[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4366":3,"related-4366":118},{"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":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":117},4366,"Article topic: comprehensive assessment of soil and flora contamination by heavy metals in the zone of influence of the Nikolaevskaya enrichment plant","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_46","Agricultural lands in East Kazakhstan region cover a significant area, with large portions of pasture and cropland situated in close proximity to operating industrial facilities. Despite this, no comprehensive integrated assessment combining remote sensing, ground-based soil monitoring, and flora survey had previously been conducted for the zone of influence of the Nikolaevskaya enrichment plant. This study aimed to fill that gap by a 40-year land cover analysis (1985-2024) using publicly available satellite data processed in Google Earth Engine with soil sampling and vegetation surveys across 17 sites. Soil analyses revealed not only elevated heavy metal concentrations but also a sharp pH decrease, conditions under which heavy metals become significantly more mobile and available for plant uptake. Technosol formation was observed at multiple sampling points, indicating a permanent transformation of the natural soil profile. The flora survey recorded 72 species from 62 genera, with ruderal and synanthropic species domina-ting disturbed habitats and native species persisting only at control points more distant from the facility. Taken together, these findings point to ongoing ecosystem degradation and raise concrete concerns for the regional food chain given the pastureland and cropland adjacent to the plant.","东哈萨克斯坦地区的农业用地面积广阔，其中大片牧场和农田与正在运营的工业设施相距不远。尽管如此，此前尚未针对尼古拉耶夫斯克选矿厂影响区域开展过将遥感、地面土壤监测与植物区系调查相结合的综合评估。本研究旨在填补这一空白，通过为期40年的土地覆盖分析（1985—2024年），利用Google Earth Engine处理的公开卫星数据，并结合17个样点的土壤采样与植被调查。土壤分析不仅揭示了重金属浓度升高，还发现pH值急剧下降，在这种条件下，重金属的迁移性和植物可利用性显著增强。在多个采样点观察到技术土（Technosol）的形成，表明自然土壤剖面发生了永久性改变。植物区系调查共记录到62属72种植物，其中扰动生境以杂草类和伴人植物物种为主，原生物种仅存于距设施较远的对照点。综合来看，这些发现表明生态系统正在持续退化，并且鉴于该厂邻近牧场和农田，对区域食物链构成了切实的担忧。",null,"Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"结合40年遥感与地面采样的重金属污染综合评估，方法扎实、结论具体，但属区域案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"耕地保护","农业生态","遥感监测","Google Earth Engine","土壤重金属",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,69],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":57},15,true,[34],[56,34],"土壤",[31],{"code":40,"score":21,"supported":53,"title":59,"abstract":60,"tags":63},[],[61,62],"遥感","remote sensing",[30,29],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"H1",[],[],[27],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"H6",[],[],[28],[75,76],"Nikolaevskaya 选矿厂 土壤污染","东哈萨克斯坦 农田 重金属","Nikolaevskaya选矿厂土壤污染-4366",0,"10.51886\u002F1999-740x_2026_3_46",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":78,"oa_url":108,"card":109,"direction":115,"ingested_from":116},"W7220713350",[83,86,88,91,94,97,100,103,106],{"name":84,"orcid":85},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":87,"orcid":9},"M. Altynbayeva",{"name":89,"orcid":90},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":92,"orcid":93},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":95,"orcid":96},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":98,"orcid":99},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":101,"orcid":102},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":104,"orcid":105},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":107,"orcid":9},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"综合遥感、土壤采样与植被调查，评估哈萨克斯坦一选矿厂影响区40年重金属污染与生态退化。","Google Earth Engine处理1985-2024卫星数据，结合17个","土壤重金属升高且pH骤降，重金属迁移性增强，形成技术土，植被以杂草为主，威胁食物链。","农业绿色发展与碳","可延伸研究重金属活化与pH耦合机制，并构建污染区农田食物链风险预警模型。","农业遥感与作物表型","openalex","2026-10-07T23:30:29.146414Z",{"total":119,"page":22,"page_size":119,"items":120},6,[121,182,257,330,405,472],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":133,"tags":135,"primary_topic":140,"secondary_topics":141,"classification":143,"search_phrases":161,"slug":164,"view_count":165,"doi":166,"paper":167,"created_at":181},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。","COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES",70,{"impact":17,"substance":130,"depth":131,"authority":20,"freshness":21,"relevant":22,"comment":132},20,17,"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。",[134],{"name":127,"url":124},[136,137,138,29,139],"智慧农业","Sentinel-2","NDVI","土壤侵蚀",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[142],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":144},{"candidates":145,"agricultural_evidence":53},[146,151,156],{"code":40,"score":130,"supported":53,"title":147,"abstract":149,"tags":150},[148],"multispectral",[61,62,148],[138,137,29],{"code":33,"score":152,"supported":53,"title":153,"abstract":154,"tags":155},14,[34],[56,34],[],{"code":157,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},"I1",[],[],[136],[162,163],"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":166,"openalex_id":168,"authors":169,"venue":127,"cited_by_count":78,"oa_url":124,"card":176,"direction":115,"ingested_from":116},"W7220651513",[170,173],{"name":171,"orcid":172},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":174,"orcid":175},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":177,"method":178,"finding":179,"direction":115,"opportunity":180},"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。","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。","2026-10-07T23:30:29.080156Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":195,"tags":197,"primary_topic":201,"secondary_topics":202,"classification":209,"search_phrases":234,"slug":237,"view_count":229,"doi":238,"paper":239,"created_at":256},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":192,"substance":18,"depth":19,"authority":152,"freshness":193,"relevant":22,"comment":194},16,9,"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。",[196],{"name":188,"url":185},[137,29,198,199,200],"土壤退化","盐渍化","土地退化评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[203,208],{"code":204,"slug":205,"name":206,"path":207,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":210},{"candidates":211,"agricultural_evidence":53},[212,216,223,227],{"code":33,"score":52,"supported":53,"title":213,"abstract":214,"tags":215},[34],[56,34],[199],{"code":204,"score":193,"supported":53,"title":217,"abstract":218,"tags":222},[],[219,220,221],"灌溉","水资源","water resource",[],{"code":40,"score":21,"supported":53,"title":224,"abstract":225,"tags":226},[],[61,62],[137,29],{"code":228,"score":229,"supported":53,"title":230,"abstract":231,"tags":233},"H7",3,[],[232],"气候适应",[],[235,236],"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":238,"openalex_id":240,"authors":241,"venue":188,"cited_by_count":78,"oa_url":250,"card":251,"direction":115,"ingested_from":116},"W7214801255",[242,245,248],{"name":243,"orcid":244},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":246,"orcid":247},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":249,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":252,"method":253,"finding":254,"direction":115,"opportunity":255},"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":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":265,"score_detail":266,"sources":268,"tags":270,"primary_topic":274,"secondary_topics":280,"classification":283,"search_phrases":302,"slug":305,"view_count":165,"doi":306,"paper":307,"created_at":329},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":130,"depth":192,"authority":20,"freshness":21,"relevant":22,"comment":267},"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。",[269],{"name":263,"url":260},[271,137,272,29,273],"数字农业","土壤有机碳","水稻休耕",{"code":275,"slug":276,"name":277,"path":278,"parent_code":279},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[281,282],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":284},{"candidates":285,"agricultural_evidence":53},[286,290,294,298],{"code":33,"score":52,"supported":53,"title":287,"abstract":288,"tags":289},[34],[56,34],[272],{"code":275,"score":152,"supported":53,"title":291,"abstract":292,"tags":293},[276],[277,276],[],{"code":40,"score":21,"supported":53,"title":295,"abstract":296,"tags":297},[],[61,62],[137,29],{"code":157,"score":22,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[271],[303,304],"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":306,"openalex_id":308,"authors":309,"venue":263,"cited_by_count":78,"oa_url":260,"card":324,"direction":115,"ingested_from":116},"W7214617000",[310,313,315,318,321],{"name":311,"orcid":312},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":314,"orcid":9},"D Priya",{"name":316,"orcid":317},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":319,"orcid":320},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":322,"orcid":323},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":325,"method":326,"finding":327,"direction":115,"opportunity":328},"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":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":337,"category":12,"cover_url":9,"hotness":338,"is_selected":14,"score":339,"score_detail":340,"sources":342,"tags":346,"primary_topic":351,"secondary_topics":356,"classification":359,"search_phrases":385,"slug":388,"view_count":377,"doi":389,"paper":390,"created_at":404},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,66,{"impact":17,"substance":19,"depth":52,"authority":17,"freshness":193,"relevant":22,"comment":341},"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[343,344],{"name":336,"url":333},{"name":336,"url":345},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[347,29,348,349,350],"土地退化","GIS","土壤盐渍化","灌溉农业",{"code":352,"slug":353,"name":354,"path":355,"parent_code":44},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[357,358],{"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":360},{"candidates":361,"agricultural_evidence":53},[362,368,372,376,381],{"code":352,"score":19,"supported":53,"title":363,"abstract":364,"tags":367},[348],[365,348,366],"地理信息","geographic information",[348],{"code":40,"score":52,"supported":53,"title":369,"abstract":370,"tags":371},[62],[61,62],[29],{"code":33,"score":152,"supported":53,"title":373,"abstract":374,"tags":375},[34],[56,34],[],{"code":204,"score":377,"supported":53,"title":378,"abstract":379,"tags":380},4,[],[219],[350],{"code":65,"score":22,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[347],[386,387],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279","10.5281\u002Fzenodo.22900049",{"doi":389,"openalex_id":391,"authors":392,"venue":336,"cited_by_count":78,"oa_url":333,"card":399,"direction":115,"ingested_from":116},"W7214052773",[393,395,397],{"name":394,"orcid":9},"Mardiyev",{"name":396,"orcid":9},"Ergashaliyev",{"name":398,"orcid":9},"G'aniyev",{"tldr":400,"method":401,"finding":402,"direction":115,"opportunity":403},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","2026-09-23T23:30:19.341486Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":9,"source_name":411,"source_url":408,"published_at":412,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":413,"score_detail":414,"sources":416,"tags":418,"primary_topic":422,"secondary_topics":423,"classification":430,"search_phrases":452,"slug":455,"view_count":119,"doi":456,"paper":457,"created_at":471},3174,"Remote sensing and geospatial modeling for the detection of war-induced land use abandonment, soil disturbance, and degradation in Sumy region of Ukraine: a systematic review","https:\u002F\u002Fdoi.org\u002F10.31548\u002Fzemleustriy2026.03.015","The full-scale military invasion of Ukraine has caused multidimensional transformations of agricultural landscapes, land cover, and ecosystems. The Sumy region, located along the northeastern border with the Russian Federation, is a critical region that has suffered from intense fighting, prolonged artillery shelling, and ongoing border clashes during the study period from 2022 to May 2026. This paper systematically evaluates the application of remote sensing and geospatial modeling techniques to detect, quantify, and monitor war-induced land abandonment, as well as physical land cover disturbances, chemical contamination, and broader environmental degradation in this theater of war. Combining recent scientific publications on the war in Ukraine and comparative global examples (Syria, Iraq, and Sudan), the effectiveness of integrating multi-sensor data, including optical (Sentinel-2, Landsat), synthetic aperture radar (SAR; Sentinel-1), thermal, and very high resolution (VHR; Maxar, WorldView) platforms, as well as advanced machine and deep learning algorithms (Random Forest, Deep Learning) and time series analysis (FANTA, two-period curve approximation), is assessed. The results of the study show that while geospatial modeling is an indispensable tool for rapid, large-scale, and non-contact damage assessment in active conflict settings, significant critical research gaps remain, namely separating short-term cessation of agricultural use from actual land abandonment caused by war, verifying such facts in the face of a severe shortage of reliable ground data, and managing sensitive geospatial information. This systematic review highlights existing methodological gaps and outlines future perspectives needed to build a harmonized spatial monitoring system capable of guiding post-conflict recovery and environmental remediation based on actual remote sensing data at the regional scale.Received: 01.07.2026;Accepted:26.08.2026;","乌克兰遭受的大规模军事入侵已导致农业景观、土地覆盖和生态系统发生多维转变。苏梅州位于乌克兰与俄罗斯联邦东北部接壤的边境沿线，是一个关键地区，在研究期间（2022年至2026年5月）遭受了激烈战斗、长期炮击以及持续边境冲突的影响。本文系统评估了遥感与地理空间建模技术在探测、量化和监测该战区战争导致的土地撂荒、土地覆盖物理扰动、化学污染及更广泛环境退化方面的应用。结合近期关于乌克兰战争的科学出版物及全球比较案例（叙利亚、伊拉克和苏丹），本文评估了多传感器数据整合的有效性，包括光学（Sentinel-2、Landsat）、合成孔径雷达（SAR；Sentinel-1）、热红外及甚高分辨率（VHR；Maxar、WorldView）平台，以及先进的机器学习和深度学习算法（随机森林、深度学习）和时间序列分析（FANTA、两期曲线近似）。研究结果表明，尽管地理空间建模是在活跃冲突环境中进行快速、大规模、非接触式损害评估不可或缺的工具，但仍存在重要的关键研究空白，即如何区分农业利用的短期中止与战争导致的实际土地撂荒，如何在严重缺乏可靠地面数据的情况下核实此类事实，以及如何管理敏感的地理空间信息。本系统综述指出了现有的方法论空白，并勾勒了未来前景，以构建一个协调的空间监测系统，从而基于区域尺度上的实际遥感数据指导冲突后恢复和环境修复。收稿日期：2026年7月1日；接受日期：2026年8月26日；","Zemleustrìj kadastr ì monìtorìng zemelʹ","2026-09-21T00:00:00Z",75,{"impact":19,"substance":130,"depth":131,"authority":17,"freshness":21,"relevant":22,"comment":415},"系统综述遥感与地理空间建模在战区耕地撂荒与土壤退化识别中的应用，方法体系与数据源梳理扎实，对农业遥感监测有参考价值，但属境外冲突场景研究，国内落地关联度有限。",[417],{"name":411,"url":408},[419,347,29,420,421],"农业人工智能","耕地撂荒","冲突农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[424,429],{"code":425,"slug":426,"name":427,"path":428,"parent_code":44},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":431},{"candidates":432,"agricultural_evidence":53},[433,437,444,448],{"code":40,"score":52,"supported":53,"title":434,"abstract":435,"tags":436},[62],[61,62],[29],{"code":425,"score":13,"supported":53,"title":438,"abstract":439,"tags":443},[],[440,441,442],"深度学习","机器学习","deep learning",[419],{"code":33,"score":21,"supported":53,"title":445,"abstract":446,"tags":447},[34],[],[],{"code":65,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},[],[],[347],[453,454],"乌克兰 苏梅州 遥感 耕地撂荒","Sentinel-1 合成孔径雷达 土地退化监测","乌克兰苏梅州遥感耕地撂荒-3174","10.31548\u002Fzemleustriy2026.03.015",{"doi":456,"openalex_id":458,"authors":459,"venue":411,"cited_by_count":78,"oa_url":408,"card":466,"direction":115,"ingested_from":116},"W7213890485",[460,462,464],{"name":461,"orcid":9},"V. Bogdanets",{"name":463,"orcid":9},"Ye. Berezhniak",{"name":465,"orcid":9},"D. Brovko",{"tldr":467,"method":468,"finding":469,"direction":115,"opportunity":470},"系统综述遥感与地理空间建模在乌克兰苏梅地区检测战争导致的土地弃耕、土壤扰动与退化的应用。","综述多源遥感（Sentinel-2\u002F1、Landsat、VHR）与机器学习、时间","遥感是冲突区快速非接触损害评估的关键工具，但区分短期停耕与真正弃耕、地面数据匮乏及敏感信息管理仍是重","可研究多源时序遥感与弱监督学习结合，构建冲突区弃耕识别与地面验证缺失下的不确定性量化框架。","2026-09-22T23:30:23.289241Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":480,"score_detail":481,"sources":483,"tags":485,"primary_topic":488,"secondary_topics":489,"classification":491,"search_phrases":519,"slug":522,"view_count":22,"doi":523,"paper":524,"created_at":554},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":192,"substance":18,"depth":19,"authority":20,"freshness":193,"relevant":22,"comment":482},"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。",[484],{"name":478,"url":475},[136,486,487,29,349],"无人机","精准农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[490],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":492},{"candidates":493,"agricultural_evidence":53},[494,499,503,508,513],{"code":40,"score":19,"supported":53,"title":495,"abstract":497,"tags":498},[496],"hyperspectral",[61,62,496],[29],{"code":33,"score":152,"supported":53,"title":500,"abstract":501,"tags":502},[34],[56,34],[],{"code":157,"score":21,"supported":53,"title":504,"abstract":505,"tags":507},[],[487,506],"precision agriculture",[136,487],{"code":425,"score":119,"supported":53,"title":509,"abstract":510,"tags":512},[],[441,511],"machine learning",[],{"code":514,"score":229,"supported":53,"title":515,"abstract":516,"tags":518},"J3",[],[517],"全球农业",[],[520,521],"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","土壤盐渍化智慧农业精准农业遥感监测-2664","10.3390\u002Fagronomy16181812",{"doi":523,"openalex_id":525,"authors":526,"venue":478,"cited_by_count":78,"oa_url":475,"card":549,"direction":115,"ingested_from":116},"W7213244904",[527,529,531,533,535,537,539,541,544,546],{"name":528,"orcid":9},"Haiye Yu",{"name":530,"orcid":9},"Muyan Yu",{"name":532,"orcid":9},"Ranzhe Jiang",{"name":534,"orcid":9},"Xin Zhang",{"name":536,"orcid":9},"Zhu Guo",{"name":538,"orcid":9},"Yaohui Fu",{"name":540,"orcid":9},"Xingbang Liu",{"name":542,"orcid":543},"Xingyu Sun","https:\u002F\u002Forcid.org\u002F0009-0008-8684-498X",{"name":545,"orcid":9},"Bingze Li",{"name":547,"orcid":548},"Yuanyuan Sui","https:\u002F\u002Forcid.org\u002F0000-0003-4849-9077",{"tldr":550,"method":551,"finding":552,"direction":115,"opportunity":553},"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"]