[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2872":3,"related-2872":55},{"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":54},2872,"Simulation of Groundwater Recharge and Surface Runoff in Ungauged basin of Northern India Using SWAT Model","https:\u002F\u002Fdoi.org\u002F10.1139\u002Fcjce-2026-0128","Rapid agricultural expansion, urbanization, and increasing anthropogenic water demands are exerting significant pressure on surface and groundwater resources, necessitating robust hydrological assessments, particularly in ungauged basins. This study quantifies groundwater recharge and surface runoff in the Yamuna–Krishni interfluve of Northern India using the SWAT model. Due to unavailability of observed streamflow data, MODIS-based evapotranspiration data was employed for model calibration and validation. During the calibration period, the model achieved an NSE of 0.73 and an R² of 0.77, while the validation results demonstrated consistent predictive capability, with corresponding NSE and R² values of 0.71 and 0.72, respectively confirming a good statistical agreement. Simulated groundwater recharge ranged from 104.71 to 201.25 mm (14.67–23.88% of rainfall), while surface runoff varied between 8.15 and 14.86% supported by Groundwater Estimation Committee norms as well as hydrological model outcomes from adjacent basins. The results highlight the effectiveness of remote sensing–driven SWAT modeling for hydrological assessment in ungauged basins, providing a scientific basis for sustainable water resource management and infrastructure planning.","快速的农业扩张、城市化以及不断增加的人为用水需求，正对地表水和地下水资源造成巨大压力，因此亟需开展可靠的水文评估，尤其是在无测站流域。本研究利用SWAT模型，对印度北部亚穆纳河—克里希尼河河间地带的地下水补给与地表径流进行了定量评估。由于缺乏实测径流数据，研究采用基于MODIS的蒸散发数据对模型进行率定与验证。在率定期，模型取得了NSE为0.73、R²为0.77的结果；验证结果则显示出稳定的预测能力，相应的NSE和R²分别为0.71和0.72，证实了良好的统计一致性。模拟的地下水补给量为104.71至201.25 mm（占降雨量的14.67%–23.88%），而地表径流占比则在8.15%至14.86%之间，这一结果得到了地下水估算委员会规范以及邻近流域水文模型成果的支持。研究结果凸显了遥感驱动的SWAT模型在无测站流域水文评估中的有效性，为可持续水资源管理和基础设施规划提供了科学依据。",null,"Canadian Journal of Civil Engineering","2026-09-17T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"以MODIS蒸散发驱动SWAT在无资料流域完成率定验证，方法可迁移，对农业水资源管理有参考价值，但属区域案例研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","水文模型","地下水补给","地表径流","无资料流域",[33,34],"SWAT模型 无资料流域 地下水补给","Yamuna-Krishni 地表径流 模拟","SWAT模型无资料流域地下水补给-2872",0,"10.1139\u002Fcjce-2026-0128",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":9,"card":47,"direction":51,"ingested_from":53},"W7213515648",[41,44],{"name":42,"orcid":43},"Praveen Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-8311-1800",{"name":45,"orcid":46},"Raj Mohan Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8029-4584",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"用SWAT模型结合MODIS蒸散发数据，模拟印度北部无观测流域的地下水补给与地表径流。","SWAT水文模型，以MODIS蒸散发数据替代实测径流进行率定与验证。","率定期NSE=0.73、R²=0.77，验证期NSE=0.71、R²=0.72；地下水补给占降雨14","农业遥感与作物表型","无观测流域可用遥感蒸散发替代径流率定水文模型，可延伸至多源遥感融合与灌溉用水估算。","openalex","2026-09-18T23:30:29.296252Z",{"total":56,"page":22,"page_size":56,"items":57},6,[58,90,163,201,238,278],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":9,"content":9,"source_name":63,"source_url":9,"published_at":64,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":70,"tags":72,"search_phrases":77,"slug":80,"view_count":36,"doi":9,"paper":81,"created_at":89},2855,"UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1812","研究评估了6种光谱变换方法(原始反射率Ref、一阶导数FDR、PDS、OSC、FDR+PDS、FDR+OSC),结合3种机器学习算法(KNN、SVR、MLP)。进一步开发了集成这些基础学习者的Stacking集成模型,以提高湿度干扰下土壤盐度反演的精度。结果表明,Stacking模型在评估模型中达到最高的精度和稳定性。FDR+OSC-Stacking组合实现最佳验证性能,R²p=0.87,RMSEP=0.67 mS·cm⁻¹,RPD=2.93。FDR+OSC-Stacking组合成功应用于UAV高光谱图像,用于EC1:5的空间制图。来自吉林大学。","MDPI Agronomy","2026-09-15T00:00:00Z",75,{"impact":67,"substance":18,"depth":19,"authority":20,"freshness":68,"relevant":22,"comment":69},16,8,"方法组合新颖、验证指标扎实的无人机高光谱盐分制图研究，属细分领域实质进展，值得精选。",[71],{"name":63,"url":61},[73,74,75,27,76],"智慧农业","无人机","机器学习","土壤盐渍化",[78,79],"土壤盐渍化 智慧农业 机器学习 无人机","土壤盐渍化 智慧农业","土壤盐渍化智慧农业机器学习无人机-2855",{"doi":9,"openalex_id":9,"authors":82,"venue":9,"cited_by_count":36,"oa_url":9,"card":83,"direction":51,"ingested_from":88},[],{"tldr":84,"method":85,"finding":86,"direction":51,"opportunity":87},"用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。","6种光谱变换与KNN、SVR、MLP及Stacking集成，基于UAV高光谱数据","FDR+OSC-Stacking最优，R²p=0.87、RMSEP=0.67 mS·cm⁻¹、RPD","可探索多时相\u002F多传感器融合与迁移学习，提升不同湿度与区域下盐度反演泛化性。","agent","2026-09-18T00:03:30.822732Z",{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":104,"tags":106,"search_phrases":112,"slug":115,"view_count":36,"doi":116,"paper":117,"created_at":162},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。","Recent advances in remote sensing.","2026-09-16T00:00:00Z",81,{"impact":100,"substance":101,"depth":102,"authority":20,"freshness":21,"relevant":22,"comment":103},18,22,19,"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。",[105],{"name":96,"url":93},[107,108,27,109,110,111],"农业遥感","高光谱","地表温度","哥白尼计划","合成数据",[113,114],"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","10.62880\u002Frars26005",{"doi":116,"openalex_id":118,"authors":119,"venue":96,"cited_by_count":36,"oa_url":93,"card":157,"direction":51,"ingested_from":53},"W7213413531",[120,122,124,127,130,133,136,139,141,144,146,148,150,152,155],{"name":121,"orcid":9},"Christian Miesgang",{"name":123,"orcid":9},"Sandra Dotzler",{"name":125,"orcid":126},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":128,"orcid":129},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":131,"orcid":132},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":134,"orcid":135},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":137,"orcid":138},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":140,"orcid":9},"Louis Snyders",{"name":142,"orcid":143},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":145,"orcid":9},"Sven Kautlenbach",{"name":147,"orcid":9},"Catherine Odera",{"name":149,"orcid":9},"Tetiana Shtym",{"name":151,"orcid":9},"Tanel Tamm",{"name":153,"orcid":154},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":156,"orcid":9},"Melisa Soledad Heredia",{"tldr":158,"method":159,"finding":160,"direction":51,"opportunity":161},"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。","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。","2026-09-17T23:30:37.404847Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":169,"source_url":166,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":174,"tags":176,"search_phrases":181,"slug":184,"view_count":36,"doi":185,"paper":186,"created_at":200},2795,"Statistical evaluation of sentinel-2 spectral indices as environmental indicators of shallow soil resistivity for soil corrosion assessment in a semi-arid region of northwestern Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00732-x","Abstract Soil corrosion poses a significant threat to buried engineering infrastructure, particularly in semi-arid environments where variations in soil properties and surface environmental conditions influence electrochemical processes. This study integrated Sentinel-2-derived environmental indices with shallow-layer electrical resistivity and soil pH measurements to evaluate environmental factors associated with soil corrosion susceptibility in Sabon Gida, Katsina State, northwestern Nigeria. Sentinel-2 Level-2 A imagery acquired during the 2026 dry season was processed to derive the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), and Salinity Index (SI). Spectral index values were extracted at 44 Vertical Electrical Sounding (VES) locations, where shallow-layer resistivity and soil pH data were available. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were employed to investigate the relationships between the remotely sensed environmental variables and field-measured soil properties. The results indicate that the study area is characterized by sparse vegetation cover, low surface moisture, limited bare-soil exposure, and relatively higher salinity-index values that may indicate spatial variations in salinity-related environmental conditions during the dry-season image acquisition period. Strong correlations were observed among the Sentinel-2-derived indices, whereas only weak relationships were found between the spectral indices and shallow-layer resistivity. Multiple linear regression analysis yielded an R² value of 0.197, indicating that approximately 19.7% of the variation in soil resistivity was explained by the model. Although the salinity index was identified as the only statistically significant predictor, the overall regression model was not statistically significant (F = 1.867, p = 0.123). These findings suggest that the remotely sensed variables provide only limited explanatory power with respect to shallow soil resistivity. Nevertheless, the results indicate that Sentinel-2-derived environmental indices provide useful information for characterizing surface environmental conditions associated with soil corrosion but should be used as complementary indicators rather than direct substitutes for field-based geophysical investigations. The study demonstrates that integrating remote sensing with geophysical measurements provides a more robust framework for preliminary soil corrosion susceptibility assessment and offers valuable support for infrastructure planning and environmental monitoring in data-scarce semi-arid regions.","摘要 土壤腐蚀对埋地工程基础设施构成重大威胁，尤其是在半干旱环境中，土壤性质与地表环境条件的变化会影响电化学过程。本研究将Sentinel-2衍生的环境指数与浅层电阻率及土壤pH测量相结合，评估尼日利亚西北部卡齐纳州Sabon Gida地区与土壤腐蚀敏感性相关的环境因素。研究处理了2026年旱季获取的Sentinel-2 Level-2A影像，以提取归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、裸土指数（BSI）和盐分指数（SI）。在44个垂直电测深（VES）点位提取了光谱指数值，这些点位具备浅层电阻率和土壤pH数据。研究采用描述性统计、Pearson相关分析和多元线性回归，探讨遥感环境变量与实地测量土壤性质之间的关系。结果表明，研究区植被覆盖稀疏、地表湿度较低、裸土暴露有限，且盐分指数值相对较高，这可能指示旱季影像获取期间盐分相关环境条件的空间变化。Sentinel-2衍生的各指数之间观察到较强相关性，而光谱指数与浅层电阻率之间仅存在弱关系。多元线性回归分析得出R²值为0.197，表明模型可解释约19.7%的土壤电阻率变异。尽管盐分指数被识别为唯一具有统计学意义的预测因子，但整体回归模型未达到统计学显著性（F = 1.867，p = 0.123）。这些发现表明，遥感变量对浅层土壤电阻率的解释力有限。然而，结果也显示Sentinel-2衍生的环境指数可为表征与土壤腐蚀相关的地表环境条件提供有用信息，但应作为补充性指标，而非实地地球物理调查的直接替代。本研究表明，将遥感与地球物理测量相结合可提供更稳健的框架","Discover Geoscience",61,{"impact":68,"substance":100,"depth":172,"authority":17,"freshness":21,"relevant":22,"comment":173},14,"该研究将Sentinel-2遥感指数与土壤电阻率结合评估土壤腐蚀风险，方法有创新但解释力有限，对农业基础设施规划有参考价值，属细分领域进展。",[175],{"name":169,"url":166},[177,27,178,179,180],"Sentinel-2","土壤监测","土壤腐蚀","半干旱农业",[182,183],"半干旱农业 土壤监测 土壤腐蚀 遥感","半干旱农业 土壤监测","半干旱农业土壤监测土壤腐蚀遥感-2795","10.1007\u002Fs44288-026-00732-x",{"doi":185,"openalex_id":187,"authors":188,"venue":169,"cited_by_count":36,"oa_url":194,"card":195,"direction":51,"ingested_from":53},"W7213365338",[189,192],{"name":190,"orcid":191},"Abdulhakim Ahmad","https:\u002F\u002Forcid.org\u002F0009-0004-1030-8313",{"name":193,"orcid":9},"A. F. Akpaneno","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00732-x.pdf",{"tldr":196,"method":197,"finding":198,"direction":51,"opportunity":199},"用Sentinel-2光谱指数与电法电阻率、pH对比，评估尼日利亚半干旱区土壤腐蚀环境指示能力。","Sentinel-2 L2A影像提取NDVI、NDWI、BSI、SI，结合44个","光谱指数与浅层电阻率仅弱相关，回归R²=0.197且不显著，遥感只能作辅助指标。","可引入多时相\u002F土壤水分与纹理特征，或结合机器学习提升遥感对地下土壤腐蚀属性的间接反演能力。","2026-09-17T23:30:36.379942Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":36,"score_detail":208,"sources":210,"tags":212,"search_phrases":215,"slug":218,"view_count":36,"doi":219,"paper":220,"created_at":237},2793,"Delineation of groundwater potential zones using analytical hierarchy process and GIS in Bhilangana Block of Tehri Garhwal District, Uttarakhand, India","https:\u002F\u002Fdoi.org\u002F10.33765\u002Fthate.16.4.4","Groundwater is one of the most reliable freshwater resources globally, but overexploitation has led to declining water tables, deteriorating water quality, and increased water scarcity. These problems have raised serious questions about how sustainable groundwater management can be achieved. In recent times, geospatial techniques and the Analytical Hierarchy Process (AHP) have been increasingly applied to assess groundwater availability. This research aims to delineate groundwater potential zones in the Bhilangana Block using the above-mentioned methods. Initially, remote sensing data were employed to generate thematic layers, including geomorphology, geology, slope, elevation, drainage density, lineament density, soil, rainfall, and land use\u002Fland cover. Subsequently, these layers were integrated using a multi-criteria evaluation approach, and weights were assigned using AHP-based weighted overlay analysis. The resulting groundwater potential zones were classified into five categories: very low and low (over 45 %), moderate (35.05 %), high (16.66 %), and very high (3.81 %). The findings of this research offer insights into groundwater occurrence in the study area, which could be helpful for sustainable groundwater management and long-term planning of water resources.","地下水是全球最可靠的淡水资源之一，但过度开采已导致地下水位下降、水质恶化以及水资源短缺加剧。这些问题引发了关于如何实现可持续地下水管理的严肃思考。近年来，地理空间技术和层次分析法（Analytical Hierarchy Process, AHP）越来越多地被应用于地下水可获得性评估。本研究旨在利用上述方法圈定Bhilangana区块的地下水潜力区。首先，利用遥感数据生成专题图层，包括地貌、地质、坡度、高程、河网密度、线性构造密度、土壤、降雨以及土地利用\u002F土地覆盖。随后，采用多准则评价方法整合这些图层，并利用基于AHP的加权叠加分析分配权重。所得到的地下水潜力区被划分为五类：极低和低（超过45 %）、中等（35.05 %）、高（16.66 %）以及极高（3.81 %）。本研究结果有助于认识研究区地下水的赋存情况，可为可持续地下水管理和水资源长期规划提供参考。","The holistic approach to environment",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":209},"该论文研究印度北阿坎德邦地下水潜力区划，属水文地质与遥感应用领域，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。",[211],{"name":207,"url":204},[27,213,214],"水资源管理","GIS",[216,217],"水资源管理 遥感 GIS","水资源管理 遥感","水资源管理遥感GIS-2793","10.33765\u002Fthate.16.4.4",{"doi":219,"openalex_id":221,"authors":222,"venue":207,"cited_by_count":36,"oa_url":231,"card":232,"direction":51,"ingested_from":53},"W7213317885",[223,226,229],{"name":224,"orcid":225},"Siddharth Rana","https:\u002F\u002Forcid.org\u002F0009-0006-5304-3233",{"name":227,"orcid":228},"Gaurav Gaurav","https:\u002F\u002Forcid.org\u002F0000-0001-8857-5797",{"name":230,"orcid":9},"Mohan Singh Panwar","https:\u002F\u002Fcasopis.hrcpo.com\u002Fwp-content\u002Fuploads\u002F2026\u002F09\u002FRana-et-al_HAE_16_2026_4.pdf",{"tldr":233,"method":234,"finding":235,"direction":51,"opportunity":236},"用AHP与GIS遥感数据划分印度Bhilangana区块地下水潜力区。","遥感专题图层+AHP加权叠加多准则评价。","潜力区以低和极低为主（超45%），极高仅3.81%。","可结合灌溉需求与作物分布，将地下水潜力图用于农业用水优化与井位选址。","2026-09-17T23:30:36.203631Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":248,"tags":250,"search_phrases":255,"slug":258,"view_count":36,"doi":259,"paper":260,"created_at":277},2792,"Developing a desertification index integrating intra-annual dynamics of vegetation cover and soil moisture","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71674-0","Desertification is a key expression of dryland ecosystem degradation, affecting land productivity and ecological sustainability. Reliable assessment requires indicators that capture both vegetation status and water limitation, yet the seasonal coupling between vegetation cover (VC) and soil moisture (SM) remains insufficiently understood. This study analyzed VC–SM interactions in Inner Mongolia, northern China, from 2001 to 2025. SM was estimated using the temperature vegetation drought index derived from MODIS-NDVI and MODIS-LST data. Temporal trend analysis and time-lag cross-correlation were applied to characterize seasonal VC–SM dynamics across land-cover types, and four desertification indices were developed. Intra-annual SM fluctuations closely tracked vegetation phenology. Increased SM promoted vegetation growth, while vegetation development enhanced water consumption and slowed SM accumulation. The difference between growing-season maximum VC and minimum SM was strongly correlated with peak VC (r = 0.97, p \u003C 0.01), indicating its ability to reflect soil productivity and vegetation–water coupling strength. VC–SM coupling varied among land-cover types, with stronger and longer-lagged responses in forest and cultivated lands than in grasslands and unused lands. Among the four indices, the Moisture Vegetation Normalized Difference Desertification Index (MV-NDDI) showed comparatively stable performance in capturing VC–SM relationships and distinguishing desert from non-desert areas, although the reported correlations were modest in magnitude. These findings suggest that MV-NDDI may provide a useful indicator for desertification monitoring in water-limited dryland ecosystems.","荒漠化是旱地生态系统退化的关键表现，影响土地生产力和生态可持续性。可靠的评估需要既能反映植被状况又能反映水分限制的指标，然而植被覆盖度（VC）与土壤水分（SM）之间的季节性耦合关系仍认识不足。本研究分析了2001年至2025年中国北方内蒙古地区VC–SM的相互作用。SM利用基于MODIS-NDVI和MODIS-LST数据计算得到的温度植被干旱指数进行估算。应用时间趋势分析和时滞互相关分析表征不同土地覆盖类型下季节性VC–SM动态，并构建了四个荒漠化指数。年内SM波动与植被物候密切相关。SM增加促进植被生长，而植被发育增强水分消耗并减缓SM积累。生长季最大VC与最小SM之间的差值与峰值VC高度相关（r = 0.97，p \u003C 0.01），表明其能够反映土壤生产力和植被–水分耦合强度。VC–SM耦合在不同土地覆盖类型间存在差异，林地和耕地的响应强于草地和未利用地，且滞后时间更长。在四个指数中，水分植被归一化差异荒漠化指数（MV-NDDI）在捕捉VC–SM关系和区分荒漠与非荒漠区域方面表现出相对稳定的性能，尽管所报告的相关性强度适中。这些发现表明，MV-NDDI可为水分受限的旱地生态系统荒漠化监测提供有用的指标。","Scientific Reports",79,{"impact":67,"substance":101,"depth":100,"authority":172,"freshness":21,"relevant":22,"comment":247},"基于MODIS遥感构建植被-土壤水分耦合的荒漠化指数，方法有新意、数据跨度长，对旱区生态监测有参考价值，但属区域性学术进展，非全国性重大事件。",[249],{"name":244,"url":241},[27,251,252,253,254],"土壤墒情","植被覆盖","生态评估","荒漠化监测",[256,257],"荒漠化监测 土壤墒情 植被覆盖 生态评估","荒漠化监测 土壤墒情","荒漠化监测土壤墒情植被覆盖生态评估-2792","10.1038\u002Fs41598-026-71674-0",{"doi":259,"openalex_id":261,"authors":262,"venue":244,"cited_by_count":36,"oa_url":271,"card":272,"direction":51,"ingested_from":53},"W7213310812",[263,265,267,269],{"name":264,"orcid":9},"Tianwang Lei",{"name":266,"orcid":9},"Chong Zhang",{"name":268,"orcid":9},"Chao Li",{"name":270,"orcid":9},"Xiangying Li","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71674-0_reference.pdf",{"tldr":273,"method":274,"finding":275,"direction":51,"opportunity":276},"构建融合植被覆盖与土壤湿度年内动态的荒漠化指数MV-NDDI，并在内蒙古验证。","用MODIS-NDVI与LST估算土壤湿度，结合趋势分析与时滞互相关，构建四个指","生长季最大植被覆盖与最小土壤湿度之差与峰值植被覆盖高度相关，MV-NDDI表现较稳定。","可引入多源遥感与机器学习提升土壤湿度估算精度，并验证MV-NDDI在其他干旱区的适用性。","2026-09-17T23:30:36.136113Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":285,"sources":287,"tags":289,"search_phrases":294,"slug":297,"view_count":36,"doi":298,"paper":299,"created_at":325},2790,"High-resolution land cover mapping from coarse labels via a noisy label learning-guided cross-scale framework","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2726002","High-resolution remote sensing images (HRSIs) provide essential data support for land cover mapping, where deep learning has shown great promise. However, deep learning-based methods rely on abundant high-quality annotations, while low-resolution coarse labels are difficult to use directly in HRSIs training. In this paper, a novel noisy label learning-guided cross-scale framework (NL-CSF) is proposed, which is designed to achieve high-resolution land cover mapping from coarse labels. First, a spectral-based label mask filtering strategy is developed to preliminarily optimize coarse labels. Then, an adaptive noise evaluation scheme is introduced that assigns loss weights based on the noise differences between image and label patches in the training set. Finally, we design a cross-scale transfer Transformer (CSTT) model based on the vision Transformer (ViT) architecture, and the training process is guided by a noise-weighted loss function. Two cross-scale datasets are utilized to evaluate the performance of NL-CSF in multiple spatial scale differences (10 m to 3 m, 3 m to 0.5 m, and 10 m to 0.5 m). Experimental results demonstrate that NL-CSF improves overall accuracy (OA) by at least 7%, 6%, and 4% across the three cross-scale tasks in the first dataset, and by at least 2%, 9%, and 4% in the second dataset, respectively, compared with existing methods. Furthermore, the proposed framework is applied to cross-scale mapping across Jianye District of Nanjing (urban), Sheyang County of Yancheng (agricultural), and the Yellow River Delta of Dongying (wetland), leveraging a low-resolution land cover product and high-resolution PlanetScope images to generate more precise land cover maps. These results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy coarse labels and generating reliable high-resolution land cover maps.","高分辨率遥感影像(high-resolution remote sensing images, HRSIs)为土地覆盖制图提供了重要的数据支撑，深度学习在此领域展现出巨大潜力。然而，基于深度学习的方法依赖于大量高质量标注，而低分辨率粗标签难以直接用于高分辨率遥感影像训练。本文提出了一种新的噪声标签学习引导的跨尺度框架(noisy label learning-guided cross-scale framework, NL-CSF)，旨在从粗标签实现高分辨率土地覆盖制图。首先，提出了一种基于光谱的标签掩膜过滤策略，对粗标签进行初步优化。然后，引入了一种自适应噪声评估方案，根据训练集中影像块与标签块之间的噪声差异分配损失权重。最后，基于视觉Transformer(vision Transformer, ViT)架构设计了跨尺度迁移Transformer(cross-scale transfer Transformer, CSTT)模型，并以噪声加权损失函数引导训练过程。利用两个跨尺度数据集评估NL-CSF在多种空间尺度差异(10 m至3 m、3 m至0.5 m、10 m至0.5 m)下的性能。实验结果表明，与现有方法相比，NL-CSF在第一个数据集的三个跨尺度任务中总体精度(overall accuracy, OA)分别至少提升7%、6%和4%，在第二个数据集中分别至少提升2%、9%和4%。此外，将所提框架应用于南京建邺区(城市)、盐城射阳县(农业)和东营黄河三角洲(湿地)的跨尺度制图，利用低分辨率土地覆盖产品和高分PlanetScope影像生成更精确的土地覆盖图。这些结果证明了所提框架在减轻噪声粗标签影响和生成可靠高分辨率土地覆盖图方面的有效性。","GIScience & Remote Sensing",{"impact":100,"substance":101,"depth":100,"authority":172,"freshness":21,"relevant":22,"comment":286},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图，精度提升显著，对农业遥感监测有实用价值。",[288],{"name":284,"url":281},[290,291,27,292,293],"农业人工智能","深度学习","土地覆盖","高分辨率制图",[295,296],"农业人工智能 高分辨率制图 土地覆盖 深度学习","农业人工智能 高分辨率制图","农业人工智能高分辨率制图土地覆盖深度学习-2790","10.1080\u002F15481603.2026.2726002",{"doi":298,"openalex_id":300,"authors":301,"venue":284,"cited_by_count":36,"oa_url":281,"card":320,"direction":51,"ingested_from":53},"W7213283296",[302,305,308,311,314,316,318],{"name":303,"orcid":304},"Xiangyu Nie","https:\u002F\u002Forcid.org\u002F0009-0001-5095-6401",{"name":306,"orcid":307},"Cong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-5386-7343",{"name":309,"orcid":310},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8162-9422",{"name":312,"orcid":313},"Hong Fang","https:\u002F\u002Forcid.org\u002F0000-0003-3707-0910",{"name":315,"orcid":9},"Zhen Dong",{"name":317,"orcid":9},"Sicong Liu",{"name":319,"orcid":9},"Zhaohui Xue",{"tldr":321,"method":322,"finding":323,"direction":51,"opportunity":324},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图。","谱掩膜过滤粗标签、自适应噪声评估加权损失、基于ViT的跨尺度迁移Transfor","在多个跨尺度任务上总体精度提升2%-9%，并在城市、农业、湿地场景生成更精确土地覆盖图。","可探索将粗标签跨尺度学习用于作物精细分类与长时序农情监测，降低高精度标注依赖。","2026-09-17T23:30:34.952106Z"]