[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2792":3,"related-2792":92},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":66,"slug":69,"view_count":22,"doi":70,"paper":71,"created_at":91},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。",null,"Scientific Reports","2026-09-16T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","土壤墒情","植被覆盖","生态评估","荒漠化监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},15,true,[34],[50,34],"土壤",[28],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"H7",6,[],[57,58],"干旱","drought",[],{"code":61,"score":62,"supported":14,"title":63,"abstract":64,"tags":65},"I3",2,[],[],[29,27],[67,68],"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":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":82,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7213310812",[74,76,78,80],{"name":75,"orcid":9},"Tianwang Lei",{"name":77,"orcid":9},"Chong Zhang",{"name":79,"orcid":9},"Chao Li",{"name":81,"orcid":9},"Xiangying Li",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71674-0_reference.pdf",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"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。","农业遥感与作物表型","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。","openalex","2026-09-17T23:30:36.136113Z",{"total":54,"page":22,"page_size":54,"items":93},[94,166,243,328,393,476],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":108,"tags":110,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":137,"slug":140,"view_count":141,"doi":142,"paper":143,"created_at":165},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":104,"substance":105,"depth":106,"authority":20,"freshness":21,"relevant":22,"comment":107},8,21,17,"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。",[109],{"name":100,"url":97},[27,111,28,112],"农业气象","数据同化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":116},{"candidates":117,"agricultural_evidence":47},[118,122,127,132],{"code":33,"score":46,"supported":47,"title":119,"abstract":120,"tags":121},[34],[50,34],[28],{"code":53,"score":123,"supported":47,"title":124,"abstract":125,"tags":126},7,[],[57,58],[111],{"code":61,"score":123,"supported":47,"title":128,"abstract":129,"tags":131},[],[27,130],"remote sensing",[27],{"code":133,"score":22,"supported":14,"title":134,"abstract":135,"tags":136},"I5",[],[],[112],[138,139],"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",4,"10.1175\u002Fjhm-d-26-0062.1",{"doi":142,"openalex_id":144,"authors":145,"venue":100,"cited_by_count":82,"oa_url":9,"card":160,"direction":88,"ingested_from":90},"W7220742830",[146,149,151,154,157],{"name":147,"orcid":148},"Fabricio Matías Obregón","https:\u002F\u002Forcid.org\u002F0009-0005-9861-1356",{"name":150,"orcid":9},"Lucini Magdalena",{"name":152,"orcid":153},"Omar Vicente Müller","https:\u002F\u002Forcid.org\u002F0000-0001-9943-8368",{"name":155,"orcid":156},"Romina Carla Ruscica","https:\u002F\u002Forcid.org\u002F0000-0003-0127-9579",{"name":158,"orcid":159},"Manuel Pulido","https:\u002F\u002Forcid.org\u002F0000-0003-3992-8044",{"tldr":161,"method":162,"finding":163,"direction":88,"opportunity":164},"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":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":185,"classification":186,"search_phrases":207,"slug":210,"view_count":62,"doi":211,"paper":212,"created_at":242},4065,"A new vegetation–thermal integrated approach (NNL-SM) for high-resolution soil moisture estimation","https:\u002F\u002Fdoi.org\u002F10.1080\u002F10106049.2026.2689867","High-resolution soil moisture (SM) information is essential for hydrological, agricultural and climate studies. This paper presents an NDMI–NDVI–LST-based downscaling technique, termed the NNL-SM model, to downscale SMAP SM from 9 to 1 km resolution. The model was validated in Varanasi, India, using ground SM measurements collected on fifteen dates and compared with the VMSMI and triangle optical-based models. By integrating the normalized NDMI, NDVI and LST while minimizing vegetation effects, the NNL-SM model achieved the best performance, with the lowest RMSE (0.028 m3\u002Fm3), ubRMSE (0.023 m3\u002Fm3), smallest bias (0.001 m3\u002Fm3) and highest correlation (R = 0.700). VMSMI showed moderate accuracy, whereas the Triangle model had weaker correlation. Seasonal analysis indicated better winter performance and reduced pre-monsoon accuracy due to vegetation and temperature variability. Overall, the NNL-SM approach showed promising performance for the selected heterogeneous agricultural region and can be further evaluated using hybrid indices and multi-region validation.","高分辨率土壤水分（SM）信息对水文、农业和气候研究至关重要。本文提出了一种基于NDMI–NDVI–LST的降尺度技术，称为NNL-SM模型，用于将SMAP土壤水分数据从9 km降尺度至1 km分辨率。该模型在印度瓦拉纳西使用15个日期的地面土壤水分实测数据进行验证，并与VMSMI模型和基于三角光学模型进行了比较。通过整合归一化NDMI、NDVI和LST，同时最小化植被影响，NNL-SM模型表现最佳，具有最低的RMSE（0.028 m³\u002Fm³）、ubRMSE（0.023 m³\u002Fm³）、最小偏差（0.001 m³\u002Fm³）和最高相关性（R = 0.700）。VMSMI表现出中等精度，而三角模型相关性较弱。季节性分析表明冬季表现较好，季风前由于植被和温度变异性导致精度降低。总体而言，NNL-SM方法在所选异质农业区域表现出良好性能，可进一步使用混合指数和多区域验证进行评估。","Geocarto International","2026-10-01T00:00:00Z",71,{"impact":176,"substance":105,"depth":106,"authority":177,"freshness":104,"relevant":22,"comment":178},12,13,"提出NDMI-NDVI-LST融合降尺度模型，将SMAP土壤水分从9km提升至1km并验证精度，方法新颖、结论可靠，对农业旱情监测有实用价值。",[180],{"name":172,"url":169},[182,183,27,28],"智慧农业","农业人工智能",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":187},{"candidates":188,"agricultural_evidence":47},[189,193,198,203],{"code":33,"score":46,"supported":47,"title":190,"abstract":191,"tags":192},[34],[50,34],[28],{"code":194,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},"I1",[],[],[182],{"code":199,"score":22,"supported":14,"title":200,"abstract":201,"tags":202},"I2",[],[],[183],{"code":61,"score":22,"supported":14,"title":204,"abstract":205,"tags":206},[],[],[27],[208,209],"SMAP 土壤水分 降尺度","NNL-SM 土壤水分 遥感","SMAP土壤水分降尺度-4065","10.1080\u002F10106049.2026.2689867",{"doi":211,"openalex_id":213,"authors":214,"venue":172,"cited_by_count":82,"oa_url":235,"card":236,"direction":241,"ingested_from":90},"W7215055402",[215,218,221,224,226,229,232],{"name":216,"orcid":217},"B H PRAJAPATI","https:\u002F\u002Forcid.org\u002F0009-0007-1840-9786",{"name":219,"orcid":220},"Rajendra Prasad","https:\u002F\u002Forcid.org\u002F0000-0002-3340-2853",{"name":222,"orcid":223},"Jyoti Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-9400-6635",{"name":225,"orcid":9},"Prashant Kumar Srivastava",{"name":227,"orcid":228},"Sonu Kumar","https:\u002F\u002Forcid.org\u002F0009-0002-2713-3295",{"name":230,"orcid":231},"Dileep Kumar Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-4119-7319",{"name":233,"orcid":234},"Varun Narayan Mishra","https:\u002F\u002Forcid.org\u002F0000-0002-4336-8038","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F10106049.2026.2689867?needAccess=true",{"tldr":237,"method":238,"finding":239,"direction":88,"opportunity":240},"提出NNL-SM模型，融合NDMI、NDVI和LST将SMAP土壤水分从9km降尺度至1km。","基于NDMI-NDVI-LST的降尺度技术，用印度Varanasi地面数据验证。","NNL-SM精度最优，RMSE 0.028 m³\u002Fm³，R=0.700，冬季表现优于季风前。","可探索混合指数与多区域验证，提升异质农业区及季风前土壤水分估算精度。","智慧农业 \u002F 农业物联网","2026-10-03T23:30:11.581927Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"primary_topic":260,"secondary_topics":265,"classification":272,"search_phrases":298,"slug":301,"view_count":302,"doi":303,"paper":304,"created_at":327},3790,"Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110822","Soil moisture (SM) plays a critical role in the land–atmosphere water cycle and agricultural water resource management. However, the applicability of prevalent SM products in typical irrigation districts remains insufficiently assessed. In this study, two in situ SM observation networks—an automatic meteorological station network (AMS) and an agro-meteorological experimental station network (AES)—were used to comprehensively evaluate the accuracy and irrigation-associated response characteristics of seven microwave and reanalysis SM products in the North China Plain. The results indicated that: (1) AES-based evaluations generally showed a higher overall correlation (R = 0.58) and smaller errors with the SM products than AMS-based evaluations (R = 0.54). However, when restricted to common observation dates, the correlation coefficients decreased while the biases were close to zero in the AES-based evaluation. (2) Significant differences were observed among the evaluated products under different monitoring objectives. The ERA5-Land reanalysis product performed best for continuous SM monitoring, while the European Space Agency Climate Change Initiative (ESA CCI) Passive product achieved the highest correlation coefficient with in situ observations (R = 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.043 m 3 m −3 ) on recorded irrigation days and exhibited a comparatively pronounced positive SM response under precipitation-controlled conditions, indicating greater sensitivity to short-term irrigation-associated wetting. (3) Strict precipitation control substantially affected the interpretation of irrigation-associated responses. ERA5-Land and GLDAS-Noah showed little systematic positive change when precipitation was controlled using both station observations and model precipitation forcing, while threshold-based probability of detection (POD) varied substantially with the selected SM response threshold. These findings demonstrate that product suitability depends on the monitoring objective and that precipitation attribution and threshold sensitivity should be explicitly considered when using SM products for irrigation-related applications.","土壤水分（SM）在陆气水循环和农业水资源管理中起着至关重要的作用。然而，主流SM产品在典型灌区的适用性仍缺乏充分评估。本研究利用两套原位SM观测网络——自动气象站网络（AMS）和农业气象实验站网络（AES），综合评估了七种微波和再分析SM产品在华北平原的精度及与灌溉相关的响应特征。结果表明：（1）基于AES的评估总体上显示出比基于AMS的评估更高的整体相关性（R = 0.58）和更小的误差（R = 0.54）。然而，当限定于共同观测日期时，基于AES的评估中相关系数下降，而偏差接近于零。（2）在不同监测目标下，所评估产品之间存在显著差异。ERA5-Land再分析产品在连续SM监测中表现最佳，而欧洲空间局气候变化倡议（ESA CCI）被动产品在记录灌溉日与原位观测的相关系数最高（R = 0.69），无偏均方根误差最低（ubRMSE = 0.043 m³ m⁻³），并在降水控制条件下表现出相对明显的SM正响应，表明其对短期灌溉相关增湿具有更高的敏感性。（3）严格的降水控制显著影响了对灌溉相关响应的解释。当利用站点观测和模型降水强迫控制降水时，ERA5-Land和GLDAS-Noah几乎未表现出系统性正变化，而基于阈值的检测概率（POD）随所选SM响应阈值的变化而显著变化。这些发现表明，产品适用性取决于监测目标，且在将SM产品用于灌溉相关应用时，应明确考虑降水归因和阈值敏感性。","Agricultural Water Management","2026-09-28T00:00:00Z",74,{"impact":176,"substance":18,"depth":19,"authority":20,"freshness":104,"relevant":22,"comment":253},"基于华北平原实测网络的七种土壤水分产品评估，为灌溉监测产品选型提供实证依据，专业价值较高但属细分领域研究。",[255],{"name":249,"url":246},[257,27,28,258,259],"农业水资源","灌溉管理","华北平原",{"code":261,"slug":262,"name":263,"path":264,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[266,267],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":61,"slug":268,"name":269,"path":270,"parent_code":271},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":273},{"candidates":274,"agricultural_evidence":47},[275,283,287,292],{"code":261,"score":276,"supported":47,"title":277,"abstract":278,"tags":282},19,[262],[279,280,262,281],"灌溉","水资源","water resource",[257,258],{"code":33,"score":46,"supported":47,"title":284,"abstract":285,"tags":286},[34],[50,34],[28],{"code":61,"score":176,"supported":47,"title":288,"abstract":289,"tags":291},[130],[290],"估产",[27],{"code":53,"score":21,"supported":47,"title":293,"abstract":294,"tags":297},[],[295,111,296],"气候变化","climate change",[],[299,300],"ESA CCI 土壤水分 灌溉","华北平原 土壤水分 遥感","ESACCI土壤水分灌溉-3790",3,"10.1016\u002Fj.agwat.2026.110822",{"doi":303,"openalex_id":305,"authors":306,"venue":249,"cited_by_count":82,"oa_url":321,"card":322,"direction":88,"ingested_from":90},"W7214549221",[307,310,312,314,316,318],{"name":308,"orcid":309},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":311,"orcid":9},"Yu Li",{"name":313,"orcid":9},"Jiawei Lv",{"name":315,"orcid":9},"Lei Wang",{"name":317,"orcid":9},"Zhiqing Cheng",{"name":319,"orcid":320},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":323,"method":324,"finding":325,"direction":88,"opportunity":326},"评估七种遥感与再分析土壤湿度产品在典型灌区的精度及对灌溉事件的响应。","用自动气象站与农业气象站两套实测网络，对比七种微波\u002F再分析产品。","ESA CCI Passive对灌溉日响应最灵敏，ERA5-Land适合连续监测，降水控制影响响应判","可针对灌溉事件构建降水控制下的阈值自适应土壤湿度响应检测方法，提升灌区遥感监测可靠性。","2026-09-29T23:30:28.424080Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":339,"tags":341,"primary_topic":343,"secondary_topics":344,"classification":345,"search_phrases":368,"slug":370,"view_count":54,"doi":371,"paper":372,"created_at":392},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing","2026-09-22T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":338},"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[340],{"name":334,"url":331},[182,183,342,27,28],"深度学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":346},{"candidates":347,"agricultural_evidence":47},[348,352,356,360,364],{"code":33,"score":46,"supported":47,"title":349,"abstract":350,"tags":351},[34],[50,34],[28],{"code":61,"score":123,"supported":47,"title":353,"abstract":354,"tags":355},[],[27,130],[27],{"code":261,"score":302,"supported":47,"title":357,"abstract":358,"tags":359},[],[280],[],{"code":199,"score":62,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[183,342],{"code":194,"score":22,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[182],[208,369],"Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":371,"openalex_id":373,"authors":374,"venue":334,"cited_by_count":82,"oa_url":331,"card":387,"direction":88,"ingested_from":90},"W7208807695",[375,377,379,381,383,385],{"name":376,"orcid":9},"Yuyang Fan",{"name":378,"orcid":9},"Jianwei Ma",{"name":380,"orcid":9},"Mengmeng Li",{"name":382,"orcid":9},"Changqing Ke",{"name":384,"orcid":9},"Bin Cheng",{"name":386,"orcid":9},"Zheng Duan",{"tldr":388,"method":389,"finding":390,"direction":88,"opportunity":391},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":249,"source_url":396,"published_at":399,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":405,"primary_topic":407,"secondary_topics":413,"classification":416,"search_phrases":441,"slug":444,"view_count":22,"doi":445,"paper":446,"created_at":475},1312,"Accurate diagnosis of soil moisture and canopy water content in citrus orchards based on synergistic multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110725","Accurate prediction of soil moisture is essential for precise agricultural irrigation decision-making and regional water resource optimal scheduling. However, regional soil moisture inversion commonly suffers from three major limitations, including overreliance on single remote sensing data sources, fixed canopy parameters adopted in conventional physical models, and severe cloud contamination in the cloudy mountainous areas of Southwest China. In this study, a synergistic multi-source remote sensing framework integrating Landsat-8\u002F9, Sentinel-2, and Sentinel-1A datasets was established to improve the traditional water cloud model by introducing phenology-driven dynamic canopy parameters. Specifically, canopy coverage and canopy water content of late-maturing citrus across different growth stages were accurately retrieved from Landsat-8\u002F9 and Sentinel-2 imagery to optimize the simulation of canopy backscattering signals. The all-weather C-band radar data effectively mitigate the persistent cloud-cover limitations prevalent in the hilly regions of Southwest China. The results indicated that the vegetation water content in large-scale citrus orchards retrieved by optical remote sensing exhibits significant spatial variability, mainly distributed in the range of 1.65–5.71 kg·m −2 and 1.55–5.78 kg·m −2 , respectively. The soil moisture inversion results using multi-temporal Sentinel-1A data in VV polarization outperformed those in VH polarization, with R² ranges of 0.785–0.854 and 0.515–0.759, RMSE ranges of 6.125–8.351% and 6.942–8.412%, respectively. The two polarizations data have the highest inversion accuracy in citrus fruit maturation stage, with R² ranges of 0.811–0.842 and 0.682–0.711, RMSE ranges of 6.125–6.132% and 6.942–7.025%, respectively. This study proposes a novel methodology for high-resolution and precision inversion of surface soil moisture at the regional farmland scale.","土壤水分的精准预测对于精准农业灌溉决策和区域水资源优化调度至关重要。然而，区域土壤水分反演普遍面临三大局限，包括对单一遥感数据源的过度依赖、传统物理模型中采用固定冠层参数，以及中国西南多云山区严重的云污染问题。本研究构建了一种融合Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据集的协同多源遥感框架，通过引入物候驱动的动态冠层参数对传统水云模型进行改进。具体而言，利用Landsat-8\u002F9和Sentinel-2影像精确反演了不同生长阶段晚熟柑橘的冠层覆盖度和冠层含水量，以优化冠层后向散射信号的模拟。全天候C波段雷达数据有效缓解了西南丘陵地区普遍存在的持续云覆盖限制。结果表明，光学遥感反演的大规模柑橘园植被含水量表现出显著的空间变异性，主要分布在1.65–5.71 kg·m⁻²和1.55–5.78 kg·m⁻²范围内。使用多时相Sentinel-1A VV极化的土壤水分反演结果优于VH极化，R²范围分别为0.785–0.854和0.515–0.759，RMSE范围分别为6.125–8.351%和6.942–8.412%。两种极化数据在柑橘果实成熟期反演精度最高，R²范围分别为0.811–0.842和0.682–0.711，RMSE范围分别为6.125–6.132%和6.942–7.025%。本研究提出了一种在区域农田尺度上实现地表土壤水分高分辨率精准反演的新方法。","2026-08-31T00:00:00Z",75,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":302,"relevant":22,"comment":402},"提出多源遥感协同反演土壤水分的新方法，对丘陵山地精准灌溉有参考价值，但时效性一般。",[404],{"name":249,"url":396},[182,27,28,406],"柑橘",{"code":408,"slug":409,"name":410,"path":411,"parent_code":412},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[414,415],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":61,"slug":268,"name":269,"path":270,"parent_code":271},{"status":40,"locked":14,"version":41,"method":42,"evidence":417},{"candidates":418,"agricultural_evidence":47},[419,424,428,432,436],{"code":408,"score":46,"supported":47,"title":420,"abstract":422,"tags":423},[421],"citrus",[406,421],[406],{"code":33,"score":46,"supported":47,"title":425,"abstract":426,"tags":427},[34],[50,34],[28],{"code":61,"score":46,"supported":47,"title":429,"abstract":430,"tags":431},[130],[27,130],[27],{"code":261,"score":21,"supported":47,"title":433,"abstract":434,"tags":435},[],[279,280,262,281],[],{"code":194,"score":141,"supported":47,"title":437,"abstract":438,"tags":440},[],[439],"精准农业",[182],[442,443],"土壤墒情 智慧农业 柑橘 遥感","土壤墒情 智慧农业","土壤墒情智慧农业柑橘遥感-1312","10.1016\u002Fj.agwat.2026.110725",{"doi":445,"openalex_id":447,"authors":448,"venue":249,"cited_by_count":82,"oa_url":396,"card":470,"direction":88,"ingested_from":90},"W7204808715",[449,451,453,455,458,461,464,466,468],{"name":450,"orcid":9},"Zongjun Wu",{"name":452,"orcid":9},"Bin Zhu",{"name":454,"orcid":9},"Ningbo Cui",{"name":456,"orcid":457},"Wei Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-0215-8584",{"name":459,"orcid":460},"Yanjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5286-6269",{"name":462,"orcid":463},"Liwen Xing","https:\u002F\u002Forcid.org\u002F0000-0003-0007-3560",{"name":465,"orcid":9},"Quanshan Liu",{"name":467,"orcid":9},"Shenglin Wen",{"name":469,"orcid":9},"Bihang Fan",{"tldr":471,"method":472,"finding":473,"direction":88,"opportunity":474},"融合多源遥感数据改进水云模型，实现柑橘园土壤水分和冠层含水量高精度反演。","集成Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据，引","VV极化反演精度优于VH，果实成熟期精度最高，R²达0.842。","可探索将动态冠层参数方法应用于其他作物或区域，结合更高分辨率数据提升反演精度。","2026-09-01T23:30:20.097045Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":9,"source_name":482,"source_url":479,"published_at":483,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":484,"score_detail":485,"sources":487,"tags":489,"primary_topic":493,"secondary_topics":499,"classification":505,"search_phrases":536,"slug":539,"view_count":82,"doi":540,"paper":541,"created_at":558},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":17,"substance":105,"depth":106,"authority":177,"freshness":13,"relevant":22,"comment":486},"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[488],{"name":482,"url":479},[182,490,491,28,492],"机器学习","遥感监测","冬小麦",{"code":494,"slug":495,"name":496,"path":497,"parent_code":498},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[500,501],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":199,"slug":502,"name":503,"path":504,"parent_code":271},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":506},{"candidates":507,"agricultural_evidence":47},[508,514,518,523,528,532],{"code":494,"score":509,"supported":47,"title":510,"abstract":512,"tags":513},26,[511,495],"winter wheat",[496,511,495],[492],{"code":33,"score":46,"supported":47,"title":515,"abstract":516,"tags":517},[34],[50,34],[28],{"code":199,"score":46,"supported":47,"title":519,"abstract":521,"tags":522},[520],"machine learning",[490,520],[490],{"code":61,"score":13,"supported":47,"title":524,"abstract":525,"tags":527},[],[27,130,526],"multispectral",[491],{"code":261,"score":54,"supported":47,"title":529,"abstract":530,"tags":531},[],[279,262],[],{"code":194,"score":141,"supported":47,"title":533,"abstract":534,"tags":535},[],[182],[182],[537,538],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":540,"openalex_id":542,"authors":543,"venue":482,"cited_by_count":82,"oa_url":479,"card":553,"direction":241,"ingested_from":90},"W7223156204",[544,547,549,551],{"name":545,"orcid":546},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":548,"orcid":9},"Yi Zhang",{"name":550,"orcid":9},"WEI Lei",{"name":552,"orcid":9},"Chengcai Zhang",{"tldr":554,"method":555,"finding":556,"direction":88,"opportunity":557},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z"]