[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4365":3,"related-4365":94},{"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":45,"search_phrases":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":93},4365,"An Index-Based Algorithm for Detecting Water Soil Erosion Indicators from Sentinel-2 Multispectral Data and Its Verification on Field Data of Rostov Oblast and Krasnodar Krai","https:\u002F\u002Fdoi.org\u002F10.23947\u002F2587-8999-2026-10-3-49-57","Introduction . The paper presents the development and testing of an index-based algorithm for detecting indicators of soil erosion by water from Sentinel-2 remote sensing data, considered as the first practical stage in constructing a hybrid physics-based mass-transport model with neural-network parameter identification. Materials and Methods . The algorithm relies on joint analysis of time series of the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), followed by spatial change detection (ΔBSI) with threshold filtering at the 90th percentile. The algorithm was first validated on synthetic data and then applied to real Sentinel-2 imagery for two test sites – the Kamensky District of Rostov Oblast and the vicinity of Moldovanskoye village, Krymsky District, Krasnodar Krai – covering 10 dates within 2024. Results. The algorithm is shown to consistently identify spatially localised zones of increasing bare-soil exposure. Discussion . A key limitation – the inability to distinguish erosion-related exposure from ordinary agrotechnical bare soil — is discussed, along with directions for resolving it (digital elevation model overlay, multi-year analysis). Conclusion . An index-based algorithm for detecting indicators of water-induced soil erosion from Sentinel-2 multispectral time series was developed and validated. Verification on synthetic data and application to two real test areas in southern Russia confirmed its operability while demonstrating the need to separate erosion-related changes from agricultural soil exposure in subsequent model development.","引言。本文介绍了一种基于指数的算法开发与测试，该算法用于从Sentinel-2遥感数据中检测水蚀土壤侵蚀指标，被视为构建基于物理的质量输运混合模型并结合神经网络参数辨识的第一个实践阶段。材料与方法。该算法依赖于对归一化植被指数（NDVI）和裸土指数（BSI）时间序列的联合分析，随后进行空间变化检测（ΔBSI），并在第90百分位数处进行阈值滤波。该算法首先在合成数据上进行了验证，然后应用于两个测试点的真实Sentinel-2影像——罗斯托夫州卡缅斯基区和克拉斯诺达尔边疆区克里姆斯克区摩尔多瓦诺夫斯科耶村附近——覆盖2024年内的10个日期。结果。结果表明，该算法能够一致地识别出裸土暴露增加的空间局部区域。讨论。讨论了关键局限性——无法区分与侵蚀相关的裸土暴露和普通农技作业造成的裸土——以及解决该问题的方向（叠加数字高程模型、多年分析）。结论。开发并验证了一种基于指数的算法，用于从Sentinel-2多光谱时间序列中检测水蚀土壤侵蚀指标。在合成数据上的验证以及在俄罗斯南部两个真实测试区域的应用证实了其可操作性，同时表明在后续模型开发中有必要将与侵蚀相关的变化与农业裸土暴露区分开来。",null,"COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES","2026-10-05T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"提出基于NDVI与BSI时序的土壤水蚀指标遥感识别算法，并在俄罗斯南部两个试验区验证，方法有新意但属区域尺度研究，对国内农业遥感监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","Sentinel-2","NDVI","遥感监测","土壤侵蚀",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,59,65],{"code":33,"score":18,"supported":52,"title":53,"abstract":55,"tags":58},true,[54],"multispectral",[56,57,54],"遥感","remote sensing",[29,28,30],{"code":40,"score":60,"supported":52,"title":61,"abstract":62,"tags":64},14,[41],[63,41],"土壤",[],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I1",[],[],[27],[71,72],"Sentinel-2 土壤侵蚀 遥感","罗斯托夫州 克拉斯诺达尔 土壤侵蚀","Sentinel-2土壤侵蚀遥感-4365",2,"10.23947\u002F2587-8999-2026-10-3-49-57",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7220651513",[79,82],{"name":80,"orcid":81},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":83,"orcid":84},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"开发基于Sentinel-2时序NDVI与BSI指数的水土流失指标检测算法，并在俄罗斯南部实地验证。","联合NDVI与BSI时序，用ΔBSI阈值（90百分位）检测裸土变化，合成与真实数","算法能稳定识别裸土暴露增加区域，但无法区分侵蚀与农事裸土，需DEM和多年度分析。","农业遥感与作物表型","可融合DEM与多年度时序，构建区分侵蚀与农事裸土的分类模型，并接入神经网络参数识别。","openalex","2026-10-07T23:30:29.080156Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,164,237,310,394,475],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":105,"is_selected":14,"score":106,"score_detail":107,"sources":111,"tags":115,"primary_topic":118,"secondary_topics":119,"classification":121,"search_phrases":144,"slug":147,"view_count":22,"doi":148,"paper":149,"created_at":163},2321,"ASSESSMENT OF SOIL SALINITY USING THE NDVI VEGETATION INDEX BASED ON REMOTE SENSING DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702718","This article examines the assessment of soil salinity in the irrigated meadow-sierozem soils of the E. Qahhorov agricultural area in Khovos District, Syrdarya Region, based on remote sensing data. Sentinel-2 satellite data covering the period from 2016 to 2025 were used in the study, and the Normalized Difference Vegetation Index (NDVI) was calculated using geospatial technologies. The long-term and seasonal dynamics of vegetation cover in the study area were analyzed, and the relationship between NDVI values and salinity levels was assessed. Based on an NDVI classification adapted to the natural conditions of the study area, criteria for the indirect assessment of soil salinity levels were proposed. The results demonstrated that the NDVI can be effectively used as an indicator for determining the impact of soil salinity on vegetation cover, assessing salinity spatially, and conducting long-term monitoring of salinity in irrigated agricultural lands.","本文基于遥感数据，对锡尔河州霍沃斯区E. Qahhorov农业区灌溉草甸灰钙土的土壤盐渍化进行了评估。研究使用了2016年至2025年期间的Sentinel-2卫星数据，并利用地理空间技术计算了归一化植被指数（NDVI）。分析了研究区植被覆盖的长期和季节性动态，并评估了NDVI值与盐渍化程度之间的关系。基于适应研究区自然条件的NDVI分类，提出了土壤盐渍化程度间接评估的标准。结果表明，NDVI可有效用作确定土壤盐渍化对植被覆盖影响、空间评估盐渍化以及开展灌溉农田盐渍化长期监测的指标。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-11T00:00:00Z",25,65,{"impact":17,"substance":108,"depth":109,"authority":17,"freshness":21,"relevant":22,"comment":110},18,15,"基于Sentinel-2长时序NDVI数据提出土壤盐渍化间接评估标准，方法可复用但属区域性案例研究，公共影响有限。",[112,113],{"name":103,"url":100},{"name":103,"url":114},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702719",[27,29,30,116,117],"土壤盐渍化","灌溉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[120],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":122},{"candidates":123,"agricultural_evidence":52},[124,129,133,140],{"code":33,"score":125,"supported":52,"title":126,"abstract":127,"tags":128},16,[57],[56,57],[29,30],{"code":40,"score":60,"supported":52,"title":130,"abstract":131,"tags":132},[41],[63,41],[],{"code":134,"score":135,"supported":52,"title":136,"abstract":137,"tags":139},"H3",4,[],[138],"灌溉",[117],{"code":66,"score":22,"supported":14,"title":141,"abstract":142,"tags":143},[],[],[27],[145,146],"土壤盐渍化 智慧农业 灌溉农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业灌溉农业遥感监测-2321","10.5281\u002Fzenodo.22702718",{"doi":148,"openalex_id":150,"authors":151,"venue":103,"cited_by_count":85,"oa_url":100,"card":158,"direction":90,"ingested_from":92},"W7212227143",[152,154,156],{"name":153,"orcid":9},"U.K. Umirova",{"name":155,"orcid":9},"D.A. Kodirova",{"name":157,"orcid":9},"O.O. Davronov",{"tldr":159,"method":160,"finding":161,"direction":90,"opportunity":162},"利用Sentinel-2遥感数据计算NDVI，评估灌溉草甸灰钙土的土壤盐渍化程度。","Sentinel-2数据（2016-2025）与NDVI指数，结合地理空间技术分","NDVI可有效指示盐渍化对植被的影响，实现空间评估与长期监测。","可探索多指数融合与机器学习提升盐渍化反演精度，并推广至不同土壤类型区。","2026-09-13T23:30:22.820810Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"primary_topic":183,"secondary_topics":184,"classification":190,"search_phrases":214,"slug":217,"view_count":209,"doi":218,"paper":219,"created_at":236},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":125,"substance":174,"depth":108,"authority":60,"freshness":175,"relevant":22,"comment":176},22,9,"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。",[178],{"name":170,"url":167},[28,30,180,181,182],"土壤退化","盐渍化","土地退化评估",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[185,189],{"code":134,"slug":186,"name":187,"path":188,"parent_code":44},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":191},{"candidates":192,"agricultural_evidence":52},[193,197,203,207],{"code":40,"score":109,"supported":52,"title":194,"abstract":195,"tags":196},[41],[63,41],[181],{"code":134,"score":175,"supported":52,"title":198,"abstract":199,"tags":202},[],[138,200,201],"水资源","water resource",[],{"code":33,"score":21,"supported":52,"title":204,"abstract":205,"tags":206},[],[56,57],[28,30],{"code":208,"score":209,"supported":52,"title":210,"abstract":211,"tags":213},"H7",3,[],[212],"气候适应",[],[215,216],"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":218,"openalex_id":220,"authors":221,"venue":170,"cited_by_count":85,"oa_url":230,"card":231,"direction":90,"ingested_from":92},"W7214801255",[222,225,228],{"name":223,"orcid":224},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":226,"orcid":227},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":229,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":232,"method":233,"finding":234,"direction":90,"opportunity":235},"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":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":248,"tags":250,"primary_topic":254,"secondary_topics":260,"classification":263,"search_phrases":282,"slug":285,"view_count":74,"doi":286,"paper":287,"created_at":309},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":17,"substance":18,"depth":125,"authority":20,"freshness":21,"relevant":22,"comment":247},"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[249],{"name":243,"url":240},[251,28,252,30,253],"数字农业","土壤有机碳","水稻休耕",{"code":255,"slug":256,"name":257,"path":258,"parent_code":259},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[261,262],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":264},{"candidates":265,"agricultural_evidence":52},[266,270,274,278],{"code":40,"score":109,"supported":52,"title":267,"abstract":268,"tags":269},[41],[63,41],[252],{"code":255,"score":60,"supported":52,"title":271,"abstract":272,"tags":273},[256],[257,256],[],{"code":33,"score":21,"supported":52,"title":275,"abstract":276,"tags":277},[],[56,57],[28,30],{"code":66,"score":22,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[251],[283,284],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":286,"openalex_id":288,"authors":289,"venue":243,"cited_by_count":85,"oa_url":240,"card":304,"direction":90,"ingested_from":92},"W7214617000",[290,293,295,298,301],{"name":291,"orcid":292},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":294,"orcid":9},"D Priya",{"name":296,"orcid":297},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":299,"orcid":300},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":302,"orcid":303},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":305,"method":306,"finding":307,"direction":90,"opportunity":308},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":326,"secondary_topics":327,"classification":329,"search_phrases":359,"slug":361,"view_count":22,"doi":362,"paper":363,"created_at":393},2664,"Soil Salinity Mapping from UAV-Borne Hyperspectral Imagery with Soil Moisture Correction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181812","Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with Rp2 = 0.87, RMSEP = 0.67 mS·cm−1, and RPD = 2.93. Compared with the Ref–Stacking model, Rp2 increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm−1 (from 1.25 to 0.67 mS·cm−1). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture.","土壤盐渍化对全球农业可持续发展和生态安全构成重大威胁，每年造成大量作物损失。基于无人机的高光谱遥感凭借其高空间分辨率和灵活的数据获取能力，为土壤盐分监测提供了有前景的解决方案。然而，土壤水分会改变电磁辐射的散射和吸收特性，从而改变土壤光谱反射率并掩盖与盐分相关的诊断性光谱特征，这可能降低传统盐分估算模型的精度。本研究评估了六种光谱变换方法——原始反射率数据（Ref）、一阶导数（FDR）、分段直接标准化（PDS）、正交信号校正（OSC）、FDR + PDS和FDR + OSC——并结合三种机器学习算法：K近邻（KNN）、支持向量回归（SVR）和多层感知机（MLP）。进一步构建了集成这些基学习器的Stacking集成模型，以提高水分干扰下的土壤盐分反演精度。结果表明，Stacking模型在所评估的模型中达到了最高的精度和稳定性。与XGBoost和随机森林（RF）的额外比较进一步证实了所提出的Stacking框架的竞争性性能。FDR + OSC–Stacking组合取得了最佳的验证表现，Rp2 = 0.87，RMSEP = 0.67 mS·cm−1，RPD = 2.93。与Ref–Stacking模型相比，Rp2提高了0.32（从0.55增至0.87），而RMSEP降低了0.58 mS·cm−1（从1.25降至0.67 mS·cm−1）。结果表明，PDS在校正水分相关光谱变异方面效果有限，而OSC在保留与盐分估算相关的光谱信息的同时，更有效地减轻了水分干扰。在所评估的机器学习模型中，Stacking集成模型取得了优于MLP、SVR和KNN的预测性能。此外，FDR + OSC–Stacking组合在所评估的建模框架中表现最佳，并成功应用于无人机高光谱影像进行EC1:5的空间制图。这些发现证明了将适当的光谱校正与Stacking相结合用于基于无人机的土壤盐分评估的潜力，并为精准农业中的针对性盐分管理提供了有用的技术支持。","Agronomy","2026-09-15T00:00:00Z",78,{"impact":125,"substance":174,"depth":108,"authority":20,"freshness":175,"relevant":22,"comment":320},"该研究提出FDR+OSC光谱校正与Stacking集成模型相结合的无人机高光谱土壤盐分反演方法，验证精度显著提升（Rp²=0.87），为盐渍化农田精准管理提供了可落地的技术方案，专业深度与信息增量均较突出。",[322],{"name":316,"url":313},[27,324,325,30,116],"无人机","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[328],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":330},{"candidates":331,"agricultural_evidence":52},[332,337,341,346,353],{"code":33,"score":108,"supported":52,"title":333,"abstract":335,"tags":336},[334],"hyperspectral",[56,57,334],[30],{"code":40,"score":60,"supported":52,"title":338,"abstract":339,"tags":340},[41],[63,41],[],{"code":66,"score":21,"supported":52,"title":342,"abstract":343,"tags":345},[],[325,344],"precision agriculture",[27,325],{"code":347,"score":95,"supported":52,"title":348,"abstract":349,"tags":352},"I2",[],[350,351],"机器学习","machine learning",[],{"code":354,"score":209,"supported":52,"title":355,"abstract":356,"tags":358},"J3",[],[357],"全球农业",[],[360,146],"土壤盐渍化 智慧农业 精准农业 遥感监测","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":362,"openalex_id":364,"authors":365,"venue":316,"cited_by_count":85,"oa_url":313,"card":388,"direction":90,"ingested_from":92},"W7213244904",[366,368,370,372,374,376,378,380,383,385],{"name":367,"orcid":9},"Haiye Yu",{"name":369,"orcid":9},"Muyan Yu",{"name":371,"orcid":9},"Ranzhe Jiang",{"name":373,"orcid":9},"Xin Zhang",{"name":375,"orcid":9},"Zhu Guo",{"name":377,"orcid":9},"Yaohui Fu",{"name":379,"orcid":9},"Xingbang Liu",{"name":381,"orcid":382},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":384,"orcid":9},"Bingze Li",{"name":386,"orcid":387},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":389,"method":390,"finding":391,"direction":90,"opportunity":392},"用无人机高光谱结合水分校正与Stacking集成模型反演土壤盐分。","六种光谱变换与KNN\u002FSVR\u002FMLP及Stacking，对比XGBoost、RF","FDR+OSC-Stacking最优，Rp²=0.87，OSC比PDS更能消除水分干扰。","可探索水分校正与深度学习结合、多时相无人机盐分动态监测及跨区域迁移。","2026-09-16T23:30:29.016721Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":410,"secondary_topics":411,"classification":418,"search_phrases":460,"slug":463,"view_count":74,"doi":9,"paper":464,"created_at":474},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":125,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":404},"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[406],{"name":400,"url":397},[27,325,56,408,409],"盐碱地治理","土壤监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[412,417],{"code":413,"slug":414,"name":415,"path":416,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":419},{"candidates":420,"agricultural_evidence":52},[421,425,433,437,441,445,449,454],{"code":33,"score":108,"supported":52,"title":422,"abstract":423,"tags":424},[57],[56,57,54],[56],{"code":413,"score":19,"supported":52,"title":426,"abstract":428,"tags":432},[427],"GIS",[429,430,427,431],"地理信息","空间分析","geographic information",[],{"code":40,"score":109,"supported":52,"title":434,"abstract":435,"tags":436},[41],[63,41],[408],{"code":66,"score":13,"supported":52,"title":438,"abstract":439,"tags":440},[344],[],[27,325],{"code":347,"score":95,"supported":52,"title":442,"abstract":443,"tags":444},[],[350,351],[],{"code":134,"score":209,"supported":52,"title":446,"abstract":447,"tags":448},[],[138],[],{"code":208,"score":209,"supported":52,"title":450,"abstract":451,"tags":453},[],[452],"干旱",[],{"code":455,"score":209,"supported":52,"title":456,"abstract":457,"tags":459},"I7",[],[458],"methodological framework",[],[461,462],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":465,"authors":466,"venue":400,"cited_by_count":85,"oa_url":397,"card":469,"direction":90,"ingested_from":92},"W7221843179",[467],{"name":468,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":470,"method":471,"finding":472,"direction":90,"opportunity":473},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":480,"content":9,"source_name":481,"source_url":478,"published_at":482,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":483,"score_detail":484,"sources":487,"tags":489,"primary_topic":492,"secondary_topics":493,"classification":499,"search_phrases":523,"slug":526,"view_count":85,"doi":527,"paper":528,"created_at":548},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":17,"substance":485,"depth":19,"authority":20,"freshness":175,"relevant":22,"comment":486},21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[488],{"name":481,"url":478},[27,490,56,116,491],"农业人工智能","新疆农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[494,495],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":347,"slug":496,"name":497,"path":498,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":46,"locked":14,"version":47,"method":48,"evidence":500},{"candidates":501,"agricultural_evidence":52},[502,506,510,515,519],{"code":33,"score":109,"supported":52,"title":503,"abstract":504,"tags":505},[57],[56,57],[56],{"code":40,"score":60,"supported":52,"title":507,"abstract":508,"tags":509},[41],[63,41],[],{"code":347,"score":175,"supported":52,"title":511,"abstract":513,"tags":514},[512],"deep learning",[],[490],{"code":208,"score":209,"supported":52,"title":516,"abstract":517,"tags":518},[],[452],[],{"code":66,"score":22,"supported":14,"title":520,"abstract":521,"tags":522},[],[],[27],[524,525],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":527,"openalex_id":529,"authors":530,"venue":481,"cited_by_count":85,"oa_url":478,"card":543,"direction":90,"ingested_from":92},"W7221816183",[531,533,536,538,540],{"name":532,"orcid":9},"Mingjie Qian",{"name":534,"orcid":535},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":537,"orcid":9},"Peng Wang",{"name":539,"orcid":9},"Hangyuan Liu",{"name":541,"orcid":542},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":544,"method":545,"finding":546,"direction":90,"opportunity":547},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z"]