[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3790":3,"related-3790":119},{"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":50,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":118},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。",null,"Agricultural Water Management","2026-09-28T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业水资源","遥感","土壤墒情","灌溉管理","华北平原",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,71,77],{"code":33,"score":57,"supported":58,"title":59,"abstract":60,"tags":64},19,true,[34],[61,62,34,63],"灌溉","水资源","water resource",[27,30],{"code":40,"score":66,"supported":58,"title":67,"abstract":68,"tags":70},15,[41],[69,41],"土壤",[29],{"code":45,"score":17,"supported":58,"title":72,"abstract":74,"tags":76},[73],"remote sensing",[75],"估产",[28],{"code":78,"score":79,"supported":58,"title":80,"abstract":81,"tags":85},"H7",9,[],[82,83,84],"气候变化","农业气象","climate change",[],[87,88],"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":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":109,"oa_url":110,"card":111,"direction":115,"ingested_from":117},"W7214549221",[95,98,100,102,104,106],{"name":96,"orcid":97},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":99,"orcid":9},"Yu Li",{"name":101,"orcid":9},"Jiawei Lv",{"name":103,"orcid":9},"Lei Wang",{"name":105,"orcid":9},"Zhiqing Cheng",{"name":107,"orcid":108},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"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","农业遥感与作物表型","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。","openalex","2026-09-29T23:30:28.424080Z",{"total":120,"page":22,"page_size":120,"items":121},6,[122,189,267,352,435,509],{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":134,"tags":136,"primary_topic":142,"secondary_topics":143,"classification":146,"search_phrases":166,"slug":169,"view_count":90,"doi":170,"paper":171,"created_at":188},3845,"Problems of soil degradation assessment using spectral indices from space images","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71433-1","This article presents an integrated method of spectral indices for assessing soil degradation using Sentinel-2 Earth Remote Sensing (ERS) data. Unlike approaches based on individual indices, the method combines indicators of vegetation, humidity, thermal state, and salinity, increasing the accuracy of mapping saline and degraded lands while minimizing errors caused by variations in natural conditions. The application of the method in the Tashkent region of Uzbekistan (41.1465°N, 69.3009°E) showed that the category of “extremely high” and “high” degradation accounts for 3.29% and 8.28% of pixels, “medium” and “low” for 9.4% and 9.52%, while the remaining 69.51% belong to other category (class) of land cover, confirming the concentration of degradation foci in lowland areas. Comparison with 2020 showed a notable increase in the share of critical degradation (growth from 10.46% to 11.57%), while the share of the “extremely high” category increased from 2.80% to 3.29%, indicating a shift toward more severe conditions that aligns with official regional salinization statistics. Analysis of meteorological data (2013–2025) revealed an inverse relationship between temperature and precipitation, confirming the climatic nature of salinization; four stages of salinization were identified—latent, progressive, severe, and complete degradation and the main components method (PCA) was applied to reduce the size of the data. These results correspond to the expansion of irrigated lands in Uzbekistan from 3.5 to 8.5 million hectares and the associated increase in groundwater levels. Consequently, this method is not the final means of delimiting degraded lands, but represents a quick, inexpensive preliminary check before field surveys: it marks potentially degraded areas for priority ground surveys, supplementing official surveys and government statistics rather than replacing them. The method is applicable in agriculture, land cadastre, and environmental monitoring as an auxiliary tool for climate adaptation and water resource management.","本文提出了一种利用Sentinel-2地球遥感（ERS）数据评估土壤退化的光谱指数集成方法。与基于单一指数的方法不同，该方法综合了植被、湿度、热状态和盐分指标，提高了盐碱化和退化土地制图的精度，同时最大限度地减少了自然条件变化引起的误差。该方法在乌兹别克斯坦塔什干地区（北纬41.1465°，东经69.3009°）的应用表明，“极高”和“高”退化类别分别占像元的3.29%和8.28%，“中等”和“低”分别占9.4%和9.52%，其余69.51%属于其他土地覆盖类别（等级），证实了退化热点集中在低地地区。与2020年相比，临界退化比例显著增加（从10.46%增至11.57%），而“极高”类别比例从2.80%增至3.29%，表明退化状况趋于严重，这与官方区域盐渍化统计数据一致。气象数据（2013—2025年）分析揭示了气温与降水之间的反比关系，证实了盐渍化的气候成因；识别出盐渍化的四个阶段——潜伏期、进展期、严重期和完全退化期，并应用主成分分析方法（PCA）对数据进行了降维处理。这些结果与乌兹别克斯坦灌溉面积从350万公顷扩展至850万公顷以及相关的地下水位上升相吻合。因此，该方法并非划定退化土地的最终手段，而是实地调查前一种快速、低成本的初步筛查：它标记出潜在退化区域以供优先开展地面调查，是对官方调查和政府统计的补充而非替代。该方法适用于农业、土地地籍和环境监测领域，可作为气候适应和水资源管理的辅助工具。","Scientific Reports","2026-09-29T00:00:00Z",79,{"impact":132,"substance":18,"depth":19,"authority":20,"freshness":79,"relevant":22,"comment":133},16,"基于Sentinel-2多光谱指数的土壤退化综合评估方法，方法新颖、数据翔实，对盐渍化遥感监测与耕地保护有参考价值。",[135],{"name":128,"url":125},[137,138,139,140,141],"Sentinel-2","遥感监测","土壤退化","盐渍化","土地退化评估",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[144,145],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":147},{"candidates":148,"agricultural_evidence":58},[149,153,157,161],{"code":40,"score":66,"supported":58,"title":150,"abstract":151,"tags":152},[41],[69,41],[140],{"code":33,"score":79,"supported":58,"title":154,"abstract":155,"tags":156},[],[61,62,63],[],{"code":45,"score":21,"supported":58,"title":158,"abstract":159,"tags":160},[],[28,73],[137,138],{"code":78,"score":90,"supported":58,"title":162,"abstract":163,"tags":165},[],[164],"气候适应",[],[167,168],"乌兹别克斯坦 塔什干 土壤盐渍化","Sentinel-2 光谱指数 土壤退化","乌兹别克斯坦塔什干土壤盐渍化-3845","10.1038\u002Fs41598-026-71433-1",{"doi":170,"openalex_id":172,"authors":173,"venue":128,"cited_by_count":109,"oa_url":182,"card":183,"direction":115,"ingested_from":117},"W7214801255",[174,177,180],{"name":175,"orcid":176},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":178,"orcid":179},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":181,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":184,"method":185,"finding":186,"direction":115,"opportunity":187},"提出融合多光谱指数的Sentinel-2土壤退化评估方法，在乌兹别克斯坦验证并识别盐渍化阶段。","Sentinel-2影像多指数融合（植被、湿度、热、盐分）结合PCA与气象数据分","退化集中于低地，2020年以来极重度退化占比由2.80%升至3.29%，与官方盐渍化统计一致。","可将该快速预筛方法与地面调查、机器学习结合，构建多区域可迁移的退化早期预警与制图框架。","2026-09-30T23:30:29.386707Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":197,"sources":202,"tags":204,"primary_topic":207,"secondary_topics":208,"classification":210,"search_phrases":249,"slug":252,"view_count":253,"doi":254,"paper":255,"created_at":266},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",{"impact":132,"substance":198,"depth":199,"authority":200,"freshness":21,"relevant":22,"comment":201},20,17,13,"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[203],{"name":195,"url":192},[205,27,28,30,206],"智慧农业","农业干旱",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[209],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":211},{"candidates":212,"agricultural_evidence":58},[213,217,224,230,237,244],{"code":45,"score":66,"supported":58,"title":214,"abstract":215,"tags":216},[73],[28,73],[28],{"code":33,"score":218,"supported":58,"title":219,"abstract":220,"tags":223},11,[],[61,62,221,34,222],"蒸散发","evapotranspiration",[27,30],{"code":78,"score":120,"supported":58,"title":225,"abstract":226,"tags":229},[],[227,228],"干旱","drought",[],{"code":231,"score":120,"supported":58,"title":232,"abstract":233,"tags":236},"H8",[],[234,235],"可持续农业","sustainable agriculture",[],{"code":238,"score":120,"supported":58,"title":239,"abstract":240,"tags":243},"I7",[],[241,242],"文献计量","bibliometric",[],{"code":245,"score":22,"supported":14,"title":246,"abstract":247,"tags":248},"I1",[],[],[205],[250,251],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667",2,"10.1007\u002Fs43621-026-04728-y",{"doi":254,"openalex_id":256,"authors":257,"venue":195,"cited_by_count":109,"oa_url":192,"card":261,"direction":115,"ingested_from":117},"W7213266434",[258],{"name":259,"orcid":260},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":262,"method":263,"finding":264,"direction":115,"opportunity":265},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":279,"tags":281,"primary_topic":285,"secondary_topics":286,"classification":293,"search_phrases":316,"slug":319,"view_count":320,"doi":321,"paper":322,"created_at":351},4085,"Seasonal Snow Water Resources and Spring Runoff Harvesting Potential in the North Kazakhstan Region: An Integrated Remote Sensing and GIS-Based Multi-Criteria Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fearth7050163","Seasonal snow is a critical water resource in Northern Kazakhstan, where spring snowmelt dominates surface runoff and agricultural water supply. However, traditional rainwater harvesting frameworks often neglect snow water equivalent (SWE), limiting their applicability in cold, snow-dominated steppe environments. This study develops an integrated remote sensing and GIS-based approach to identify potential sites for spring runoff harvesting. Using Sentinel-2 imagery, snow cover fraction (SCF) was derived and combined with a digital elevation model (DEM) to estimate snow depth through a quadratic regression model, validated against in situ data from 23 meteorological stations. Snow water equivalent was then calculated by incorporating field-measured snow density. Alongside SWE, six additional parameters—slope, drainage density, land use\u002Fland cover (LULC), and soil texture (sand, silt, clay)—were integrated within an Analytical Hierarchy Process (AHP) multi-criteria decision analysis. Results show that SWE, drainage density, and LULC collectively contributed 75% of the weight in site suitability, underscoring their dominant role in snowmelt-driven hydrology. The final suitability map indicates that 28.03% of the region falls into the “Very High” class, concentrated in central and northwestern districts. Developing just 30% of these potential zones could yield approximately 4.2 × 108 m3 of usable meltwater, supporting up to 105,000 ha under full irrigation or 280,000 ha under deficit irrigation. The study demonstrates that integrating high-resolution SWE with MCDA provides a scalable framework for enhancing agricultural resilience, mitigating floods, and strengthening water security in cold, semi-arid landscapes.","季节性积雪是哈萨克斯坦北部的重要水资源，春季融雪主导着地表径流和农业供水。然而，传统的雨水收集框架往往忽视雪水当量（SWE），限制了其在寒冷、以积雪为主的草原环境中的适用性。本研究开发了一种集成遥感和GIS的方法，用于识别春季径流收集的潜在场址。利用Sentinel-2影像提取积雪覆盖率（SCF），并结合数字高程模型（DEM）通过二次回归模型估算雪深，利用23个气象站的实地数据进行验证。随后，结合实地测量的雪密度计算雪水当量。除SWE外，还将坡度、河网密度、土地利用\u002F土地覆盖（LULC）以及土壤质地（砂、粉砂、黏土）等六个附加参数纳入层次分析法（AHP）多准则决策分析。结果表明，SWE、河网密度和LULC在场地适宜性评价中合计贡献了75%的权重，凸显了它们在融雪驱动水文中的主导作用。最终适宜性图显示，该区域28.03%的面积属于“极高”等级，集中在中部和西北部地区。仅开发这些潜在区域的30%，即可产生约4.2 × 10⁸ m³的可用融水，可支持多达105,000公顷的充分灌溉或280,000公顷的亏缺灌溉。本研究表明，将高分辨率SWE与多准则决策分析相结合，为增强农业韧性、减轻洪水和加强寒冷半干旱地区的水安全提供了一个可扩展的框架。","Earth","2026-10-02T00:00:00Z",77,{"impact":132,"substance":277,"depth":19,"authority":200,"freshness":79,"relevant":22,"comment":278},21,"该研究将遥感与GIS多准则评估结合，量化了北哈萨克斯坦融雪水资源与灌溉潜力，方法新颖、数据扎实，对寒区农业水资源管理有参考价值，但地域性较强，属细分领域进展。",[280],{"name":273,"url":270},[27,282,28,283,284],"哈萨克斯坦","GIS","融雪灌溉",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[287,292],{"code":288,"slug":289,"name":290,"path":291,"parent_code":49},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":294},{"candidates":295,"agricultural_evidence":58},[296,300,304,308,312],{"code":45,"score":66,"supported":58,"title":297,"abstract":298,"tags":299},[73],[28,73],[28],{"code":288,"score":17,"supported":58,"title":301,"abstract":302,"tags":303},[283],[283],[283],{"code":33,"score":13,"supported":58,"title":305,"abstract":306,"tags":307},[],[61,62,34,63],[27],{"code":40,"score":120,"supported":58,"title":309,"abstract":310,"tags":311},[],[69,41],[],{"code":78,"score":90,"supported":58,"title":313,"abstract":314,"tags":315},[],[227],[],[317,318],"北哈萨克斯坦 融雪 灌溉潜力","Sentinel-2 雪水当量 遥感","北哈萨克斯坦融雪灌溉潜力-4085",5,"10.3390\u002Fearth7050163",{"doi":321,"openalex_id":323,"authors":324,"venue":273,"cited_by_count":109,"oa_url":270,"card":346,"direction":115,"ingested_from":117},"W7216430083",[325,328,331,333,335,337,340,343],{"name":326,"orcid":327},"Zhanassyl Teleubay","https:\u002F\u002Forcid.org\u002F0000-0003-4783-5489",{"name":329,"orcid":330},"Erkin Ussalinov","https:\u002F\u002Forcid.org\u002F0000-0003-1907-9532",{"name":332,"orcid":9},"Laura Ryskulbekova",{"name":334,"orcid":9},"Asset Arystanov",{"name":336,"orcid":9},"Ibragim Seitassanov",{"name":338,"orcid":339},"Улжан Онласын","https:\u002F\u002Forcid.org\u002F0000-0001-5789-5464",{"name":341,"orcid":342},"Nuray Kutymova","https:\u002F\u002Forcid.org\u002F0009-0000-7214-1708",{"name":344,"orcid":345},"Farabi Yermekov","https:\u002F\u002Forcid.org\u002F0000-0002-0290-3866",{"tldr":347,"method":348,"finding":349,"direction":115,"opportunity":350},"结合遥感与GIS评估哈萨克斯坦北部季节性雪水资源及春季径流收集潜力。","Sentinel-2与DEM估算雪深和SWE，结合AHP多准则决策分析选址。","SWE、河网密度和土地利用贡献75%权重，28%区域为极高适宜区。","可将SWE驱动的选址框架推广至其他寒旱区，并耦合气候情景与灌溉效率优化。","2026-10-03T23:30:32.427699Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":374,"classification":377,"search_phrases":411,"slug":414,"view_count":109,"doi":415,"paper":416,"created_at":434},3168,"Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104524","Rice is the predominant crop cultivated in the Kommamuru Canal Command Area of Guntur district, Andhra Pradesh. Rice productivity is strongly influenced by irrigation water availability and climatic conditions. The present study aimed to analyse the spatial and temporal variability of rice productivity using the DSSAT-CERES Rice model integrated with Sentinel-2 remote sensing data during the kharif seasons of 2022, 2023 and 2024. Rice-cultivated areas were mapped using multi-temporal Sentinel-2 imagery, while weather, soil, crop management and cultivar data were used as inputs for DSSAT simulations. The model was calibrated and validated using field observations and yield data collected from representative locations within the command area. Simulated rice yields showed considerable variation among mandals and across years, ranging from 1,560.58 to 4,111.06 kg ha⁻¹. The highest productivity was observed in 2024 due to favourable rainfall distribution, adequate canal water supply and improved crop growth conditions, whereas relatively lower productivity was recorded in 2023 owing to moisture stress and irregular irrigation availability. Spatial yield maps generated through the integration of DSSAT outputs and GIS-based rice maps identified high- and low-productivity zones across the command area. The study demonstrated the usefulness of combining crop simulation modelling and remote sensing techniques for rice yield assessment, irrigation planning, yield forecasting and sustainable water resource management in canal command areas. The study confirmed that the DSSAT-CERES Rice model effectively simulated rice productivity in the Kommamuru Canal Command Area during 2022-2024. Overall, the study provides a reliable framework for sustainable rice production and efficient water management in canal command areas and can be applied for future agricultural planning and decision-making. The validation results showed low percentage deviation values ranging from 0.94% to 4.39%, indicating good agreement between simulated and actual farmer yields. Low percentage deviation values between simulated and actual yields indicated good model accuracy and reliability for yield prediction. The integration of DSSAT, GIS and remote sensing techniques was useful for sustainable rice production planning and efficient irrigation management.","水稻是安得拉邦贡土尔县科马穆鲁灌区种植的主要作物。水稻生产力受灌溉可用水量和气候条件的强烈影响。本研究旨在利用DSSAT-CERES水稻模型结合Sentinel-2遥感数据，分析2022年、2023年和2024年kharif季水稻生产力的时空变异性。利用多时相Sentinel-2影像绘制水稻种植区，同时将气象、土壤、作物管理和品种数据作为DSSAT模拟的输入。利用从灌区内代表性地点收集的田间观测数据和产量数据对模型进行校准和验证。模拟水稻产量在不同mandal和年份间表现出相当大的变异，范围为1,560.58至4,111.06 kg ha⁻¹。2024年由于有利的降雨分布、充足的渠水供应和改善的作物生长条件，生产力最高；而2023年由于水分胁迫和不规律的灌溉可用性，生产力相对较低。通过整合DSSAT输出和基于GIS的水稻分布图生成的空间产量图，识别出灌区内的高产区和低产区。研究表明，将作物模拟模型与遥感技术相结合，对于灌区水稻产量评估、灌溉规划、产量预测和可持续水资源管理具有实用价值。研究证实，DSSAT-CERES水稻模型有效模拟了2022-2024年科马穆鲁灌区的水稻生产力。总体而言，本研究为灌区可持续水稻生产和高效水资源管理提供了可靠的框架，可应用于未来的农业规划和决策。验证结果显示，百分比偏差值较低，范围为0.94%至4.39%，表明模拟产量与实际农民产量之间具有良好的一致性。模拟产量与实际产量之间较低的百分比偏差值表明模型在产量预测方面具有良好的准确性和可靠性。DSSAT、GIS和遥感技术的整合有助于可持续水稻生产规划和高效灌溉管理。","Journal of Experimental Agriculture International","2026-09-22T00:00:00Z",72,{"impact":17,"substance":198,"depth":199,"authority":200,"freshness":13,"relevant":22,"comment":362},"印度区域尺度的DSSAT与Sentinel-2融合估产研究，方法成熟、验证可靠，对遥感估产与灌溉管理有参考价值，但属区域性案例，公共影响有限。",[364],{"name":358,"url":355},[366,367,28,368,30],"水稻","产量预测","作物模型",{"code":370,"slug":371,"name":366,"path":372,"parent_code":373},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[375,376],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":378},{"candidates":379,"agricultural_evidence":58},[380,384,388,392,396,400,406],{"code":370,"score":66,"supported":58,"title":381,"abstract":382,"tags":383},[371],[366,371],[366],{"code":33,"score":13,"supported":58,"title":385,"abstract":386,"tags":387},[],[61,62,34,63],[30],{"code":45,"score":21,"supported":58,"title":389,"abstract":390,"tags":391},[],[28,73],[367,28],{"code":40,"score":120,"supported":58,"title":393,"abstract":394,"tags":395},[],[69,41],[],{"code":288,"score":90,"supported":58,"title":397,"abstract":398,"tags":399},[],[283],[],{"code":401,"score":90,"supported":58,"title":402,"abstract":403,"tags":405},"J1",[],[404],"农业规划",[],{"code":407,"score":22,"supported":14,"title":408,"abstract":409,"tags":410},"I2",[],[],[368],[412,413],"DSSAT CERES Rice 水稻","Sentinel-2 水稻 遥感估产","DSSATCERESRice水稻-3168","10.9734\u002Fjeai\u002F2026\u002Fv48i104524",{"doi":415,"openalex_id":417,"authors":418,"venue":358,"cited_by_count":109,"oa_url":355,"card":429,"direction":115,"ingested_from":117},"W7213966772",[419,421,423,425,427],{"name":420,"orcid":9},"Rana Prathap",{"name":422,"orcid":9},"G. Ravi Babu",{"name":424,"orcid":9},"V. Muthayya Chowdary",{"name":426,"orcid":9},"K.Krupavathi",{"name":428,"orcid":9},"K. Chandrasekhar",{"tldr":430,"method":431,"finding":432,"direction":115,"opportunity":433},"用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20","DSSAT-CERES水稻模型、Sentinel-2多时相影像、GIS空间制图与","模拟产量1560-4111 kg\u002Fha，2024年最高、2023年因水分胁迫最低，验证偏差仅0.94","可引入机器学习同化遥感与作物模型，提升灌区尺度产量预报精度并支撑灌溉决策。","2026-09-22T23:30:22.711640Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":455,"search_phrases":486,"slug":489,"view_count":253,"doi":490,"paper":491,"created_at":508},1768,"Remote Sensing and GIS-Based Spatial Planning of Soil and Water Conservation Measures in a Semi-Arid Watershed Maliwadgaon of Maharashtra, India","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95662","Site-specific planning of water harvesting and soil and water conservation (SWC) measures is important for improving water availability and watershed resilience in semi-arid regions. This study aimed to identify and prioritise suitable locations for SWC structures in the 43 km² Maliwadgaon watershed of Chhatrapati Sambhajinagar district, Maharashtra, India, using an integrated remote sensing (RS) and Geographic Information System (GIS) approach. A 30 m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model and Landsat-8 imagery were used to derive terrain, drainage, land use\u002Fland cover (LULC), and vegetation characteristics. Elevation, slope, drainage density, stream order, dissection index, LULC, and normalized difference vegetation index (NDVI) were integrated through multi-criteria evaluation (MCE) for site-suitability assessment. The watershed has an elevation range of approximately 509–591 m and a dendritic, fourth-order drainage network with a drainage density of 0.991 km\u002Fkm². The MCE identified 40 potential conservation structures: 13 farm ponds, 13 cement nala bunds, 5 percolation tanks, 4 underground barriers, and 5 gully plugs. The proposed locations provide a spatial basis for runoff interception, erosion control, water storage, groundwater recharge, and agricultural water management. The findings demonstrate the practical utility of RS–GIS-based MCE as a decision-support approach for prioritising site-specific SWC interventions in a semi-arid watershed.","在半干旱地区，水资源收集及水土保持（SWC）措施的定点规划对于改善水资源可利用性和增强流域韧性至关重要。本研究旨在利用遥感（RS）与地理信息系统（GIS）集成方法，识别并优先确定印度马哈拉施特拉邦恰特拉帕蒂·桑巴吉纳加尔区Maliwadgaon流域（面积43平方公里）内适宜建设水土保持设施的位置。研究采用30米分辨率的航天飞机雷达地形任务（SRTM）数字高程模型和Landsat-8影像，提取地形、水系、土地利用\u002F土地覆盖（LULC）及植被特征。通过多准则评价（MCE）整合高程、坡度、排水密度、河流等级、切割指数、土地利用\u002F土地覆盖及归一化差异植被指数（NDVI），进行选址适宜性评估。该流域海拔范围约为509–591米，水系呈树枝状，为四级排水网络，排水密度为0.991公里\u002F平方公里。多准则评价识别出40处潜在的水土保持设施：13座田间池塘、13座水泥沟壑拦水坝、5座渗滤池、4座地下屏障及5处冲沟堵截设施。所提出的选址为径流拦截、侵蚀控制、蓄水、地下水补给及农业水资源管理提供了空间依据。研究结果表明，基于遥感与地理信息系统的多准则评价作为一种决策支持方法，在半干旱流域优先确定定点水土保持干预措施方面具有实际应用价值。","International Journal of Environment and Climate Change","2026-09-05T00:00:00Z",60,{"impact":17,"substance":19,"depth":132,"authority":17,"freshness":253,"relevant":22,"comment":445},"研究利用遥感和GIS技术在半干旱流域进行水土保持措施选址，方法实用，但时效性较低。",[447],{"name":441,"url":438},[27,28,283,449,450],"水土保持","半干旱区",{"code":288,"slug":289,"name":290,"path":291,"parent_code":49},[453,454],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":456},{"candidates":457,"agricultural_evidence":58},[458,464,468,472,477,482],{"code":288,"score":19,"supported":58,"title":459,"abstract":460,"tags":463},[283],[461,283,462],"地理信息","geographic information",[283],{"code":45,"score":66,"supported":58,"title":465,"abstract":466,"tags":467},[73],[28,73],[28],{"code":40,"score":218,"supported":58,"title":469,"abstract":470,"tags":471},[41],[41],[],{"code":33,"score":473,"supported":58,"title":474,"abstract":475,"tags":476},4,[],[62],[27],{"code":478,"score":473,"supported":58,"title":479,"abstract":480,"tags":481},"H6",[],[449],[449],{"code":78,"score":90,"supported":58,"title":483,"abstract":484,"tags":485},[],[227],[],[487,488],"农业水资源 半干旱区 水土保持 遥感","农业水资源 半干旱区","农业水资源半干旱区水土保持遥感-1768","10.9734\u002Fijecc\u002F2026\u002Fv16i95662",{"doi":490,"openalex_id":492,"authors":493,"venue":441,"cited_by_count":109,"oa_url":438,"card":503,"direction":115,"ingested_from":117},"W7208812908",[494,497,499,501],{"name":495,"orcid":496},"Prasannajit D. Gangurde","https:\u002F\u002Forcid.org\u002F0009-0002-9266-8931",{"name":498,"orcid":9},"Sanjay N. Pawar",{"name":500,"orcid":9},"M. S. Pendke",{"name":502,"orcid":9},"Anilkumar M. Kamble",{"tldr":504,"method":505,"finding":506,"direction":115,"opportunity":507},"利用遥感和GIS对半干旱流域进行水土保持措施的空间规划与选址。","集成SRTM DEM、Landsat-8影像，通过多准则评价进行选址。","识别出40个潜在保护结构，包括池塘、堤坝等，用于水资源管理。","可扩展至其他半干旱流域，结合高分辨率影像和机器学习优化选址精度。","2026-09-06T23:30:27.290464Z",{"id":510,"title":511,"url":512,"summary":513,"summary_zh":514,"content":9,"source_name":10,"source_url":512,"published_at":515,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":516,"score_detail":517,"sources":519,"tags":521,"primary_topic":523,"secondary_topics":529,"classification":532,"search_phrases":557,"slug":560,"view_count":22,"doi":561,"paper":562,"created_at":591},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":90,"relevant":22,"comment":518},"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。",[520],{"name":10,"url":512},[205,28,29,522],"柑橘",{"code":524,"slug":525,"name":526,"path":527,"parent_code":528},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[530,531],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":533},{"candidates":534,"agricultural_evidence":58},[535,540,544,548,552],{"code":524,"score":66,"supported":58,"title":536,"abstract":538,"tags":539},[537],"citrus",[522,537],[522],{"code":40,"score":66,"supported":58,"title":541,"abstract":542,"tags":543},[41],[69,41],[29],{"code":45,"score":66,"supported":58,"title":545,"abstract":546,"tags":547},[73],[28,73],[28],{"code":33,"score":79,"supported":58,"title":549,"abstract":550,"tags":551},[],[61,62,34,63],[],{"code":245,"score":473,"supported":58,"title":553,"abstract":554,"tags":556},[],[555],"精准农业",[205],[558,559],"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":561,"openalex_id":563,"authors":564,"venue":10,"cited_by_count":109,"oa_url":512,"card":586,"direction":115,"ingested_from":117},"W7204808715",[565,567,569,571,574,577,580,582,584],{"name":566,"orcid":9},"Zongjun Wu",{"name":568,"orcid":9},"Bin Zhu",{"name":570,"orcid":9},"Ningbo Cui",{"name":572,"orcid":573},"Wei Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-0215-8584",{"name":575,"orcid":576},"Yanjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5286-6269",{"name":578,"orcid":579},"Liwen Xing","https:\u002F\u002Forcid.org\u002F0000-0003-0007-3560",{"name":581,"orcid":9},"Quanshan Liu",{"name":583,"orcid":9},"Shenglin Wen",{"name":585,"orcid":9},"Bihang Fan",{"tldr":587,"method":588,"finding":589,"direction":115,"opportunity":590},"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"]