[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3845":3,"related-3845":110},{"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":84,"slug":87,"view_count":79,"doi":88,"paper":89,"created_at":109},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。",null,"Scientific Reports","2026-09-29T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"基于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。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","遥感监测","土壤退化","盐渍化","土地退化评估",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"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},[56,63,70,77],{"code":33,"score":57,"supported":58,"title":59,"abstract":60,"tags":62},15,true,[34],[61,34],"土壤",[30],{"code":40,"score":21,"supported":58,"title":64,"abstract":65,"tags":69},[],[66,67,68],"灌溉","水资源","water resource",[],{"code":45,"score":71,"supported":58,"title":72,"abstract":73,"tags":76},8,[],[74,75],"遥感","remote sensing",[27,28],{"code":78,"score":79,"supported":58,"title":80,"abstract":81,"tags":83},"H7",3,[],[82],"气候适应",[],[85,86],"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":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":100,"oa_url":101,"card":102,"direction":106,"ingested_from":108},"W7214801255",[92,95,98],{"name":93,"orcid":94},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":96,"orcid":97},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":99,"orcid":9},"Rasul Shamsiev",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"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。","农业遥感与作物表型","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。","openalex","2026-09-30T23:30:29.386707Z",{"total":111,"page":22,"page_size":111,"items":112},6,[113,189,250,322,429,493],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":125,"tags":127,"primary_topic":132,"secondary_topics":133,"classification":136,"search_phrases":160,"slug":163,"view_count":79,"doi":164,"paper":165,"created_at":188},3790,"Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110822","Soil moisture (SM) plays a critical role in the land–atmosphere water cycle and agricultural water resource management. However, the applicability of prevalent SM products in typical irrigation districts remains insufficiently assessed. In this study, two in situ SM observation networks—an automatic meteorological station network (AMS) and an agro-meteorological experimental station network (AES)—were used to comprehensively evaluate the accuracy and irrigation-associated response characteristics of seven microwave and reanalysis SM products in the North China Plain. The results indicated that: (1) AES-based evaluations generally showed a higher overall correlation (R = 0.58) and smaller errors with the SM products than AMS-based evaluations (R = 0.54). However, when restricted to common observation dates, the correlation coefficients decreased while the biases were close to zero in the AES-based evaluation. (2) Significant differences were observed among the evaluated products under different monitoring objectives. The ERA5-Land reanalysis product performed best for continuous SM monitoring, while the European Space Agency Climate Change Initiative (ESA CCI) Passive product achieved the highest correlation coefficient with in situ observations (R = 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.043 m 3 m −3 ) on recorded irrigation days and exhibited a comparatively pronounced positive SM response under precipitation-controlled conditions, indicating greater sensitivity to short-term irrigation-associated wetting. (3) Strict precipitation control substantially affected the interpretation of irrigation-associated responses. ERA5-Land and GLDAS-Noah showed little systematic positive change when precipitation was controlled using both station observations and model precipitation forcing, while threshold-based probability of detection (POD) varied substantially with the selected SM response threshold. These findings demonstrate that product suitability depends on the monitoring objective and that precipitation attribution and threshold sensitivity should be explicitly considered when using SM products for irrigation-related applications.","土壤水分（SM）在陆气水循环和农业水资源管理中起着至关重要的作用。然而，主流SM产品在典型灌区的适用性仍缺乏充分评估。本研究利用两套原位SM观测网络——自动气象站网络（AMS）和农业气象实验站网络（AES），综合评估了七种微波和再分析SM产品在华北平原的精度及与灌溉相关的响应特征。结果表明：（1）基于AES的评估总体上显示出比基于AMS的评估更高的整体相关性（R = 0.58）和更小的误差（R = 0.54）。然而，当限定于共同观测日期时，基于AES的评估中相关系数下降，而偏差接近于零。（2）在不同监测目标下，所评估产品之间存在显著差异。ERA5-Land再分析产品在连续SM监测中表现最佳，而欧洲空间局气候变化倡议（ESA CCI）被动产品在记录灌溉日与原位观测的相关系数最高（R = 0.69），无偏均方根误差最低（ubRMSE = 0.043 m³ m⁻³），并在降水控制条件下表现出相对明显的SM正响应，表明其对短期灌溉相关增湿具有更高的敏感性。（3）严格的降水控制显著影响了对灌溉相关响应的解释。当利用站点观测和模型降水强迫控制降水时，ERA5-Land和GLDAS-Noah几乎未表现出系统性正变化，而基于阈值的检测概率（POD）随所选SM响应阈值的变化而显著变化。这些发现表明，产品适用性取决于监测目标，且在将SM产品用于灌溉相关应用时，应明确考虑降水归因和阈值敏感性。","Agricultural Water Management","2026-09-28T00:00:00Z",74,{"impact":123,"substance":18,"depth":19,"authority":20,"freshness":71,"relevant":22,"comment":124},12,"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。",[126],{"name":119,"url":116},[128,74,129,130,131],"农业水资源","土壤墒情","灌溉管理","华北平原",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[134,135],{"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":137},{"candidates":138,"agricultural_evidence":58},[139,144,148,153],{"code":40,"score":140,"supported":58,"title":141,"abstract":142,"tags":143},19,[41],[66,67,41,68],[128,130],{"code":33,"score":57,"supported":58,"title":145,"abstract":146,"tags":147},[34],[61,34],[129],{"code":45,"score":123,"supported":58,"title":149,"abstract":150,"tags":152},[75],[151],"估产",[74],{"code":78,"score":21,"supported":58,"title":154,"abstract":155,"tags":159},[],[156,157,158],"气候变化","农业气象","climate change",[],[161,162],"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","10.1016\u002Fj.agwat.2026.110822",{"doi":164,"openalex_id":166,"authors":167,"venue":119,"cited_by_count":100,"oa_url":182,"card":183,"direction":106,"ingested_from":108},"W7214549221",[168,171,173,175,177,179],{"name":169,"orcid":170},"Rui Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-6114-6412",{"name":172,"orcid":9},"Yu Li",{"name":174,"orcid":9},"Jiawei Lv",{"name":176,"orcid":9},"Lei Wang",{"name":178,"orcid":9},"Zhiqing Cheng",{"name":180,"orcid":181},"Dong Wu","https:\u002F\u002Forcid.org\u002F0000-0002-3202-255X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0378377426007043\u002Fpdf",{"tldr":184,"method":185,"finding":186,"direction":106,"opportunity":187},"评估七种遥感与再分析土壤湿度产品在典型灌区的精度及对灌溉事件的响应。","用自动气象站与农业气象站两套实测网络，对比七种微波\u002F再分析产品。","ESA CCI Passive对灌溉日响应最灵敏，ERA5-Land适合连续监测，降水控制影响响应判","可针对灌溉事件构建降水控制下的阈值自适应土壤湿度响应检测方法，提升灌区遥感监测可靠性。","2026-09-29T23:30:28.424080Z",{"id":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":197,"score_detail":198,"sources":203,"tags":205,"primary_topic":209,"secondary_topics":210,"classification":212,"search_phrases":229,"slug":232,"view_count":233,"doi":234,"paper":235,"created_at":249},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","2026-10-05T00:00:00Z",70,{"impact":123,"substance":199,"depth":200,"authority":201,"freshness":71,"relevant":22,"comment":202},20,17,13,"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。",[204],{"name":195,"url":192},[206,27,207,28,208],"智慧农业","NDVI","土壤侵蚀",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[211],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":213},{"candidates":214,"agricultural_evidence":58},[215,220,224],{"code":45,"score":199,"supported":58,"title":216,"abstract":218,"tags":219},[217],"multispectral",[74,75,217],[207,27,28],{"code":33,"score":20,"supported":58,"title":221,"abstract":222,"tags":223},[34],[61,34],[],{"code":225,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},"I1",[],[],[206],[230,231],"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":234,"openalex_id":236,"authors":237,"venue":195,"cited_by_count":100,"oa_url":192,"card":244,"direction":106,"ingested_from":108},"W7220651513",[238,241],{"name":239,"orcid":240},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":242,"orcid":243},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":245,"method":246,"finding":247,"direction":106,"opportunity":248},"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":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":260,"tags":262,"primary_topic":266,"secondary_topics":272,"classification":275,"search_phrases":294,"slug":297,"view_count":233,"doi":298,"paper":299,"created_at":321},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",69,{"impact":123,"substance":199,"depth":17,"authority":201,"freshness":71,"relevant":22,"comment":259},"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。",[261],{"name":256,"url":253},[263,27,264,28,265],"数字农业","土壤有机碳","水稻休耕",{"code":267,"slug":268,"name":269,"path":270,"parent_code":271},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[273,274],{"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":276},{"candidates":277,"agricultural_evidence":58},[278,282,286,290],{"code":33,"score":57,"supported":58,"title":279,"abstract":280,"tags":281},[34],[61,34],[264],{"code":267,"score":20,"supported":58,"title":283,"abstract":284,"tags":285},[268],[269,268],[],{"code":45,"score":71,"supported":58,"title":287,"abstract":288,"tags":289},[],[74,75],[27,28],{"code":225,"score":22,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[263],[295,296],"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":298,"openalex_id":300,"authors":301,"venue":256,"cited_by_count":100,"oa_url":253,"card":316,"direction":106,"ingested_from":108},"W7214617000",[302,305,307,310,313],{"name":303,"orcid":304},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":306,"orcid":9},"D Priya",{"name":308,"orcid":309},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":311,"orcid":312},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":314,"orcid":315},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":317,"method":318,"finding":319,"direction":106,"opportunity":320},"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":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":330,"score_detail":331,"sources":333,"tags":335,"primary_topic":339,"secondary_topics":343,"classification":346,"search_phrases":400,"slug":403,"view_count":100,"doi":404,"paper":405,"created_at":428},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":201,"substance":19,"depth":57,"authority":123,"freshness":71,"relevant":22,"comment":332},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[334],{"name":328,"url":325},[156,336,28,337,338],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":78,"slug":340,"name":341,"path":342,"parent_code":37},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[344,345],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":347},{"candidates":348,"agricultural_evidence":58},[349,354,358,363,368,375,381,388,394],{"code":78,"score":19,"supported":58,"title":350,"abstract":351,"tags":353},[158],[156,82,352,158],"干旱",[156],{"code":33,"score":57,"supported":58,"title":355,"abstract":356,"tags":357},[34],[61,34],[336],{"code":40,"score":13,"supported":58,"title":359,"abstract":360,"tags":362},[],[66,67,41,361],"evapotranspiration",[337],{"code":45,"score":364,"supported":58,"title":365,"abstract":366,"tags":367},7,[],[74,75],[28],{"code":369,"score":111,"supported":58,"title":370,"abstract":371,"tags":374},"A8",[],[372,373],"粮食安全","food security",[],{"code":225,"score":111,"supported":58,"title":376,"abstract":377,"tags":380},[],[378,379],"精准农业","precision agriculture",[],{"code":382,"score":111,"supported":58,"title":383,"abstract":384,"tags":387},"I4",[],[385,386],"地理信息","geographic information",[],{"code":389,"score":79,"supported":58,"title":390,"abstract":391,"tags":393},"F3",[],[392],"养分管理",[],{"code":395,"score":79,"supported":58,"title":396,"abstract":397,"tags":399},"J1",[],[398],"农业政策",[],[401,402],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":404,"openalex_id":406,"authors":407,"venue":328,"cited_by_count":100,"oa_url":325,"card":423,"direction":106,"ingested_from":108},"W7222999158",[408,410,412,414,416,418,421],{"name":409,"orcid":9},"Asifa Naz",{"name":411,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":413,"orcid":9},"Syed Asghar Hussain Shah",{"name":415,"orcid":9},"Shahrish Nawaz",{"name":417,"orcid":9},"Muhmmad Irfan",{"name":419,"orcid":420},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":422,"orcid":9},"Muhammad Nouman Khalid",{"tldr":424,"method":425,"finding":426,"direction":106,"opportunity":427},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":449,"search_phrases":473,"slug":476,"view_count":22,"doi":477,"paper":478,"created_at":492},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing","2026-10-08T00:00:00Z",75,{"impact":123,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":439},"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[441],{"name":435,"url":432},[372,157,28,443,444],"西非农业","干旱指数",{"code":78,"slug":340,"name":341,"path":342,"parent_code":37},[447,448],{"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":450},{"candidates":451,"agricultural_evidence":58},[452,457,461,465,469],{"code":78,"score":17,"supported":58,"title":453,"abstract":455,"tags":456},[454],"drought",[352,454],[157,444],{"code":45,"score":57,"supported":58,"title":458,"abstract":459,"tags":460},[75],[74,75],[28],{"code":40,"score":20,"supported":58,"title":462,"abstract":463,"tags":464},[361],[67,361],[],{"code":33,"score":111,"supported":58,"title":466,"abstract":467,"tags":468},[],[61,34],[],{"code":369,"score":22,"supported":14,"title":470,"abstract":471,"tags":472},[],[],[372],[474,475],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":477,"openalex_id":479,"authors":480,"venue":435,"cited_by_count":100,"oa_url":432,"card":487,"direction":106,"ingested_from":108},"W7220963372",[481,484],{"name":482,"orcid":483},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":485,"orcid":486},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":488,"method":489,"finding":490,"direction":106,"opportunity":491},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":9,"source_name":499,"source_url":496,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":500,"score_detail":501,"sources":503,"tags":505,"primary_topic":510,"secondary_topics":511,"classification":513,"search_phrases":534,"slug":537,"view_count":100,"doi":538,"paper":539,"created_at":574},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。","Pochvovedenie i agrokhimiya",73,{"impact":123,"substance":18,"depth":19,"authority":201,"freshness":71,"relevant":22,"comment":502},"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。",[504],{"name":499,"url":496},[506,507,28,508,509],"耕地保护","农业生态","Google Earth Engine","土壤重金属",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[512],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":514},{"candidates":515,"agricultural_evidence":58},[516,520,524,529],{"code":33,"score":57,"supported":58,"title":517,"abstract":518,"tags":519},[34],[61,34],[509],{"code":45,"score":71,"supported":58,"title":521,"abstract":522,"tags":523},[],[74,75],[508,28],{"code":525,"score":22,"supported":14,"title":526,"abstract":527,"tags":528},"H1",[],[],[506],{"code":530,"score":22,"supported":14,"title":531,"abstract":532,"tags":533},"H6",[],[],[507],[535,536],"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","10.51886\u002F1999-740x_2026_3_46",{"doi":538,"openalex_id":540,"authors":541,"venue":499,"cited_by_count":100,"oa_url":567,"card":568,"direction":106,"ingested_from":108},"W7220713350",[542,545,547,550,553,556,559,562,565],{"name":543,"orcid":544},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":546,"orcid":9},"M. Altynbayeva",{"name":548,"orcid":549},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":551,"orcid":552},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":554,"orcid":555},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":557,"orcid":558},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":560,"orcid":561},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":563,"orcid":564},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":566,"orcid":9},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":569,"method":570,"finding":571,"direction":572,"opportunity":573},"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。","2026-10-07T23:30:29.146414Z"]