[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3675":3,"related-3675":53},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":52},3675,"Long-term (2000-2023) Monitoring and Hotspot Identification of Total Suspended Solids and Turbidity in Lake Tana, Ethiopia, Using High Temporal Resolution MODIS Satellite Imagery","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeac9e","Abstract Monitoring water quality is challenging in data-scarce regions. This study examines the use of remote sensing to assess key water quality parameters in Lake Tana, Northwestern Ethiopia, to support the sustainable management of Lake water resources. The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was applied for long-term spatiotemporal assessment of total suspended solids (TSS) and turbidity. The results show that annual mean TSS increased from 350 mg\u002Fl in 2000 to 640 mg\u002Fl in 2020, while turbidity increased from 120 to about 500 NTU before both parameters declined during 2020-2023. Seasonal analysis indicates that the highest TSS and turbidity occurred during the peak rainy month of July. The spatial variation revealed that elevated TSS and turbidity levels were concentrated primarily at the inlets of the major feeder rivers to Lake Tana. This reflects a substantial influx of sediment into the Lake during rainy seasons. During post-rainy months, TSS is redistributed across different sections of the Lake by wind. The findings of this work demonstrate the ability of remote sensing to provide long-term monitoring of water quality parameters, offering valuable insights for the sustainable management of Lake water resources.","摘要 在数据稀缺地区，水质监测面临挑战。本研究探讨了利用遥感技术评估埃塞俄比亚西北部塔纳湖（Lake Tana）关键水质参数，以支持湖泊水资源的可持续管理。研究采用中分辨率成像光谱仪（MODIS）卫星影像，对总悬浮固体（TSS）和浊度进行了长期时空评估。结果表明，年均TSS从2000年的350 mg\u002Fl增至2020年的640 mg\u002Fl，浊度从120 NTU增至约500 NTU，随后两者在2020—2023年间均有所下降。季节性分析表明，TSS和浊度最高值出现在降雨高峰的7月。空间变化显示，较高的TSS和浊度水平主要集中在塔纳湖主要补给河流的入湖口处，反映了雨季大量泥沙流入湖泊。在雨后月份，TSS受风力作用在湖泊不同区域重新分布。本研究结果证明了遥感技术在水质参数长期监测方面的能力，为湖泊水资源的可持续管理提供了有价值的见解。",null,"Environmental Research Communications","2026-09-25T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"利用MODIS长时序遥感监测塔纳湖水质，方法成熟、数据跨度长，对数据稀缺地区水质管理有参考价值，但属区域案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","水质监测","埃塞俄比亚","湖泊生态","MODIS",[33,34],"Lake Tana MODIS 水质监测","埃塞俄比亚 塔纳湖 悬浮物","LakeTanaMODIS水质监测-3675",0,"10.1088\u002F2515-7620\u002Faeac9e",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":44,"card":45,"direction":49,"ingested_from":51},"W7214348540",[41],{"name":42,"orcid":43},"Maru Fentaw Getie","https:\u002F\u002Forcid.org\u002F0009-0003-9743-4365","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeac9e\u002Fpdf",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"利用MODIS影像长时序监测埃塞俄比亚塔纳湖悬浮物与浊度并识别热点区域。","MODIS高时间分辨率遥感影像，2000-2023年TSS与浊度反演及时空分析。","TSS与浊度2000-2020年显著上升，2020-2023年回落，高值集中于主要入湖河流口。","农业遥感与作物表型","可结合降水与土地利用数据，量化流域农业侵蚀对湖泊水质贡献并构建预警模型。","openalex","2026-09-28T23:30:30.946130Z",{"total":54,"page":22,"page_size":54,"items":55},6,[56,96,154,194,239,287],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":75,"slug":78,"view_count":36,"doi":79,"paper":80,"created_at":95},2800,"Spatial Persistence and Interannual Variability of Floating Algae in Kainji Lake (Nigeria): Insights from Multi-Year Sentinel-2 Observations","https:\u002F\u002Fdoi.org\u002F10.62622\u002Fteiee.026.4.3.55-64","Background: Floating algae are increasingly recognized as indicators of ecological change in freshwater ecosystems. Although satellite remote sensing has been widely applied to monitor algal dynamics, little is known about the long-term spatial organization of floating algae in large tropical reservoirs in West Africa. Objectives: This study investigated the spatio-temporal dynamics of floating algae in Kainji Lake, Nigeria, from 2020 to 2025, with the aim of identifying recurrent hotspot zones, assessing interannual variability, and evaluating the long-term stability of floating algae distribution. Methods: Sentinel-2 Level-2A Surface Reflectance imagery was processed within the Google Earth Engine platform. Images with less than 10% cloud cover were selected and subjected to cloud masking and water extraction procedures. Floating algae were mapped using the Floating Algae Index (FAI), calculated from the red, near-infrared, and short-wave infrared spectral bands. Annual median FAI composites were generated for each year from 2020 to 2025. Descriptive statistics of positive FAI values were calculated to assess temporal variability. Multi-year persistence analysis was then performed to identify recurrent floating algae hotspot zones. Results: The results revealed pronounced spatial heterogeneity in floating algae occurrence across Kainji Lake. Floating algae were consistently concentrated within shoreline and embayment environments, whereas most open-water areas exhibited comparatively low occurrence. Mean annual positive FAI values ranged from 0.0656 to 0.0841, indicating interannual variability in floating algae intensity. Annual bloom extent varied from 139.93 to 168.64 km², representing 12 –15% of the lake surface, with the largest coverage recorded in 2022 and the smallest in 2025. Despite these temporal fluctuations, recurrent hotspot zones remained spatially consistent throughout the study period. Conclusion: Floating algae in Kainji Lake exhibit persistent spatial organization rather than random distribution. This study provides the first multi-year evidence of recurrent floating algae hotspot zones in the reservoir, establishing an important baseline for understanding floating algae dynamics, thereby addressing a significant knowledge gap in West African reservoir ecosystems and providing a foundation for future ecological assessments and monitoring programmes.","背景：浮游藻类日益被视为淡水生态系统生态变化的指示物。尽管卫星遥感已被广泛应用于监测藻类动态，但关于西非大型热带水库中浮游藻类长期空间格局的认识仍然有限。目标：本研究调查了2020年至2025年尼日利亚凯恩吉湖浮游藻类的时空动态，旨在识别反复出现的热点区域、评估年际变异性，并评价浮游藻类分布的长期稳定性。方法：在Google Earth Engine平台上处理Sentinel-2 Level-2A地表反射率影像。选取云量低于10%的影像，并进行去云和水体提取处理。利用浮游藻类指数（Floating Algae Index, FAI）对浮游藻类进行制图，该指数由红光、近红外和短波红外光谱波段计算得出。生成2020年至2025年各年的FAI年中值合成影像。计算FAI正值的描述性统计量以评估时间变异性。随后进行多年持续性分析，以识别反复出现的浮游藻类热点区域。结果：结果揭示了凯恩吉湖浮游藻类 occurrence 的显著空间异质性。浮游藻类持续集中于湖岸和湖湾环境，而大部分开阔水域的出现相对较低。年均FAI正值范围为0.0656至0.0841，表明浮游藻类强度存在年际变异性。年度藻华范围在139.93至168.64 km²之间变化，占湖泊表面积的12%–15%，其中2022年记录到最大覆盖面积，2025年最小。尽管存在这些时间波动，反复出现的热点区域在整个研究期间保持空间一致性。结论：凯恩吉湖的浮游藻类表现出持续的空间组织格局，而非随机分布。本研究首次提供了该水库浮游藻类热点区域反复出现的多年证据，为理解浮游藻类动态建立了重要基线，从而填补了西非水库生态系统中的重大知识空白，并为未来的生态评估和监测计划奠定了基础。","Trends in Ecological and Indoor Environmental Engineering","2026-09-15T00:00:00Z",66,{"impact":66,"substance":67,"depth":68,"authority":17,"freshness":21,"relevant":22,"comment":69},8,20,17,"基于Sentinel-2多年观测揭示尼日利亚Kainji湖浮藻热点空间稳定性，方法规范、数据扎实，但属区域生态研究，与国内三农信息化关联间接，可作遥感应用参考而非每日必读。",[71],{"name":62,"url":59},[73,27,28,74,30],"非洲农业","卫星遥感",[76,77],"卫星遥感 水质监测 湖泊生态 遥感监测","卫星遥感 水质监测","卫星遥感水质监测湖泊生态遥感监测-2800","10.62622\u002Fteiee.026.4.3.55-64",{"doi":79,"openalex_id":81,"authors":82,"venue":62,"cited_by_count":36,"oa_url":89,"card":90,"direction":49,"ingested_from":51},"W7213293788",[83,86],{"name":84,"orcid":85},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":87,"orcid":88},"Ruby Asunomeh","https:\u002F\u002Forcid.org\u002F0009-0009-7529-254X","https:\u002F\u002Fwww.teiee.net\u002Fpdf-226150-145194?filename=Spatial-Persistence-and-I.pdf",{"tldr":91,"method":92,"finding":93,"direction":49,"opportunity":94},"利用2020-2025年Sentinel-2影像分析尼日利亚凯恩吉湖浮藻时空动态与热点区。","Google Earth Engine处理Sentinel-2 L2A影像，用F","浮藻集中于湖岸与湾口，年际面积波动但热点区位置持续稳定。","可结合水质、气象与人类活动数据，探究热带水库浮藻热点持续性的驱动机制与预警模型。","2026-09-17T23:30:38.937817Z",{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":108,"tags":110,"search_phrases":114,"slug":117,"view_count":22,"doi":118,"paper":119,"created_at":153},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":19,"substance":18,"depth":19,"authority":106,"freshness":13,"relevant":22,"comment":107},14,"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[109],{"name":102,"url":99},[111,112,113,27,28],"机器学习","农业面源污染","智慧渔业",[115,116],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657","10.3390\u002Frs18183185",{"doi":118,"openalex_id":120,"authors":121,"venue":102,"cited_by_count":36,"oa_url":99,"card":148,"direction":49,"ingested_from":51},"W7213230686",[122,125,127,130,132,135,137,139,141,143,145],{"name":123,"orcid":124},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":126,"orcid":9},"Daganchew Aklog",{"name":128,"orcid":129},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":131,"orcid":9},"Minychl G. Dersseh",{"name":133,"orcid":134},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":136,"orcid":9},"Seleshi Yalew",{"name":138,"orcid":9},"Demesew A. Mhiret",{"name":140,"orcid":9},"Sisay B. Asress",{"name":142,"orcid":9},"Tigistu Wassie Agegnehu",{"name":144,"orcid":9},"Shawl Abebe Desta",{"name":146,"orcid":147},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":149,"method":150,"finding":151,"direction":49,"opportunity":152},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","2026-09-16T23:30:28.415573Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":161,"sources":163,"tags":165,"search_phrases":169,"slug":172,"view_count":36,"doi":173,"paper":174,"created_at":193},3676,"Assessing Land Use, Land Cover Changes, and Urbanization Impacts on Water Stress in West African Watersheds: A Systematic Methodological Review","https:\u002F\u002Fdoi.org\u002F10.3791\u002F72287","Water stress is one of the major environmental and socio-economic constraints in West Africa, where population growth, agricultural expansion, soil sealing, hydroclimatic variability, and rising pressure on surface water and groundwater rapidly transform watershed functioning. Evaluating the impacts of land use and land cover (LULC) changes and urbanization requires methodological approaches that link the spatial dynamics of landscapes to observed or simulated hydrological responses. This review provides a comparative and critical analysis of the main techniques used in the region: instrumented watersheds and in situ observations, controlled field and laboratory experiments, satellite remote sensing and multi-temporal analysis, groundwater-oriented methods, conceptual, semi-distributed, physically based, and integrated surface-subsurface hydrological modeling, statistical and machine-learning approaches, and hybrid methods coupling land-use and climate scenarios. The search and selection process followed a transparent, PRISMA-based protocol and yielded 51 included records, of which 35 are anchored in West Africa. Hydrological modeling driven by remote sensing dominates the corpus, accounting for 15 of the 35 West African records (43%), particularly with SWAT, ACRU, WaSiM, CEQUEAU, SHETRAN, HydroGeoSphere, and ParFlow-CLM; reported Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and coefficient of determination generally range from about 0.6 to 0.9, which is satisfactory to very good against standard evaluation guidelines. Statistical and machine-learning studies account for 8 of 35 records, whereas urban- and groundwater-focused studies remain scarce. Moreover, 8 of 35 records originate from a single research network, so several regional conclusions rest on a narrow and partly non-independent evidence base. In situ networks remain essential for process understanding but are constrained by cost and low density; purely satellite-based approaches suffer from cloud cover and spectral confusion in subhumid zones. The most promising advances lie in multi-source, multi-scale frameworks combining targeted instrumentation, satellite time series, integrated hydrological modeling, territorial scenarios, systematic uncertainty analyses, and co-construction with water-management and planning actors.","水资源压力是西非主要的环境和社会经济制约因素之一。在该地区，人口增长、农业扩张、土壤封闭、水文气候变率以及地表水和地下水压力的不断上升，正迅速改变着流域功能。评估土地利用与土地覆盖（LULC）变化及城市化带来的影响，需要将景观的空间动态与观测或模拟的水文响应相联系的方法学途径。本综述对该地区所采用的主要技术进行了比较性和批判性分析：受控流域与实地观测、受控野外与实验室实验、卫星遥感与多时相分析、面向地下水的方法、概念性、半分布式、基于物理过程及地表-地下耦合的综合水文模型、统计与机器学习方法，以及耦合土地利用与气候情景的混合方法。检索与筛选过程遵循透明、基于PRISMA的协议，最终纳入51篇文献，其中35篇以西非为研究区。由遥感驱动的水文建模在文献主体中占主导地位，在35篇西非文献中占15篇（43%），尤以SWAT、ACRU、WaSiM、CEQUEAU、SHETRAN、HydroGeoSphere和ParFlow-CLM为主；所报告的纳什-萨特克利夫效率（NSE）、克林-古普塔效率（KGE）和决定系数通常约在0.6至0.9之间，对照标准评价指南属于满意至很好水平。统计与机器学习研究在35篇文献中占8篇，而聚焦城市和地下水的研究仍然稀少。此外，35篇文献中有8篇来自同一研究网络，因此若干区域性结论建立在狭窄且部分非独立的证据基础之上。实地观测网络对于过程理解仍然不可或缺，但受成本和低密度制约；纯卫星方法在半湿润区则受云覆盖和光谱混淆的影响。最有前景的进展在于多源、多尺度框架，其结合了针对性观测、卫星时间序列、综合水文建模、区域情景、系统不确定性分析，以及与水资源管理和规划主体的共同构建。","Journal of Visualized Experiments",{"impact":66,"substance":67,"depth":68,"authority":20,"freshness":66,"relevant":22,"comment":162},"系统综述方法学扎实、数据翔实，但聚焦西非流域且与国内农业信息化关联较弱，仅具方法参考价值。",[164],{"name":160,"url":157},[111,27,166,167,168],"土地利用","水文模型","西非流域",[170,171],"西非流域 土地利用 水文模型","SWAT 遥感 水资源压力","西非流域土地利用水文模型-3676","10.3791\u002F72287",{"doi":173,"openalex_id":175,"authors":176,"venue":160,"cited_by_count":36,"oa_url":9,"card":188,"direction":49,"ingested_from":51},"W7214394296",[177,179,182,184,186],{"name":178,"orcid":9},"Valère-Carin Jofack Sokeng",{"name":180,"orcid":181},"Nakouana Timité","https:\u002F\u002Forcid.org\u002F0000-0002-3894-1450",{"name":183,"orcid":9},"Toto Marc Zahui",{"name":185,"orcid":9},"Kouamé Koffi Fernand",{"name":187,"orcid":9},"Koné Tiémoman",{"tldr":189,"method":190,"finding":191,"direction":49,"opportunity":192},"系统综述西非流域土地利用变化与城市化对水资源压力的评估方法。","PRISMA系统综述，纳入51篇文献，比较遥感、水文模型与机器学习等方法。","遥感驱动水文模型占主导（43%），城市与地下水研究稀缺，证据基础偏窄。","可构建多源多尺度框架，融合实地观测、遥感时序与集成水文模型，并开展不确定性分析与利益相关者协同。","2026-09-28T23:30:31.949583Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":204,"tags":206,"search_phrases":211,"slug":214,"view_count":36,"doi":215,"paper":216,"created_at":238},3674,"Mapping and dynamic monitoring of desertification on the Qinghai-Tibetan Plateau using surface Albedo and Solar-Induced Chlorophyll Fluorescence","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0359348","Desertification poses a significant threat to humanity's long-term survival and sustainable development. In this study, four types of 2D desertification assessment models were created by integrating surface albedo with four vegetation-related indicators: NDVI, MSAVI, EVI, and solar-induced chlorophyll fluorescence (SIF). A comprehensive analysis of the spatiotemporal variations in desertification on the Qinghai-Tibetan Plateau (QTP) from 2001 to 2020 was then conducted. Among the four models, the SA-SIF model demonstrated the highest overall accuracy (0.8675; 95% CI: 0.8512-0.8838) and Kappa coefficient (0.8452; 95% CI: 0.8289-0.8615), significantly outperforming the other three models (p \u003C 0.01, McNemar's test). The SA-SIF model's mapping results revealed substantial regional variability in desertification on the QTP, with the degree of desertification decreasing from northwest to southeast. Over the past decade, the expansion of severe and extremely severe desertification on the QTP has shown signs of slowing, although future trends may become more complex due to the interplay of climatic and anthropogenic factors. The SA-SIF model can serve as a reference for future desertification control operations on the QTP.","荒漠化对人类长期生存与可持续发展构成重大威胁。本研究通过将地表反照率与四种植被相关指标——归一化植被指数（NDVI）、修正土壤调整植被指数（MSAVI）、增强型植被指数（EVI）和日光诱导叶绿素荧光（SIF）——相结合，构建了四类二维荒漠化评估模型，并据此对2001年至2020年青藏高原荒漠化的时空变化进行了综合分析。在四种模型中，SA-SIF模型的总体精度（0.8675；95%置信区间：0.8512–0.8838）和Kappa系数（0.8452；95%置信区间：0.8289–0.8615）最高，显著优于其他三种模型（p \u003C 0.01，McNemar检验）。SA-SIF模型的制图结果揭示了青藏高原荒漠化存在显著的区域差异，荒漠化程度自西北向东南递减。过去十年间，青藏高原重度及极重度荒漠化的扩张呈现放缓迹象，但受气候与人为因素交互作用的影响，未来趋势可能趋于复杂。SA-SIF模型可为青藏高原未来的荒漠化治理工作提供参考。","PLoS ONE",78,{"impact":19,"substance":18,"depth":19,"authority":106,"freshness":54,"relevant":22,"comment":203},"提出SA-SIF荒漠化评估模型并揭示青藏高原2001-2020年荒漠化时空演变，方法新颖、结论可靠，对高原生态治理有参考价值。",[205],{"name":200,"url":197},[207,27,208,209,210],"荒漠化","青藏高原","SIF","生态治理",[212,213],"青藏高原 荒漠化 遥感监测","SA-SIF 模型 叶绿素荧光","青藏高原荒漠化遥感监测-3674","10.1371\u002Fjournal.pone.0359348",{"doi":215,"openalex_id":217,"authors":218,"venue":200,"cited_by_count":36,"oa_url":232,"card":233,"direction":49,"ingested_from":51},"W7214339598",[219,222,225,227,229],{"name":220,"orcid":221},"Zhijian Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4764-2943",{"name":223,"orcid":224},"Hui Lin","https:\u002F\u002Forcid.org\u002F0000-0003-1278-4351",{"name":226,"orcid":9},"Lei Wu",{"name":228,"orcid":9},"Linling Tang",{"name":230,"orcid":231},"Xin Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-8859-7240","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0359348&type=printable",{"tldr":234,"method":235,"finding":236,"direction":49,"opportunity":237},"构建地表反照率与SIF结合的荒漠化评估模型，监测青藏高原2001-2020年荒漠化动态。","融合地表反照率与NDVI、MSAVI、EVI、SIF构建四种二维模型，对比精度。","SA-SIF模型精度最高（总体精度0.8675），荒漠化程度自西北向东南递减，重度扩张减缓。","可探索SIF与多源遥感协同的荒漠化早期预警，并量化气候与人类活动贡献。","2026-09-28T23:30:30.884081Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":9,"source_name":245,"source_url":242,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":246,"sources":248,"tags":250,"search_phrases":254,"slug":257,"view_count":36,"doi":258,"paper":259,"created_at":286},3673,"A low-cost, low-tech methodology for population estimation in heterogeneous, data-scarce urban settings, applied in the mountainous area of Qwaqwa, South Africa","https:\u002F\u002Fdoi.org\u002F10.1080\u002F0035919x.2026.2729963","Accurate population data is essential for effective planning for service and resource allocation, urban development and climate change adaptation – particularly in mountainous regions, where complex topography poses challenges to both data collection and infrastructure development. This paper presents a low-cost, low-tech and replicable hybrid methodology to estimate the population size and spatial distribution and applies it in Qwaqwa, a semi-rural, semi-urban mountainous region in South Africa. Using high-resolution satellite imagery, was manually mapped 91,624 main residential buildings. The study area was stratified into seven urban structure types based on morphological attributes, with 917 randomly sampled sites surveyed, with a response rate of 52.7%. Survey data provided occupancy rates, which were used to calculate yard-level population estimates and extrapolate population density across strata. The study reveals high variability in residents per yard, with no strong relationship between population density and visible physical attributes of main buildings and yards. The assessment resulted in an average simulated population of approximately 360,000 inhabitants (range 320,000–400,000), with a mean of 3.94 inhabitants per main building, including uninhabited buildings. The distribution of the population in Qwaqwa reflects the spatial imprint of apartheid-era planning, topographic constraints and ongoing urbanisation pressures, which together shape where people live and the level of demand placed on local infrastructure and services such as water access, transport and public facilities. The findings highlight the limitations of traditional remote sensing methods in mountainous terrain, where settlement patterns are shaped by both complex topography and socio-economic dynamics.","准确的人口数据对于有效规划服务和资源分配、城市发展以及气候变化适应至关重要——尤其是在山区，复杂的地形对数据收集和基础设施建设均构成挑战。本文提出了一种低成本、低技术门槛且可复制的混合方法，用于估算人口规模与空间分布，并将其应用于南非半乡村、半城市的山区——奎奎（Qwaqwa）。利用高分辨率卫星影像，人工绘制了91,624栋主要住宅建筑。研究区根据形态特征被划分为七种城市结构类型，随机抽取917个样点进行调查，应答率为52.7%。调查数据提供了入住率，用于计算院落级人口估算值，并据此推算各分层的人口密度。研究揭示了各院落居民数量的高度变异性，人口密度与主要建筑及院落的可见物理属性之间不存在强相关关系。评估得出的模拟人口平均约为360,000人（范围320,000–400,000），每栋主要建筑平均3.94人（含无人居住建筑）。奎奎的人口分布反映了种族隔离时期规划的空间印记、地形限制以及持续的城市化压力，这些因素共同塑造了人们的居住位置以及对当地基础设施和服务的需求水平，如供水、交通和公共设施。研究结果凸显了传统遥感方法在山区地形中的局限性，在这些地区，聚落格局同时受到复杂地形和社会经济动态的影响。","Transactions of the Royal Society of South Africa",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":247},"该论文聚焦南非山区人口估算方法，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[249],{"name":245,"url":242},[251,27,252,253],"城乡融合","南非","人口估算",[255,256],"Qwaqwa 人口估算","南非 山区 遥感","Qwaqwa人口估算-3673","10.1080\u002F0035919x.2026.2729963",{"doi":258,"openalex_id":260,"authors":261,"venue":245,"cited_by_count":36,"oa_url":279,"card":280,"direction":49,"ingested_from":51},"W7214276367",[262,265,267,269,271,273,275,277],{"name":263,"orcid":264},"Jess L. Delves","https:\u002F\u002Forcid.org\u002F0000-0002-2092-2905",{"name":266,"orcid":9},"S. Schneiderbauer",{"name":268,"orcid":9},"R. Schomacker",{"name":270,"orcid":9},"S. Terzi",{"name":272,"orcid":9},"Melissa Hansen",{"name":274,"orcid":9},"Pulane Pudumo",{"name":276,"orcid":9},"Zinhle Mbongo",{"name":278,"orcid":9},"Jonas Pieper","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F0035919X.2026.2729963?needAccess=true",{"tldr":281,"method":282,"finding":283,"direction":284,"opportunity":285},"提出低成本低技术混合方法，估算南非山区Qwaqwa的人口规模与空间分布。","高分辨率卫星影像人工制图、分层随机抽样调查与占用率外推。","人口约36万，密度与建筑可见物理属性无强关联，反映种族隔离规划与地形约束。","数字乡村与农业信息化","可探索将低成本人口估算与农业服务、资源分配结合，弥补山区数据稀缺下的规划空白。","2026-09-28T23:30:29.581603Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":295,"sources":297,"tags":299,"search_phrases":304,"slug":307,"view_count":36,"doi":308,"paper":309,"created_at":323},3671,"CLOUD-BASED MULTI-TEMPORAL SAR-OPTICAL FUSION USING RANDOM FOREST FOR GEOSPATIAL INTELLIGENCE MONITORING OF KIPP IKN NUSANTARA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.08","The development of Indonesia's new capital (IKN) Nusantara drives dramatic land-cover transformation across a strategic 17,599-hectare area under the persistent cloud cover of Kalimantan, where optical remote sensing alone is insufficient for continuous monitoring.This study proposes a cloud-based multi-temporal SAR-optical fusion approach using Random Forest classification for geospatial-intelligence (GEOINT) monitoring of the Core Government Area (KIPP) of IKN.Using Google Earth Engine, we processed 70 Sentinel-1 GRD and 52 Sentinel-2 images for 2022 and 61 and 81 images for 2024, building a 12-band fusion stack (SAR backscatter VV, VH, VV\u002FVH; optical B2, B3, B4, B8, B11, B12; and NDVI, NDBI, NDWI).Six experiments compared SAR-only, optical-only, and fusion configurations.The Random Forest fusion achieved 88.89% overall accuracy and 0.8609 kappa for 2022 and 85.12% and 0.8127 for 2024 (assessed against ESA WorldCover 2021 class definitions; see Section 2.7), outperforming SAR-only (63.20%) and optical-only (86.44%).Change detection revealed 899.64 ha of direct vegetation-to-built-up conversion, a 60% expansion of built-up area driven by IKN construction.This is the first cloud-based multi-temporal fusion framework applied to KIPP IKN monitoring, with implications for spatial planning, security surveillance, and environmental compliance.","印度尼西亚新首都（IKN）努山塔拉的开发，在加里曼丹持续云覆盖下推动了一个战略性的17,599公顷区域发生剧烈的土地覆盖转变，仅依靠光学遥感不足以实现持续监测。本研究提出了一种基于云的多时相SAR-光学融合方法，采用随机森林分类对IKN核心政府区（KIPP）进行地理空间情报（GEOINT）监测。利用Google Earth Engine，我们处理了2022年的70景Sentinel-1 GRD和52景Sentinel-2影像，以及2024年的61景和81景影像，构建了一个12波段融合数据集（SAR后向散射VV、VH、VV\u002FVH；光学B2、B3、B4、B8、B11、B12；以及NDVI、NDBI、NDWI）。六组实验比较了仅SAR、仅光学和融合配置。随机森林融合在2022年达到88.89%的总体精度和0.8609的kappa系数，2024年为85.12%和0.8127（依据ESA WorldCover 2021类别定义进行评估；见第2.7节），优于仅SAR（63.20%）和仅光学（86.44%）。变化检测揭示了899.64公顷的植被直接转为建成区，IKN建设推动建成区面积扩张60%。这是首个应用于KIPP IKN监测的基于云的多时相融合框架，对空间规划、安全监控和环境合规具有重要意义。","Geographia Technica","2026-09-26T00:00:00Z",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":296},"该研究聚焦印尼新首都建设的遥感监测，属地理空间情报领域，与三农、农业信息化、智慧农业等主题无直接关联，故不进入每日精选。",[298],{"name":293,"url":290},[300,27,301,302,303],"随机森林","土地覆盖变化","SAR光学融合","空间规划",[305,306],"Sentinel-1 Sentinel-2 融合","土地覆盖变化 SAR光学融合 空间规划 遥感监测","Sentinel-1Sentinel-2融合-3671","10.21163\u002Fgt_2027.221.08",{"doi":308,"openalex_id":310,"authors":311,"venue":293,"cited_by_count":36,"oa_url":317,"card":318,"direction":49,"ingested_from":51},"W7214434955",[312,314],{"name":313,"orcid":9},"Fajar Sidik Suganda",{"name":315,"orcid":316},"Asep Adang Supriyadi","https:\u002F\u002Forcid.org\u002F0000-0003-1103-6669","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F08_suganda.pdf",{"tldr":319,"method":320,"finding":321,"direction":49,"opportunity":322},"提出云端多时相SAR-光学融合与随机森林方法，监测印尼新首都核心区土地覆盖变化。","Google Earth Engine处理Sentinel-1\u002F2影像，构建12","融合分类精度达88.89%，检测到899.64公顷植被转建设用地，建成区扩张60%。","可迁移至多云农业区作物监测，探索融合特征优化与跨区域泛化能力。","2026-09-28T23:30:27.951741Z"]