[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2173":3,"related-2173":90},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":60,"slug":63,"view_count":22,"doi":64,"paper":65,"created_at":89},2173,"Integrating Satellite Data into Near-Term, Iterative Water Temperature Forecasting Workflows Improves the Scalability of Freshwater Forecasts","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15008569\u002Fv1","Freshwater ecosystems are experiencing rapid changes and increased variability due to human activities. Near-term iterative forecasting has been shown to be a valuable tool to predict how these ecosystems will change in the future, and can be used to improve both water management and ecosystem understanding. Many freshwater forecasting systems require high-frequency in situ sensor data as input, which limits their scalability to only sites with existing sensor monitoring infrastructure. To scale these forecasting methods to waterbodies with little or no in situ data, new tools are needed. Here, we evaluated how the assimilation of satellite data into a process-based hydrodynamic model using the open-source Forecasting Lake And Reservoir Ecosystems (FLARE) system affected forecast performance for 10 lakes across the US. We forecasted water temperature across water column depths every week for 1- to 14-days ahead over two years with forecast model initial conditions and parameters updated from the assimilation of the Landsat-8\u002F9 Surface Temperature product. Comparisons of forecasts with satellite data assimilation vs. forecasts with no data assimilation reveal that satellite data can improve forecast performance across 1- to 14-day horizons. However, forecast accuracy gains were variable among lakes; at eight sites satellite data assimilation either improved or was equivalent to forecasts with no data assimilation, but at two sites satellite data worsened performance. Differences in forecast performance were most strongly correlated with water clarity and local meteorology. This study demonstrates the promise of satellite remote sensing as a key tool to improve scalability of near-term iterative freshwater forecasting.","淡水生态系统正因人类活动而经历快速变化和变异性增加。近期迭代预测已被证明是预测这些生态系统未来变化的有价值工具，并可用于改进水资源管理和生态系统理解。许多淡水预测系统需要高频原位传感器数据作为输入，这将其可扩展性限制在已有传感器监测基础设施的站点。为了将这些预测方法扩展到几乎没有或完全没有原位数据的水体，需要新的工具。在此，我们评估了利用开源“湖泊与水库生态系统预测”（FLARE）系统将卫星数据同化到基于过程的水动力模型中，如何影响美国10个湖泊的预测性能。我们在两年内每周预测各水柱深度的水温，预测时效为1至14天，并利用Landsat-8\u002F9地表温度产品的同化来更新预测模型的初始条件和参数。将有卫星数据同化的预测与无数据同化的预测进行比较，结果表明卫星数据可以在1至14天的预测时效内提高预测性能。然而，预测精度的提升在湖泊之间存在差异；在8个站点，卫星数据同化要么改善了预测，要么与无数据同化的预测相当，但在2个站点，卫星数据使预测性能变差。预测性能的差异与水体透明度和当地气象条件相关性最强。本研究表明，卫星遥感有望成为提高近期迭代淡水预测可扩展性的关键工具。",null,"OpenAlex","2026-09-09T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,8,1,"将卫星遥感数据同化进FLARE模型，验证了提升淡水水温预报可扩展性的新方法，对水产养殖与水域生态管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧渔业","遥感","水质监测","数据同化","淡水养殖",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":59},[39,47,54],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I3",7,true,[],[28,45],"remote sensing",[28],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":53},"H3",3,[],[52],"水资源",[],{"code":55,"score":22,"supported":14,"title":56,"abstract":57,"tags":58},"I5",[],[],[30],"农业相关性需要复核",[61,62],"数据同化 智慧渔业 水质监测 淡水养殖","数据同化 智慧渔业","数据同化智慧渔业水质监测淡水养殖-2173","10.22541\u002Fessoar.15008569\u002Fv1",{"doi":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":80,"oa_url":81,"card":82,"direction":86,"ingested_from":88},"W7212081241",[68,71,74,77],{"name":69,"orcid":70},"Molly Stroud","https:\u002F\u002Forcid.org\u002F0000-0001-7389-1586",{"name":72,"orcid":73},"Cayelan C. Carey","https:\u002F\u002Forcid.org\u002F0000-0001-8835-4476",{"name":75,"orcid":76},"George H. Allen","https:\u002F\u002Forcid.org\u002F0000-0001-8301-5301",{"name":78,"orcid":79},"R. Quinn Thomas","https:\u002F\u002Forcid.org\u002F0000-0003-1282-7825",0,"https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15008569\u002Fv1",{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"将卫星数据同化进FLARE模型，提升无传感器湖泊水温预报的可扩展性。","用FLARE系统同化Landsat-8\u002F9地表温度，对10个湖泊做1-14天水温","卫星同化整体提升预报精度，但效果因湖而异，与水体透明度和气象相关。","农业遥感与作物表型","可探索卫星同化在无监测水体中的适用条件，结合水体光学与气象因子优化预报策略。","openalex","2026-09-11T23:30:32.851926Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,167,241,317,394,454],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":105,"tags":107,"primary_topic":9,"secondary_topics":111,"classification":112,"search_phrases":125,"slug":128,"view_count":129,"doi":130,"paper":131,"created_at":166},5174,"Remote sensing of aquatic nutrients from proxy estimation to integration with process models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43247-026-04152-0","Leveraging its capability for large-scale, repeated monitoring, remote sensing has increasingly supported nitrogen and phosphorus estimation in surface waters. However, the evolution of this field from proxy-based nutrient retrieval to integration with process-based models remains insufficiently synthesized. Here we review remote sensing approaches for estimating aquatic nutrients, with emphasis on their roles in supporting process-based modeling, and compare their methodologies, applications, and limitations. At the watershed scale, remote sensing supports land use mapping and pollution load estimation, identifying nutrient sources and constraining transport simulations. At the lake scale, it provides continuous observations and water quality products that inform hydrodynamic and water quality models. At the field and interface scales, remote sensing helps identify hotspots of nutrient loss by capturing the heterogeneity of management practices. Despite these advances, nutrient estimation remains constrained by proxy relationships and spatial scale. Future research should integrate remote sensing with hydrodynamic and biogeochemical models to constrain nutrient fluxes and improve mechanistic understanding of aquatic nutrient cycling. At the watershed scale, remote sensing maps land use and estimates pollution loads; at the lake scale, it provides water quality data for models; at field and interface scales, it identifies nutrient-loss hotspots, based on a review of remote sensing to approaches aquatic nutrients.","凭借大规模、重复监测的能力，遥感日益支撑地表水中氮磷的估算。然而，该领域从基于代理指标的养分反演向与过程模型融合的演进仍缺乏充分综合。本文综述了用于估算水生养分的遥感方法，重点阐述其在支撑过程模型方面的作用，并比较其方法、应用与局限。在流域尺度，遥感支撑土地利用制图与污染负荷估算，识别养分来源并约束输移模拟。在湖泊尺度，遥感提供连续观测与水质产品，为水动力与水质模型提供输入。在田块与界面尺度，遥感通过捕捉管理措施的异质性，帮助识别养分流失热点。尽管取得上述进展，养分估算仍受代理关系与空间尺度的制约。未来研究应将遥感与水动力及生物地球化学模型相结合，以约束养分通量并提升对水生养分循环机理的认识。在流域尺度，遥感可制图土地利用并估算污染负荷；在湖泊尺度，遥感为模型提供水质数据；在田块与界面尺度，遥感可识别养分流失热点——上述结论基于对水生养分遥感方法的综述。","Communications Earth & Environment","2026-10-15T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":103,"freshness":21,"relevant":22,"comment":104},14,"系统综述遥感估算水体氮磷及其与过程模型耦合的进展与局限，方法学价值明确，但属综述类论文，公共影响有限。",[106],{"name":99,"url":96},[28,29,108,109,110],"水环境","面源污染","氮磷养分",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":113},{"candidates":114,"agricultural_evidence":14,"reason":59},[115,120],{"code":40,"score":116,"supported":42,"title":117,"abstract":118,"tags":119},15,[45],[28,45],[28],{"code":121,"score":22,"supported":14,"title":122,"abstract":123,"tags":124},"H5",[],[],[109],[126,127],"遥感 水体营养盐 估算","氮磷 遥感 过程模型","遥感水体营养盐估算-5174",4,"10.1038\u002Fs43247-026-04152-0",{"doi":130,"openalex_id":132,"authors":133,"venue":99,"cited_by_count":80,"oa_url":160,"card":161,"direction":86,"ingested_from":88},"W7221994887",[134,136,139,142,144,146,148,150,152,154,156,158],{"name":135,"orcid":9},"Chenyi Shi",{"name":137,"orcid":138},"Xihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4795-304X",{"name":140,"orcid":141},"Y. Jun Xu","https:\u002F\u002Forcid.org\u002F0000-0003-3646-626X",{"name":143,"orcid":9},"Xingyu Zhu",{"name":145,"orcid":9},"Shunqing Jia",{"name":147,"orcid":9},"Zejun Liu",{"name":149,"orcid":9},"Boyang Mao",{"name":151,"orcid":9},"Yan Dai",{"name":153,"orcid":9},"Yanxin Rong",{"name":155,"orcid":9},"Qinya Lv",{"name":157,"orcid":9},"Xuming Ji",{"name":159,"orcid":9},"Chengming Luo","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04152-0.pdf",{"tldr":162,"method":163,"finding":164,"direction":86,"opportunity":165},"综述遥感估算水体氮磷的方法，及其与过程模型在多尺度上的集成。","文献综述，按流域、湖泊、田块界面三个尺度比较遥感方法与局限。","遥感仍受代理关系与空间尺度制约，需与水文和生物地球化学模型集成。","可研究遥感与过程模型耦合以约束营养盐通量，填补多尺度集成与机理理解的空白。","2026-10-17T23:30:29.898801Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":179,"tags":181,"primary_topic":9,"secondary_topics":185,"classification":186,"search_phrases":220,"slug":223,"view_count":80,"doi":224,"paper":225,"created_at":240},5157,"Digital twin forestry as an informatics framework integrating remote sensing ecosystem modelling and data assimilation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44564-026-00029-8","Forests are essential to the Earth system, regulating carbon, water, and biodiversity cycles while underpinning ecosystem resilience. Yet they face mounting pressures from climate and land-use change, requiring integrative informatics approaches that link high-resolution observations with advanced ecosystem models. This systematic review defines Digital Twin Forestry (DTF) as an emerging paradigm that continuously couples multisensor Earth Observation data with ecosystem models through data assimilation, enabling dynamic, uncertainty-aware forecasting and adaptive decision support. Following PRISMA 2020 guidelines, we synthesized 57 peer-reviewed studies published between 2016 and 2025 that combined EO datasets with ecosystem modeling and assimilation frameworks. Demonstrated advances include CubeSat constellations, spaceborne LiDAR, UAV hyperspectral systems, and hybrid machine-learning surrogates. Data assimilation methods such as Ensemble Kalman Filters and Particle Filters serve as computational bridges between multi-temporal observations and dynamic model states. Results reveal uneven biome representation, with tropical and boreal forests underrepresented compared to temperate zones, underscoring equity concerns in biodiversity-rich regions. While most forestry digital twins remain at prototype or early operational stages, continuous coupling of sensing platforms to models demonstrates the potential for near-real-time forecasting that extends beyond static inventories. Future priorities include physics-informed machine learning, FAIR-compliant cloud-native workflows, and expanded monitoring in underrepresented ecosystems. Embedding biodiversity and resilience monitoring into near-real-time, uncertainty-aware workflows will be critical, enabling DTF to evolve into operational systems that provide adaptive, policy-relevant decision support for climate-resilient forest management. This review offers an integrative roadmap for advancing DTF, contributing to global sustainability and supporting the United Nations Sustainable Development Goals, including Climate Action (SDG 13) and Life on Land (SDG 15).","森林对地球系统至关重要，调节着碳、水和生物多样性循环，同时支撑着生态系统韧性。然而，森林面临气候和土地利用变化日益增大的压力，需要将高分辨率观测与先进生态系统模型相联系的综合性信息学方法。本系统综述将数字孪生林业（Digital Twin Forestry, DTF）定义为一个新兴范式，即通过数据同化将多传感器地球观测数据与生态系统模型持续耦合，从而实现动态的、具有不确定性感知的预测和适应性决策支持。遵循PRISMA 2020指南，我们综合了2016年至2025年间发表的57项同行评审研究，这些研究将地球观测数据集与生态系统建模和数据同化框架相结合。已展示的进展包括立方星（CubeSat）星座、星载激光雷达、无人机高光谱系统和混合机器学习代理模型。集合卡尔曼滤波和粒子滤波等数据同化方法充当多时相观测与动态模型状态之间的计算桥梁。结果显示生物群系代表性不均衡，热带和寒带森林相对于温带地区代表性不足，凸显了生物多样性丰富地区的公平性问题。尽管大多数林业数字孪生仍处于原型或早期运行阶段，但传感平台与模型的持续耦合展示了超越静态资源清查的近实时预测潜力。未来优先方向包括物理信息机器学习、符合FAIR原则的云原生工作流程，以及扩大代表性不足生态系统的监测。将生物多样性和韧性监测嵌入近实时、不确定性感知的工作流程将至关重要，使DTF能够发展为可操作的系统，为气候韧性森林管理提供适应性、政策相关的决策支持。本综述为推进DTF提供了综合性路线图，助力全球可持续发展并支持联合国可持续发展目标，包括气候行动（SDG 13）和陆地生物（SDG 15）。","Discover Informatics",79,{"impact":19,"substance":18,"depth":176,"authority":177,"freshness":21,"relevant":22,"comment":178},19,12,"系统综述提出数字孪生林业框架，方法新颖、结论有据，对农业信息化中的遥感与生态建模有参考价值，但属学术综述而非产业级事件。",[180],{"name":173,"url":170},[182,28,183,30,184],"数字孪生","森林监测","生态系统建模",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":187},{"candidates":188,"agricultural_evidence":14,"reason":59},[189,195,200,209,216],{"code":190,"score":116,"supported":42,"title":191,"abstract":193,"tags":194},"I1",[192],"digital twin",[182,192],[182],{"code":40,"score":177,"supported":42,"title":196,"abstract":197,"tags":199},[45],[198],"hyperspectral",[28],{"code":201,"score":202,"supported":42,"title":203,"abstract":204,"tags":208},"I2",9,[],[205,206,207],"机器学习","machine learning","decision support",[],{"code":210,"score":91,"supported":42,"title":211,"abstract":212,"tags":215},"H6",[],[213,214],"生物多样性","biodiversity",[],{"code":55,"score":22,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[30],[221,222],"遥感 数据同化 森林","生态系统建模 数字孪生 数据同化 森林监测","遥感数据同化森林-5157","10.1007\u002Fs44564-026-00029-8",{"doi":224,"openalex_id":226,"authors":227,"venue":173,"cited_by_count":80,"oa_url":233,"card":234,"direction":239,"ingested_from":88},"W7221993112",[228,231],{"name":229,"orcid":230},"Niamjit Das","https:\u002F\u002Forcid.org\u002F0000-0001-7619-8306",{"name":232,"orcid":9},"Kevin Andrew","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44564-026-00029-8.pdf",{"tldr":235,"method":236,"finding":237,"direction":86,"opportunity":238},"系统综述数字孪生林业，提出遥感与生态模型同化耦合框架。","PRISMA 2020 综述 57 篇文献，整合多源遥感、生态模型与数据同化。","多数林业数字孪生仍处原型阶段，热带与寒带森林代表性不足。","可探索物理知情机器学习与云原生工作流，填补热带、寒带森林近实时同化监测空白。","智慧农业 \u002F 农业物联网","2026-10-17T23:30:10.474821Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":252,"tags":254,"primary_topic":258,"secondary_topics":263,"classification":264,"search_phrases":286,"slug":289,"view_count":22,"doi":290,"paper":291,"created_at":316},4503,"FUSING DEEP LEARNING AND REMOTE SENSING DATA TO ESTIMATE NON-OPTICALLY ACTIVE WATER QUALITY VARIABLES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23243307","The efficient monitoring of inland water bodies such as rivers and lakes is crucial not only for ensuring human health and ecosystem balance, but also for the effective management of water resources. Two water quality variables of high importance and interest are Dissolved Oxygen (DO) and pH. The former variable is an ecosystem health index as it is connected to hypoxia events, while the latter one is linked to biochemical processes and the acidity of water. Currently, conventional water quality monitoring methods (e.g., via in-situ point sampling) are accurate for the particular point, but costly and time-consuming. On the other hand, satellite remote sensing provides a remedy, as it offers high spatial and temporal coverage. Most of the water quality applications of remote sensing data have focused on optically active parameters like Chlorophyll-a and Colored Dissolved Organic Matter (CDOM), but the estimation of non-optically active variables such as pH and DO poses a significant challenge. This study introduces a novel approach, that fuses Earth Observation (EO) and in-situ data, in a deep learning context, to provide estimations of non-optically active variables, characterized by high accuracy, high spatial coverage and low latency. The approach also considers the data imbalance challenge, which is common in environmental datasets. The approach is applied to Lake Yliki in central Greece. The Boeotikos Kifissos river basin, which lies upstream of Lake Yliki, is dominated by intense agricultural activity. The lake is also part of Athens’ water distribution system, making its efficient monitoring even more important.","对河流、湖泊等内陆水体的高效监测，不仅对保障人类健康和生态系统平衡至关重要，也对水资源的有效管理不可或缺。溶解氧（Dissolved Oxygen, DO）和pH值是两项备受关注且极为重要的水质变量。前者作为生态系统健康指标，与缺氧事件密切相关；后者则与生物化学过程及水体酸度相关。目前，传统的水质监测方法（如原位定点采样）对特定点位而言较为准确，但成本高昂且耗时费力。相比之下，卫星遥感提供了一种解决方案，因其具备较高的时空覆盖能力。遥感数据在水质方面的应用大多集中于光学活性参数，如叶绿素a和有色溶解有机物（Colored Dissolved Organic Matter, CDOM），而对pH值和溶解氧等非光学活性变量的估算则构成重大挑战。本研究提出了一种新方法，在深度学习框架下融合地球观测（Earth Observation, EO）数据与原位数据，以实现对非光学活性变量的高精度、高空间覆盖度和低延迟估算。该方法还考虑了环境数据集中常见的数据不平衡问题。该方法已应用于希腊中部的伊利基湖（Lake Yliki）。位于伊利基湖上游的维奥蒂亚基菲索斯河流域（Boeotikos Kifissos river basin）以密集的农业活动为主。该湖也是雅典供水系统的组成部分，因此对其开展高效监测尤为重要。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":251},"将深度学习与遥感、地面实测数据融合估算内陆水体非光学活性水质参数，方法新颖且面向农业流域与饮用水源监测，具备较高专业参考价值。",[253],{"name":247,"url":244},[255,256,28,257,29],"深度学习","农业面源污染","水资源管理",{"code":259,"slug":260,"name":261,"path":262,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":265,"locked":14,"version":35,"method":36,"evidence":266},"partial",{"candidates":267,"agricultural_evidence":42,"reason":285},[268,273,277,281],{"code":201,"score":116,"supported":42,"title":269,"abstract":271,"tags":272},[270],"deep learning",[255,270],[255],{"code":40,"score":116,"supported":42,"title":274,"abstract":275,"tags":276},[45],[28,45],[28],{"code":48,"score":129,"supported":42,"title":278,"abstract":279,"tags":280},[],[52],[257],{"code":121,"score":22,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[256],"一级领域明确，二级方向需要细分",[287,288],"Yliki湖 水质 遥感","溶解氧 pH 遥感 估算","Yliki湖水质遥感-4503","10.5281\u002Fzenodo.23243307",{"doi":290,"openalex_id":292,"authors":293,"venue":247,"cited_by_count":80,"oa_url":244,"card":311,"direction":86,"ingested_from":88},"W7220943541",[294,297,300,303,305,308],{"name":295,"orcid":296},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":298,"orcid":299},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":301,"orcid":302},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":304,"orcid":9},"Konstantinos Peroulis",{"name":306,"orcid":307},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":309,"orcid":310},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":312,"method":313,"finding":314,"direction":86,"opportunity":315},"融合深度学习与遥感及实地数据，估算湖泊非光学活性水质变量（溶解氧和pH）。","深度学习融合地球观测与实地数据，处理数据不平衡，应用于希腊Yliki湖。","该方法能高精度、高覆盖、低延迟地估算非光学活性水质变量。","可探索将此类方法迁移至农业流域水质监测，结合农业活动数据提升预警能力。","2026-10-09T23:30:25.773729Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":341,"classification":342,"search_phrases":366,"slug":369,"view_count":129,"doi":370,"paper":371,"created_at":393},4360,"Assessment of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America","https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjhm-d-26-0062.1","Abstract Estimation of surface soil moisture (SSM) is fundamental for hydrological monitoring and agricultural management, particularly in drought-prone regions such as central–northeastern Argentina. Data assimilation provides a robust approach for estimating SSM by integrating land surface models with remote sensing data. Satellite missions such as SMAP and SMOS deliver global SSM products; however, these observations often exhibit systematic biases arising from instrument noise, indirect measurements, and temporal and spatial heterogeneities. Since standard data assimilation techniques assume unbiased observations, bias correction is required to ensure optimal assimilation. This work evaluates off-line observation bias-correction methods within a land data assimilation framework based on the Noah-MP land surface model and an ensemble Kalman filter, focusing on the 2022 dry season in the endorheic Pampas region. During dry conditions, SSM probability density functions differ significantly among soil textures within the bin. To address sampling errors in cumulative distribution function (CDF) matching, we propose an approach in which climatological statistics are computed using homogeneous soil texture pixels, and a 45-day moving temporal window is applied to smooth CDF evolution. The proposed method is compared with standard CDF matching and normal deviate scaling. Results indicate that improved statistical sampling in CDF matching produces a measurable impact on SSM estimates, particularly during dry periods. It shows better agreement with independent in-situ soil measurements than standard CDF matching, provided that the soil texture classifications at the in-situ site and in the model grid cell are consistent.","摘要 地表土壤水分（SSM）估算对于水文监测和农业管理至关重要，尤其是在阿根廷中东北部等干旱易发地区。数据同化通过将陆面模型与遥感数据相结合，为估算SSM提供了一种稳健的方法。SMAP和SMOS等卫星任务可提供全球SSM产品；然而，这些观测往往因仪器噪声、间接测量以及时空异质性而存在系统性偏差。由于标准数据同化技术假设观测无偏，因此需要进行偏差校正以确保最优同化。本研究在基于Noah-MP陆面模型和集合卡尔曼滤波的陆面数据同化框架内，评估了离线观测偏差校正方法，重点关注内流潘帕斯地区2022年旱季。在干旱条件下，同一分箱内不同土壤质地的SSM概率密度函数存在显著差异。为解决累积分布函数（CDF）匹配中的采样误差问题，我们提出了一种方法：利用均质土壤质地像元计算气候统计量，并应用45天滑动时间窗口以平滑CDF演变。将所提方法与标准CDF匹配和正态偏差缩放进行了比较。结果表明，CDF匹配中改进的统计采样对SSM估算产生了可测量的影响，尤其是在干旱时期。与原位站点和模型网格单元土壤质地分类一致的前提下，该方法与原位土壤实测数据的吻合程度优于标准CDF匹配。","Journal of Hydrometeorology","2026-10-06T00:00:00Z",69,{"impact":21,"substance":327,"depth":328,"authority":103,"freshness":202,"relevant":22,"comment":329},21,17,"面向干旱区卫星土壤水分同化的偏差校正方法研究，方法有新意但属区域尺度学术成果，对国内农业遥感墒情监测有参考价值。",[331],{"name":323,"url":320},[28,333,334,30],"农业气象","土壤墒情",{"code":336,"slug":337,"name":338,"path":339,"parent_code":340},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":343,"locked":14,"version":35,"method":36,"evidence":344},"classified",{"candidates":345,"agricultural_evidence":42},[346,351,358,362],{"code":336,"score":116,"supported":42,"title":347,"abstract":348,"tags":350},[337],[349,337],"土壤",[334],{"code":352,"score":41,"supported":42,"title":353,"abstract":354,"tags":357},"H7",[],[355,356],"干旱","drought",[333],{"code":40,"score":41,"supported":42,"title":359,"abstract":360,"tags":361},[],[28,45],[28],{"code":55,"score":22,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[30],[367,368],"SMAP SMOS 土壤水分 同化","南美 潘帕斯 土壤湿度 偏差校正","SMAPSMOS土壤水分同化-4360","10.1175\u002Fjhm-d-26-0062.1",{"doi":370,"openalex_id":372,"authors":373,"venue":323,"cited_by_count":80,"oa_url":9,"card":388,"direction":86,"ingested_from":88},"W7220742830",[374,377,379,382,385],{"name":375,"orcid":376},"Fabricio Matías Obregón","https:\u002F\u002Forcid.org\u002F0009-0005-9861-1356",{"name":378,"orcid":9},"Lucini Magdalena",{"name":380,"orcid":381},"Omar Vicente Müller","https:\u002F\u002Forcid.org\u002F0000-0001-9943-8368",{"name":383,"orcid":384},"Romina Carla Ruscica","https:\u002F\u002Forcid.org\u002F0000-0003-0127-9579",{"name":386,"orcid":387},"Manuel Pulido","https:\u002F\u002Forcid.org\u002F0000-0003-3992-8044",{"tldr":389,"method":390,"finding":391,"direction":86,"opportunity":392},"评估南美干旱季卫星土壤湿度同化中的偏差校正方法，提出基于均质土壤纹理和滑动窗口的改进CDF匹配。","Noah-MP模型与集合卡尔曼滤波同化框架，对比标准CDF匹配和正态偏差缩放。","改进CDF匹配在干旱期对土壤湿度估计更准确，与实测数据吻合更好，但需土壤纹理分类一致。","可探索不同气候区与土壤类型下偏差校正的普适性，并耦合作物模型提升干旱监测能力。","2026-10-07T23:30:26.212164Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":9,"secondary_topics":410,"classification":411,"search_phrases":423,"slug":426,"view_count":427,"doi":428,"paper":429,"created_at":453},3076,"Research on the Inversion Method of Total Phosphorus Concentration in Water Bodies Based on PSO-Adam-BP Neural Network Model","https:\u002F\u002Fdoi.org\u002F10.15244\u002Fpjoes\u002F220960","This study investigated Baiyangdian Lake in the Xiong’an New Area to invert total phosphorus (TP) concentrations using Sentinel-2 multispectral imagery and in situ measurements, aiming to support water-quality assessment and eutrophication management. Based on the multispectral remote sensing data of Sentinel-2 and measured total phosphorus data of water bodies, the study used specific feature combinations such as NDTI, B5, B11-B12 difference, and sensitive combination bands of measured data as model inputs to construct a PSO-Adam-BP neural network machine learning model for total phosphorus concentration inversion. Compared with the traditional BP and PSO-BP baseline models, the proposed framework significantly improved the accuracy of the model, with an R2 value reaching 0.9351. Moreover, it successfully reduced the average relative error by up to 53.5%, from 4.80% to 2.24%, and decreased the maximum relative error by up to 31.7%, from 11.21% to 7.66%, demonstrating the model’s highly robust and precise inversion performance. This study provides a new method for water quality detection in Baiyangdian Lake and is of great significance to the protection and development of the water environment in Xiong’an New Area.","本研究以雄安新区白洋淀为研究对象，利用Sentinel-2多光谱影像和实地测量数据反演总磷（TP）浓度，旨在为水质评价和富营养化管理提供支持。基于Sentinel-2多光谱遥感数据和白洋淀水体总磷实测数据，研究采用NDTI、B5、B11-B12差值等特征组合及实测数据的敏感组合波段作为模型输入，构建了PSO-Adam-BP神经网络机器学习模型用于总磷浓度反演。与传统BP和PSO-BP基准模型相比，所提出的框架显著提高了模型精度，R²值达到0.9351。此外，该模型成功将平均相对误差最多降低53.5%，从4.80%降至2.24%，并将最大相对误差最多降低31.7%，从11.21%降至7.66%，展现出高度稳健且精确的反演性能。本研究为白洋淀水质检测提供了新方法，对雄安新区水环境保护与开发具有重要意义。","Polish Journal of Environmental Studies","2026-09-18T00:00:00Z",72,{"impact":116,"substance":327,"depth":328,"authority":20,"freshness":91,"relevant":22,"comment":404},"基于Sentinel-2与PSO-Adam-BP模型实现白洋淀总磷浓度高精度反演，方法新颖、数据可靠，对雄安水环境智慧监测有实用价值。",[406],{"name":400,"url":397},[408,205,28,29,409],"农业人工智能","白洋淀",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":412},{"candidates":413,"agricultural_evidence":14,"reason":59},[414,419],{"code":40,"score":13,"supported":42,"title":415,"abstract":416,"tags":418},[],[28,45,417],"multispectral",[28],{"code":201,"score":21,"supported":42,"title":420,"abstract":421,"tags":422},[],[205,206],[408,205],[424,425],"白洋淀 总磷 遥感反演","Sentinel-2 水质 反演","白洋淀总磷遥感反演-3076",2,"10.15244\u002Fpjoes\u002F220960",{"doi":428,"openalex_id":430,"authors":431,"venue":400,"cited_by_count":80,"oa_url":447,"card":448,"direction":86,"ingested_from":88},"W7213537374",[432,434,436,439,442,444],{"name":433,"orcid":9},"Guanxing Wang",{"name":435,"orcid":9},"Cui Jia",{"name":437,"orcid":438},"Linghan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-8537-8787",{"name":440,"orcid":441},"Shan An","https:\u002F\u002Forcid.org\u002F0000-0001-7796-6952",{"name":443,"orcid":9},"Jia Xi",{"name":445,"orcid":446},"Yaxue Liu","https:\u002F\u002Forcid.org\u002F0009-0006-9025-8030","https:\u002F\u002Fwww.pjoes.com\u002Fpdf-220960-145347?filename=Research-on-the-Inversion.pdf",{"tldr":449,"method":450,"finding":451,"direction":86,"opportunity":452},"用PSO-Adam-BP神经网络结合Sentinel-2影像反演白洋淀总磷浓度。","Sentinel-2多光谱与实测数据，NDTI等特征组合，PSO-Adam-BP","R²达0.9351，平均相对误差由4.80%降至2.24%，精度显著优于BP与PSO-BP。","可迁移至其他内陆水体与多参数水质反演，探索时序泛化与跨区域迁移能力。","2026-09-21T23:30:25.856035Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":469,"secondary_topics":470,"classification":471,"search_phrases":491,"slug":494,"view_count":427,"doi":495,"paper":496,"created_at":530},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":103,"freshness":13,"relevant":22,"comment":464},"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。",[466],{"name":460,"url":457},[205,256,27,468,29],"遥感监测",{"code":259,"slug":260,"name":261,"path":262,"parent_code":9},[],{"status":265,"locked":14,"version":35,"method":36,"evidence":472},{"candidates":473,"agricultural_evidence":42,"reason":285},[474,478,482,487],{"code":201,"score":116,"supported":42,"title":475,"abstract":476,"tags":477},[206],[205,206],[205],{"code":40,"score":116,"supported":42,"title":479,"abstract":480,"tags":481},[45],[28,45],[468],{"code":48,"score":91,"supported":42,"title":483,"abstract":484,"tags":486},[],[52,485],"water resource",[],{"code":121,"score":22,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[256],[492,493],"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":495,"openalex_id":497,"authors":498,"venue":460,"cited_by_count":80,"oa_url":457,"card":525,"direction":86,"ingested_from":88},"W7213230686",[499,502,504,507,509,512,514,516,518,520,522],{"name":500,"orcid":501},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":503,"orcid":9},"Daganchew Aklog",{"name":505,"orcid":506},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":508,"orcid":9},"Minychl G. Dersseh",{"name":510,"orcid":511},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":513,"orcid":9},"Seleshi Yalew",{"name":515,"orcid":9},"Demesew A. Mhiret",{"name":517,"orcid":9},"Sisay B. Asress",{"name":519,"orcid":9},"Tigistu Wassie Agegnehu",{"name":521,"orcid":9},"Shawl Abebe Desta",{"name":523,"orcid":524},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":526,"method":527,"finding":528,"direction":86,"opportunity":529},"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"]