":84,"doi":162,"paper":163,"created_at":180},4230,"Vegetation dynamics in Mizoram Northeast India from 2005 to 2025 using landsat imagery and image specific NDVI thresholding","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00136-2","Vegetation monitoring is important for evaluating landscape change in ecologically sensitive mountain regions. This study assessed vegetation dynamics across Mizoram, India, an eastern Himalayan-extension landscape, between 2005 and 2025 using Landsat 5 Thematic Mapper and Landsat 9 Operational Land Imager\u002FThermal Infrared Sensor surface-reflectance imagery. Satellite images were processed in Google Earth Engine, and normalized difference vegetation index have been used to derive vegetation and non-vegetation areas using image-specific thresholds. Spatial overlay analysis was subsequently applied to identify vegetation persistence, gain, loss, and persistent non-vegetated land at the state and district levels.Vegetation remained the dominant mapped land-cover category in both years but declined from 20,558.8 km² (97.52%) in 2005 to 20,283.8 km² (96.20%) in 2025, representing a net reduction of 275.0 km². Gross vegetation loss is 602.0 km², exceeding gross vegetation gain of 326.9 km². Vegetation loss was spatially heterogeneous, with the largest net declines observed in Lawngtlai, Kolasib, Saiha, Aizawl, and Lunglei districts, whereas Mamit, Champhai, and Saitual recorded stable net gains. The findings provide baseline information on vegetation-extent dynamics at the state and district level in Mizoram and offer useful insights for future vegetation monitoring, forest conservation, sustainable landscape management, and land-use planning in the eastern Himalayan region.","植被监测对于评估生态敏感山区景观变化具有重要意义。本研究利用Landsat 5专题制图仪（Thematic Mapper）和Landsat 9操作陆地成像仪\u002F热红外传感器（Operational Land Imager\u002FThermal Infrared Sensor）地表反射率影像，评估了2005年至2025年间印度米佐拉姆邦——喜马拉雅东延景观区的植被动态。卫星影像在Google Earth Engine中处理，利用归一化植被指数（normalized difference vegetation index）并结合各影像特定阈值提取植被区与非植被区。随后应用空间叠加分析，在邦和县两级识别植被持续、增加、减少及持续非植被土地。两个年份中植被均为主要土地覆盖类型，但面积从2005年的20558.8 km²（97.52%）下降至2025年的20283.8 km²（96.20%），净减少275.0 km²。植被总减少面积为602.0 km²，超过总增加面积326.9 km²。植被减少在空间上呈异质性，净减少最大的县为Lawngtlai、Kolasib、Saiha、Aizawl和Lunglei，而Mamit、Champhai和Saitual则录得稳定的净增加。研究结果为米佐拉姆邦邦和县两级植被范围动态提供了基线信息，并为喜马拉雅东部地区未来的植被监测、森林保护、可持续景观管理和土地利用规划提供了有益参考。","Discover Forests","2026-10-03T00:00:00Z",65,{"impact":29,"substance":26,"depth":27,"authority":25,"freshness":70,"relevant":8,"comment":139},"基于Landsat与GEE的20年植被动态监测，方法规范、数据扎实，但属区域生态研究，与国内农业信息化关联较弱，公共价值有限。",[141],{"name":135,"url":132},[143,144,4,145,146],"NDVI","遥感监测","森林保护","植被动态",[],{"status":149,"locked":22,"version":58,"method":59,"evidence":150},"needs_review",{"candidates":151,"agricultural_evidence":22,"reason":157},[152],{"code":52,"score":153,"supported":22,"title":154,"abstract":155,"tags":156},3,[],[],[4,143,146,144],"农业相关性需要复核",[159,160],"Mizoram Landsat NDVI 植被","印度东北部 植被变化 遥感","MizoramLandsatNDVI植被-4230","10.1007\u002Fs44415-026-00136-2",{"doi":162,"openalex_id":164,"authors":165,"venue":135,"cited_by_count":84,"oa_url":174,"card":175,"direction":125,"ingested_from":127},"W7218499716",[166,169,172],{"name":167,"orcid":168},"K. Lalramngaizuala","https:\u002F\u002Forcid.org\u002F0009-0005-5754-3449",{"name":170,"orcid":171},"Vishwambhar Prasad Sati","https:\u002F\u002Forcid.org\u002F0000-0001-6423-3119",{"name":173,"orcid":17},"F. C. Kypacharili","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00136-2.pdf",{"tldr":176,"method":177,"finding":178,"direction":125,"opportunity":179},"基于Landsat影像与NDVI阈值法评估印度米佐拉姆2005—2025年植被动态。","Landsat 5\u002F9地表反射率影像，Google Earth Engine，影","植被占比由97.52%降至96.20%，净减275 km²，损失602 km²大于增益326.9 k","可引入时序NDVI与机器学习区分植被退化驱动因素，并耦合地形与政策数据做多尺度监测。","2026-10-05T23:30:25.009678Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":17,"source_name":187,"source_url":184,"published_at":188,"category":20,"cover_url":17,"hotness":189,"is_selected":22,"score":137,"score_detail":190,"sources":192,"tags":196,"primary_topic":200,"secondary_topics":205,"classification":207,"search_phrases":225,"slug":228,"view_count":8,"doi":229,"paper":230,"created_at":246},3895,"Analysis Of Changes In Land Use And Land Cover In Pune City Over A Decade Using Geospatial Techniques.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053479","Abstract Land use and land cover (LULC) changes play a significant role in reflecting natural and anthropogenic environmental changes. In this study, changes in 'Land Use and Land Cover' (LULC) in Pune city for the period 2015-2025 have been analysed using the techniques of 'Remote Sensing' and 'Geographic Information System' (GIS). The May 2015 and May 2025 30 m resolution Landsat 08 satellite images were used. For LULC classification, the 'Supervised Maximum Likelihood Classification' method was used, resulting in 5 major LULC classes: built-up land, barren land, and agricultural land, vegetation, and water bodies. More than 100 'training samples' were taken for each land cover type to improve classification accuracy. The findings of the study show that there was a considerable shift in the spatial distribution of LULC in this decade. Built-up land increased from 154.48 sq. km (30.48%) in 2015 to 292.26 sq. km (57.86%) in 2025, representing an increase of approximately 137.78 sq. km. Conversely, the area of barren land decreased from 203.94 sq. km (40.24%) to 106.93 sq. km (21.10%). The amount of agricultural land has reduced from 44.39 sq. km (8.75%) to 18.93 sq. km (3.72%), indicating that agricultural land has been converted for urbanisation on a large scale. Regarding vegetation cover, 85.91 sq. km showed a marginal decrease from 16.95% to 15.58%, whereas the area of water bodies decreased from 18.13 sq. km (3.58%) to 10.77 sq. km (2.12%). The results show the rapid growth of Pune city during the study period, which is mainly observed in the loss or transformation of barren land, agricultural land and water bodies. The study shows the usefulness of using Landsat data, ‘supervised maximum likelihood classification’, GIS and ‘change detection' techniques for tracking long-term urban growth and environmental changes. The results of this research can help urban planners and decision makers in formulating policies for sustainable land use and in safeguarding environmentally sensitive areas.","摘要 土地利用与土地覆盖（LULC）变化在反映自然与人为环境变化方面发挥着重要作用。本研究利用遥感（Remote Sensing）和地理信息系统（GIS）技术，分析了浦那市2015—2025年期间土地利用与土地覆盖（LULC）的变化。研究使用了2015年5月和2025年5月分辨率为30米的Landsat 08卫星影像。在LULC分类中，采用了监督最大似然分类（Supervised Maximum Likelihood Classification）方法，最终得到5个主要LULC类别：建设用地、裸地、农用地、植被和水体。为提高分类精度，每种土地覆盖类型均选取了100个以上的训练样本（training samples）。研究结果表明，在这十年间，LULC的空间分布发生了显著变化。建设用地从2015年的154.48平方公里（30.48%）增加到2025年的292.26平方公里（57.86%），增加了约137.78平方公里。相反，裸地面积从203.94平方公里（40.24%）减少到106.93平方公里（21.10%）。农用地面积从44.39平方公里（8.75%）减少到18.93平方公里（3.72%），表明农用地已大规模转化为城市化用地。植被覆盖方面，85.91平方公里略有下降，从16.95%降至15.58%；水体面积则从18.13平方公里（3.58%）减少到10.77平方公里（2.12%）。结果表明，研究期间浦那市快速扩张，主要体现在裸地、农用地和水体的减少或转变上。研究表明，利用Landsat数据、监督最大似然分类、GIS和变化检测（change detection）技术对于追踪长期城市增长与环境变化具有重要价值。本研究结果可帮助城市规划者和决策者制定可持续土地利用政策，并保护环境敏感区域。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,{"impact":29,"substance":26,"depth":27,"authority":25,"freshness":70,"relevant":8,"comment":191},"基于Landsat遥感与GIS的浦那市十年土地利用变化研究，数据翔实、结论明确，对农业用地流失与城市扩张监测有参考价值，但属区域案例研究，公共影响有限。",[193,194],{"name":187,"url":184},{"name":187,"url":195},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053480",[35,197,4,198,199],"土地利用","GIS","城市化",{"code":201,"slug":202,"name":203,"path":204,"parent_code":50},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[206],{"code":52,"slug":53,"name":54,"path":55,"parent_code":50},{"status":57,"locked":22,"version":58,"method":59,"evidence":208},{"candidates":209,"agricultural_evidence":64},[210,216,220],{"code":201,"score":21,"supported":64,"title":211,"abstract":212,"tags":215},[],[213,198,214],"地理信息","geographic information",[198],{"code":52,"score":29,"supported":64,"title":217,"abstract":218,"tags":219},[],[35,78],[4,35],{"code":221,"score":8,"supported":22,"title":222,"abstract":223,"tags":224},"H6",[],[],[197],[226,227],"土地利用 城市化 遥感 Landsat","土地利用 城市化","土地利用城市化遥感Landsat-3895","10.5281\u002Fzenodo.23053479",{"doi":229,"openalex_id":231,"authors":232,"venue":187,"cited_by_count":84,"oa_url":184,"card":241,"direction":125,"ingested_from":127},"W7214889813",[233,235,237,239],{"name":234,"orcid":17},"Kalyani Machhindra sonawane",{"name":236,"orcid":17},"Sumitra Nandkumar Nikam",{"name":238,"orcid":17},"Sunil W. Gaikwad",{"name":240,"orcid":17},"Yogesh N. Pawar",{"tldr":242,"method":243,"finding":244,"direction":125,"opportunity":245},"用遥感与GIS分析印度浦那市2015-2025年土地利用变化，发现建设用地大幅扩张、农地水体锐减。","Landsat 8 30米影像，监督最大似然分类，5类LULC与变化检测。","建设用地从30.48%增至57.86%，农地由8.75%降至3.72%，裸地与水体同步减少。","可结合多时相高分辨率影像与CA-Markov模型，预测城市扩张对农地和水体的未来胁迫。","2026-10-01T23:30:28.617704Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":17,"source_name":253,"source_url":250,"published_at":254,"category":20,"cover_url":17,"hotness":21,"is_selected":22,"score":255,"score_detail":256,"sources":260,"tags":262,"primary_topic":266,"secondary_topics":271,"classification":273,"search_phrases":305,"slug":308,"view_count":8,"doi":309,"paper":310,"created_at":324},1431,"S-SEBI based Evapotranspiration Estimation Using Landsat 8 Data in Kendrapara District of Odisha, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95656","Background: Accurate estimation of evapotranspiration (ET) is essential for agricultural water management, irrigation planning, and regional water-resource assessment, particularly in areas where ground-based meteorological observations are limited. Aim: This study aimed to assess the spatial and temporal variability of actual evapotranspiration (ETa) and reference evapotranspiration (ETo) over Kendrapara district, Odisha, India, during 2016 and 2021 using the Simplified Surface Energy Balance Index (S-SEBI) model, and to compare satellite-derived ETa with FAO Penman–Monteith and Hargreaves–Samani ETo estimates. Study Design: A remote sensing and GIS-based comparative study was conducted using the S-SEBI surface energy balance model to estimate ETa from Landsat 8 imagery and evaluate its performance against conventional ETo methods. Place and Duration of Study: The study was carried out in Kendrapara district, Odisha, India, using satellite and climate data for the years 2016 and 2021. Two dates were selected primarily based on the availability of suitable Landsat 8 images with adequate quality, spatial coverage, and minimal cloud contamination over the study area. Methodology: Landsat 8 (WRS Path\u002FRow 139\u002F46) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data were obtained from the USGS Earth Explorer platform to derive surface reflectance and land surface temperature. ETa was estimated using the S-SEBI model, while ETo values based on the FAO Penman–Monteith and Hargreaves–Samani methods were obtained from the Climate Engine platform. Statistical error analysis was performed to assess the agreement between satellite-derived ETa and reference ETo estimates. Results: ETa exhibited significant spatial variability associated with vegetation condition and surface moisture. Mean ETa increased from 3.27 mm day⁻¹ in 2016 to 3.87 mm day⁻¹ in 2021, corresponding to higher net radiation and soil heat flux. S-SEBI-derived ETa closely matched FAO Penman–Monteith ETo, whereas the Hargreaves–Samani method consistently overestimated evapotranspiration. Error analysis indicated lower bias and uncertainty for the S-SEBI estimates. Conclusion: The S-SEBI model reliably estimated spatially explicit ETa with minimal ground data, demonstrating strong potential for drought monitoring, crop water requirement assessment, irrigation planning, and sustainable water resource management in data-scarce regions.","背景：准确估算蒸散量（ET）对于农业用水管理、灌溉规划及区域水资源评估至关重要，尤其是在地面气象观测资料有限的地区。目的：本研究旨在利用简化地表能量平衡指数（S-SEBI）模型，评估2016年和2021年印度奥里萨邦肯德拉帕拉县实际蒸散量（ETa）和参考蒸散量（ETo）的时空变化，并将卫星反演的ETa与FAO Penman–Monteith和Hargreaves–Samani方法估算的ETo进行比较。研究设计：采用基于遥感和GIS的对比研究，利用S-SEBI地表能量平衡模型从Landsat 8影像估算ETa，并评估其与传统ETo方法的性能差异。研究地点与时间：研究在印度奥里萨邦肯德拉帕拉县开展，使用2016年和2021年的卫星及气候数据。主要根据研究区域内可用Landsat 8影像的质量、空间覆盖范围和云污染程度，选取了两个日期。方法：从USGS Earth Explorer平台获取Landsat 8（WRS Path\u002FRow 139\u002F46）操作陆地成像仪（OLI）和热红外传感器（TIRS）数据，用于反演地表反射率和地表温度。采用S-SEBI模型估算ETa，而基于FAO Penman–Monteith和Hargreaves–Samani方法的ETo值则从Climate Engine平台获取。通过统计误差分析评估卫星反演ETa与参考ETo估算值之间的一致性。结果：ETa表现出与植被状况和地表湿度相关的显著空间变异性。平均ETa从2016年的3.27毫米\u002F天增加到2021年的3.87毫米\u002F天，这与较高的净辐射和土壤热通量相对应。S-SEBI反演的ETa与FAO Penman–Monteith ETo高度吻合，而Hargreaves–Samani方法则持续高估蒸散量。误差分析表明，S-SEBI估算的偏差和不确定性较低。结论：S-SEBI模型能够在极少地面数据条件下可靠地估算具有空间显式特征的ETa，显示出在数据稀缺地区进行干旱监测、作物需水量评估、灌溉规划和可持续水资源管理的巨大潜力。","International Journal of Environment and Climate Change","2026-09-02T00:00:00Z",58,{"impact":21,"substance":257,"depth":27,"authority":25,"freshness":258,"relevant":8,"comment":259},18,2,"论文利用Landsat 8数据评估印度奥里萨邦蒸散发，方法可靠，对数据稀缺地区农业水资源管理有参考价值，但地域性强，时效性低。",[261],{"name":253,"url":250},[263,264,265,4],"农业遥感","水资源管理","蒸散发",{"code":267,"slug":268,"name":269,"path":270,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[272],{"code":52,"slug":53,"name":54,"path":55,"parent_code":50},{"status":57,"locked":22,"version":58,"method":59,"evidence":274},{"candidates":275,"agricultural_evidence":64},[276,285,289,294,301],{"code":267,"score":277,"supported":64,"title":278,"abstract":280,"tags":284},19,[279],"evapotranspiration",[281,282,268,283,279],"灌溉","水资源","water resource",[264,265],{"code":52,"score":29,"supported":64,"title":286,"abstract":287,"tags":288},[],[35,78],[4,263],{"code":39,"score":290,"supported":64,"title":291,"abstract":292,"tags":293},6,[],[67,40],[],{"code":295,"score":290,"supported":64,"title":296,"abstract":297,"tags":300},"H7",[],[298,299],"干旱","drought",[],{"code":201,"score":153,"supported":64,"title":302,"abstract":303,"tags":304},[],[198],[],[306,307],"水资源管理 农业遥感 蒸散发 Landsat","水资源管理 农业遥感","水资源管理农业遥感蒸散发Landsat-1431","10.9734\u002Fijecc\u002F2026\u002Fv16i95656",{"doi":309,"openalex_id":311,"authors":312,"venue":253,"cited_by_count":84,"oa_url":250,"card":319,"direction":125,"ingested_from":127},"W7205007124",[313,315,317],{"name":314,"orcid":17},"Ritoban Pandit",{"name":316,"orcid":17},"Bama Shankar Rath",{"name":318,"orcid":17},"Dwarika Mohan Das",{"tldr":320,"method":321,"finding":322,"direction":125,"opportunity":323},"利用Landsat 8和S-SEBI模型估算印度奥里萨邦Kendrapara地区的蒸散发，验证其精度","S-SEBI模型、Landsat 8遥感数据、FAO Penman-Montei","S-SEBI估算的ETa与FAO PM方法接近，Hargreaves-Samani高估；ETa从20","可探索S-SEBI模型在不同气候区及高分辨率数据下的适用性，结合机器学习改进地表参数反演，提升数据稀缺区蒸散发估算精度。","2026-09-02T23:30:51.113113Z"]