[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5174":3,"related-5174":97},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":96},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。",null,"Communications Earth & Environment","2026-10-15T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","水质监测","水环境","面源污染","氮磷养分",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":51},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"I3",15,true,[43],"remote sensing",[26,43],[26],{"code":47,"score":21,"supported":14,"title":48,"abstract":49,"tags":50},"H5",[],[],[29],"农业相关性需要复核",[53,54],"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":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7221994887",[61,63,66,69,71,73,75,77,79,81,83,85],{"name":62,"orcid":9},"Chenyi Shi",{"name":64,"orcid":65},"Xihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4795-304X",{"name":67,"orcid":68},"Y. Jun Xu","https:\u002F\u002Forcid.org\u002F0000-0003-3646-626X",{"name":70,"orcid":9},"Xingyu Zhu",{"name":72,"orcid":9},"Shunqing Jia",{"name":74,"orcid":9},"Zejun Liu",{"name":76,"orcid":9},"Boyang Mao",{"name":78,"orcid":9},"Yan Dai",{"name":80,"orcid":9},"Yanxin Rong",{"name":82,"orcid":9},"Qinya Lv",{"name":84,"orcid":9},"Xuming Ji",{"name":86,"orcid":9},"Chengming Luo",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04152-0.pdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"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。","农业遥感与作物表型","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。","openalex","2026-10-17T23:30:29.898801Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,159,237,298,387,439],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":9,"secondary_topics":117,"classification":118,"search_phrases":133,"slug":136,"view_count":137,"doi":138,"paper":139,"created_at":158},2433,"Hierarchical spectral ensemble with physics-informed augmentation for global water quality retrieval from hyperspectral remote sensing","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1860176","Retrieving water quality variables from remotely sensed reflectance spectra (Rrs) with global-scale, cross-waterbody applicability remains a grand challenge in environmental remote sensing. Here we introduce Hierarchical Spectral Ensemble (HSE), a machine learning pipeline for concurrent retrieval of four ecologically relevant water quality parameters (chlorophyll-a (Chl-a), total suspended solids (TSS), coloured dissolved organic matter absorption coefficient at 440 nm (aCDOM(440)), and Secchi depth (Zsd)), applied to the GLORIA 2022 global dataset of 7,572 co-located hyperspectral in situ ground truth measurements spanning six continents. HSE leverages a combination of five diverse base learners trained with 10-fold out-of-fold stacking, and integrated with a diversity-constrained non-negative least squares (NNLS) meta-learner, supported by a 291-dimensional feature suite spanning spectral, spatial, and temporal representations. We propose Interpolation-based Spectral Data Augmentation (ISDA), an ecologically inspired, class-balance oversampling methodology applied to the training set after the initial stratified split to prevent information leakage. Evaluated on the held-out global test set, HSE attains R2 = 0.822, 0.704, 0.841, and 0.962 for Chl-a, TSS, aCDOM(440), and Zsd respectively (mean R2 = 0.832; all in original physical units following back-transformation). Stratified analysis on a per-quartile basis reveals negative values of R2 in low-concentration regimes for Chl-a, TSS, and aCDOM(440), illustrating how global metrics can mask failures in retrieval across a majority of the distribution, specifically in the oligotrophic regime. This constitutes the primary known limitation of the method: reliable prediction in low-concentration, low-optical-signal regimes remains elusive and is demonstrated only in high-concentration, eutrophic samples where a measurable optical signal exists.","从遥感反射光谱（Rrs）中反演水质变量并实现全球尺度、跨水体的适用性，仍是环境遥感领域的一项重大挑战。本文提出分层光谱集成（Hierarchical Spectral Ensemble, HSE），一种机器学习流程，用于同时反演四个与生态相关的水质参数（叶绿素a（Chl-a）、总悬浮固体（TSS）、440 nm处有色溶解有机物吸收系数（aCDOM(440)）和透明度（Zsd）），并应用于GLORIA 2022全球数据集，该数据集包含横跨六大洲的7,572组同步高光谱原位地面实测数据。HSE结合了五种不同的基学习器，采用10折折外堆叠进行训练，并与多样性约束的非负最小二乘（NNLS）元学习器集成，辅以涵盖光谱、空间和时间表示的291维特征集。我们提出基于插值的光谱数据增强（Interpolation-based Spectral Data Augmentation, ISDA），这是一种受生态学启发的类平衡过采样方法，在初始分层划分后应用于训练集以防止信息泄漏。在留出的全球测试集上评估，HSE在Chl-a、TSS、aCDOM(440)和Zsd上分别达到R2 = 0.822、0.704、0.841和0.962（平均R2 = 0.832；均在反变换后以原始物理单位表示）。基于四分位数的分层分析揭示了Chl-a、TSS和aCDOM(440)在低浓度条件下R2为负值，说明全球指标可能掩盖分布中大多数区域的检索失败，特别是在贫营养条件下。这构成了该方法已知的主要局限性：在低浓度、低光学信号条件下实现可靠预测仍然难以实现，仅在存在可测量光学信号的高浓度、富营养化样本中得到验证。","Frontiers in Remote Sensing","2026-09-11T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":110,"freshness":20,"relevant":21,"comment":111},13,"基于GLORIA全球高光谱数据集提出分层光谱集成方法，可同步反演叶绿素a、悬浮物等四项水质参数，方法新颖、数据规模大，但低浓度水体反演失效的局限也交代清楚，对农业水环境遥感监测有参考价值。",[113],{"name":106,"url":103},[115,116,26,27,28],"农业人工智能","机器学习",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":119},{"candidates":120,"agricultural_evidence":14,"reason":51},[121,127],{"code":39,"score":122,"supported":41,"title":123,"abstract":125,"tags":126},26,[43,124],"hyperspectral",[26,43,124],[26],{"code":128,"score":20,"supported":41,"title":129,"abstract":130,"tags":132},"I2",[],[116,131],"machine learning",[115,116],[134,135],"农业人工智能 机器学习 水质监测 水环境","农业人工智能 机器学习","农业人工智能机器学习水质监测水环境-2433",3,"10.3389\u002Ffrsen.2026.1860176",{"doi":138,"openalex_id":140,"authors":141,"venue":106,"cited_by_count":87,"oa_url":152,"card":153,"direction":93,"ingested_from":95},"W7212323204",[142,144,146,148,150],{"name":143,"orcid":9},"Amirthalakshmi TM",{"name":145,"orcid":9},"Hemanth S",{"name":147,"orcid":9},"Ganesan PV",{"name":149,"orcid":9},"Rahul SG",{"name":151,"orcid":9},"Gayathri M","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1860176\u002Fpdf",{"tldr":154,"method":155,"finding":156,"direction":93,"opportunity":157},"提出分层光谱集成模型HSE，从高光谱遥感反射率中全球尺度反演四种水质参数。","用GLORIA 2022全球7572条高光谱数据，结合10折堆叠集成、NNLS元","整体R²达0.832，但低浓度贫营养水体中Chl-a、TSS、aCDOM(440)的R²为负，反演失","低浓度、弱光学信号水体的反演仍是空白，可探索物理约束与少样本学习提升贫营养水体精度。","2026-09-14T23:30:27.361699Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":166,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":170,"tags":172,"primary_topic":176,"secondary_topics":181,"classification":182,"search_phrases":206,"slug":209,"view_count":21,"doi":210,"paper":211,"created_at":236},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":17,"substance":18,"depth":17,"authority":110,"freshness":13,"relevant":21,"comment":169},"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。",[171],{"name":165,"url":162},[173,174,26,175,27],"深度学习","农业面源污染","水资源管理",{"code":177,"slug":178,"name":179,"path":180,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":183,"locked":14,"version":34,"method":35,"evidence":184},"partial",{"candidates":185,"agricultural_evidence":41,"reason":205},[186,191,195,201],{"code":128,"score":40,"supported":41,"title":187,"abstract":189,"tags":190},[188],"deep learning",[173,188],[173],{"code":39,"score":40,"supported":41,"title":192,"abstract":193,"tags":194},[43],[26,43],[26],{"code":196,"score":56,"supported":41,"title":197,"abstract":198,"tags":200},"H3",[],[199],"水资源",[175],{"code":47,"score":21,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[174],"一级领域明确，二级方向需要细分",[207,208],"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":210,"openalex_id":212,"authors":213,"venue":165,"cited_by_count":87,"oa_url":162,"card":231,"direction":93,"ingested_from":95},"W7220943541",[214,217,220,223,225,228],{"name":215,"orcid":216},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":218,"orcid":219},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":221,"orcid":222},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":224,"orcid":9},"Konstantinos Peroulis",{"name":226,"orcid":227},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":229,"orcid":230},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":232,"method":233,"finding":234,"direction":93,"opportunity":235},"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":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":250,"tags":252,"primary_topic":9,"secondary_topics":254,"classification":255,"search_phrases":267,"slug":270,"view_count":271,"doi":272,"paper":273,"created_at":297},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":40,"substance":247,"depth":248,"authority":110,"freshness":98,"relevant":21,"comment":249},21,17,"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。",[251],{"name":243,"url":240},[115,116,26,27,253],"白洋淀",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":256},{"candidates":257,"agricultural_evidence":14,"reason":51},[258,263],{"code":39,"score":13,"supported":41,"title":259,"abstract":260,"tags":262},[],[26,43,261],"multispectral",[26],{"code":128,"score":20,"supported":41,"title":264,"abstract":265,"tags":266},[],[116,131],[115,116],[268,269],"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":272,"openalex_id":274,"authors":275,"venue":243,"cited_by_count":87,"oa_url":291,"card":292,"direction":93,"ingested_from":95},"W7213537374",[276,278,280,283,286,288],{"name":277,"orcid":9},"Guanxing Wang",{"name":279,"orcid":9},"Cui Jia",{"name":281,"orcid":282},"Linghan Gao","https:\u002F\u002Forcid.org\u002F0000-0002-8537-8787",{"name":284,"orcid":285},"Shan An","https:\u002F\u002Forcid.org\u002F0000-0001-7796-6952",{"name":287,"orcid":9},"Jia Xi",{"name":289,"orcid":290},"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":293,"method":294,"finding":295,"direction":93,"opportunity":296},"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":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":312,"tags":314,"primary_topic":317,"secondary_topics":322,"classification":323,"search_phrases":357,"slug":360,"view_count":21,"doi":361,"paper":362,"created_at":386},2768,"Spatial–temporal variability of the Bua River’s physicochemical water quality: a DPSIR-based analysis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2722406","Freshwater ecosystems are increasingly threatened by climate variability and catchment degradation, resulting in declining water quality and ecosystem resilience. We assessed spatial and seasonal variability in water quality along the Bua River, Malawi, and applied the Drivers–Pressures–State–Impact–Response (DPSIR) framework to identify degradation pathways and management responses. A stratified sampling design was used across upstream, midstream, and downstream sections during three distinct seasons. Downstream sections consistently exhibited poorer water quality, with turbidity reaching 17.17 ± 0.63 NTU, EC increasing to 183.00 ± 2.80 µS cm−1, ammonium concentrations reaching 120.14 ± 2.00 µg L−1, and chlorophyll-a increasing to 6.70 ± 0.70 µg L−1. Factorial ANOVA revealed significant seasonal effects (F₂,₅₂ = 21.84, p \u003C 0.0001), site (F₂,₅₂ = 6.25, p = 0.0036), and water-quality parameters (F₁₃,₅₂ = 58.80, p \u003C 0.0001). Elevated nutrient concentrations and sediment loads were associated with anthropogenic stressors. Our DPSIR linked these pressures to ecological degradation, declining fisheries productivity and increasing risks to water-dependent livelihoods. Our findings highlight the need for integrated catchment management, riparian restoration, climate-smart agriculture, and strengthened water-quality monitoring to support Malawi’s Nationally Determined Contributions (NDCs), and Sustainable Development Goals (SDGs).","淡水生态系统日益受到气候变率和流域退化的威胁，导致水质下降和生态系统恢复力减弱。我们评估了马拉维布阿河沿线水质的空间和季节变异性，并应用驱动力—压力—状态—影响—响应（DPSIR）框架来识别退化路径和管理响应。采用分层抽样设计，在三个不同季节对上游、中游和下游河段进行采样。下游河段的水质始终较差，浊度达到17.17 ± 0.63 NTU，电导率（EC）升高至183.00 ± 2.80 µS cm⁻¹，铵盐浓度达到120.14 ± 2.00 µg L⁻¹，叶绿素a增加至6.70 ± 0.70 µg L⁻¹。因子方差分析（ANOVA）揭示了显著的季节效应（F₂,₅₂ = 21.84，p \u003C 0.0001）、点位效应（F₂,₅₂ = 6.25，p = 0.0036）和水质参数效应（F₁₃,₅₂ = 58.80，p \u003C 0.0001）。营养物浓度和沉积物负荷升高与人为压力因素相关。我们的DPSIR框架将这些压力与生态退化、渔业生产力下降以及依赖水资源生计的风险增加联系起来。我们的研究结果凸显了综合流域管理、河岸带修复、气候智慧型农业和加强水质监测的必要性，以支持马拉维的国家自主贡献（NDCs）和可持续发展目标（SDGs）。","International Journal of River Basin Management","2026-09-16T00:00:00Z",66,{"impact":20,"substance":308,"depth":309,"authority":110,"freshness":310,"relevant":21,"comment":311},20,16,9,"以DPSIR框架量化马拉维Bua河水质时空变化，方法规范、数据翔实，但属境外流域案例，对国内三农与农业信息化的直接借鉴价值有限，可作国际参考而非每日精选。",[313],{"name":304,"url":301},[315,175,27,316,29],"气候变化","流域治理",{"code":196,"slug":318,"name":319,"path":320,"parent_code":321},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":324,"locked":14,"version":34,"method":35,"evidence":325},"classified",{"candidates":326,"agricultural_evidence":41},[327,331,337,343,348,352],{"code":196,"score":56,"supported":41,"title":328,"abstract":329,"tags":330},[],[199],[175],{"code":332,"score":137,"supported":41,"title":333,"abstract":334,"tags":336},"D",[],[335],"渔业",[],{"code":338,"score":137,"supported":41,"title":339,"abstract":340,"tags":342},"I1",[],[341],"smart agriculture",[],{"code":344,"score":21,"supported":14,"title":345,"abstract":346,"tags":347},"D4",[],[],[27],{"code":47,"score":21,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[29],{"code":353,"score":21,"supported":14,"title":354,"abstract":355,"tags":356},"H7",[],[],[315],[358,359],"水资源管理 气候变化 水质监测 流域治理","水资源管理 气候变化","水资源管理气候变化水质监测流域治理-2768","10.1080\u002F15715124.2026.2722406",{"doi":361,"openalex_id":363,"authors":364,"venue":304,"cited_by_count":87,"oa_url":378,"card":379,"direction":385,"ingested_from":95},"W7213250794",[365,368,370,372,375],{"name":366,"orcid":367},"James N. Nkhoswe","https:\u002F\u002Forcid.org\u002F0000-0002-9044-5913",{"name":369,"orcid":9},"Amon Aine",{"name":371,"orcid":9},"Tamara Tembo",{"name":373,"orcid":374},"Madalitso Chatsika","https:\u002F\u002Forcid.org\u002F0000-0002-3354-4720",{"name":376,"orcid":377},"A. H. N. Mtethiwa","https:\u002F\u002Forcid.org\u002F0000-0003-0793-5186","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.33845035",{"tldr":380,"method":381,"finding":382,"direction":383,"opportunity":384},"评估马拉维布阿河水质时空变化，并用DPSIR框架分析退化路径与管理对策。","三季上中下游分层采样，测定理化指标，因子方差分析与DPSIR框架。","下游水质最差，浊度、电导率、铵盐和叶绿素a显著升高，季节与点位影响显著。","农业绿色发展与碳","可结合流域农业面源污染监测与气候智慧型农业措施，量化其对水质及碳减排的协同效应。","智慧农业 \u002F 农业物联网","2026-09-17T23:30:10.468460Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":9,"content":9,"source_name":392,"source_url":390,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":9,"secondary_topics":401,"classification":402,"search_phrases":422,"slug":425,"view_count":310,"doi":426,"paper":427,"created_at":438},2309,"Modeling Water Quality Parameters in Laga Dadi Reservoir, Ethiopia: An Integrated Remote Sensing and Machine Learning Approach","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10838920\u002Fv1","Modeling Water Quality Parameters in Laga Dadi Reservoir, Ethiopia: An Integrated Remote Sensing and Machine Learning Approach。Research Square","Research Square","2026-09-10T00:00:00Z",55,{"impact":20,"substance":17,"depth":40,"authority":98,"freshness":20,"relevant":21,"comment":396},"埃塞俄比亚水库水质遥感与机器学习建模研究，方法有参考价值但属区域性案例，公共影响有限。",[398],{"name":392,"url":390},[400,116,26,175,27],"智慧农业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":403},{"candidates":404,"agricultural_evidence":14,"reason":51},[405,410,414,418],{"code":128,"score":406,"supported":41,"title":407,"abstract":408,"tags":409},12,[131],[131],[116],{"code":39,"score":406,"supported":41,"title":411,"abstract":412,"tags":413},[43],[43],[26],{"code":196,"score":21,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[175],{"code":338,"score":21,"supported":14,"title":419,"abstract":420,"tags":421},[],[],[400],[423,424],"水资源管理 智慧农业 机器学习 水质监测","水资源管理 智慧农业","水资源管理智慧农业机器学习水质监测-2309","10.21203\u002Frs.3.rs-10838920\u002Fv1",{"doi":426,"openalex_id":428,"authors":429,"venue":392,"cited_by_count":87,"oa_url":437,"card":9,"direction":385,"ingested_from":95},"W7212151964",[430,432,435],{"name":431,"orcid":9},"Sadirak Tasissa",{"name":433,"orcid":434},"Kenatu Angassa","https:\u002F\u002Forcid.org\u002F0000-0002-1449-2451",{"name":436,"orcid":9},"Workineh Tesfaye","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10838920\u002Flatest.pdf","2026-09-13T23:30:12.531084Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":450,"tags":452,"primary_topic":9,"secondary_topics":456,"classification":457,"search_phrases":474,"slug":477,"view_count":21,"doi":478,"paper":479,"created_at":500},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。","OpenAlex","2026-09-09T00:00:00Z",77,{"impact":309,"substance":18,"depth":17,"authority":110,"freshness":20,"relevant":21,"comment":449},"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。",[451],{"name":445,"url":442},[453,26,27,454,455],"智慧渔业","数据同化","淡水养殖",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":458},{"candidates":459,"agricultural_evidence":14,"reason":51},[460,465,469],{"code":39,"score":461,"supported":41,"title":462,"abstract":463,"tags":464},7,[],[26,43],[26],{"code":196,"score":137,"supported":41,"title":466,"abstract":467,"tags":468},[],[199],[],{"code":470,"score":21,"supported":14,"title":471,"abstract":472,"tags":473},"I5",[],[],[454],[475,476],"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":478,"openalex_id":480,"authors":481,"venue":9,"cited_by_count":87,"oa_url":494,"card":495,"direction":93,"ingested_from":95},"W7212081241",[482,485,488,491],{"name":483,"orcid":484},"Molly Stroud","https:\u002F\u002Forcid.org\u002F0000-0001-7389-1586",{"name":486,"orcid":487},"Cayelan C. Carey","https:\u002F\u002Forcid.org\u002F0000-0001-8835-4476",{"name":489,"orcid":490},"George H. Allen","https:\u002F\u002Forcid.org\u002F0000-0001-8301-5301",{"name":492,"orcid":493},"R. Quinn Thomas","https:\u002F\u002Forcid.org\u002F0000-0003-1282-7825","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15008569\u002Fv1",{"tldr":496,"method":497,"finding":498,"direction":93,"opportunity":499},"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。","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。","2026-09-11T23:30:32.851926Z"]