[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1641":3,"related-1641":97},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":29,"classification":30,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":96},1641,"MANIFESTATION OF DESERTIFICATION PROCESSES OF SANDY AND SANDLIFE SOILS IN THE RIGHT-BANK FOREST-STEP OF UKRAINE DEPENDING ON THEIR MOISTURE LEVEL","https:\u002F\u002Fdoi.org\u002F10.37128\u002F2707-5826-2026-3-8","Key factors contributing to the intensification of aridification and desertification processes include climatic conditions, including insufficient soil moisture, uneven distribution of precipitation, and increased evaporation. These processes can be significantly exacerbated by non-compliance with crop rotation, excessive use of pesticides, intensive tillage, and other negative impacts. In our study, the main attention was paid to test polygons - areas of soil where the vegetation cover was partially or completely absent, or significantly thinned in the late spring period due to overdrying, which caused manifestations of desertification. The selection of plots was carried out on the basis of remote sensing data of the territory of Vinnytsia district, which is within the Vinnytsia region of Ukraine. The coordinates of the identified points were used for the analysis together with cartograms of the soil cover, which allowed us to distinguish three main types of soils: sod-podzolic, gray podzolic and podzolic chernozem. Each of these types was represented by variants with sandy and sandy loam mechanical composition. Soil moisture was determined on the selected test polygons using the laboratory gravimetric method. The processes of desertification of sandy and sandy loam soils largely depend on their moisture level at different periods of time, which, in turn, is determined by the amount of precipitation. The minimum level of moisture available to plants in sandy soils should be more than 5%, and in sandy loams - not less than 8%. In the spring (April), due to moisture deficiency, sod-podzolic and gray podzolic sandy and sandy loam soils could not provide a sufficient supply of moisture for the germination of seeds of agricultural crops under the existing weather conditions. This led to the fact that such soils began to approach the state of the desert. In July, with the arrival of significant precipitation, there was a sharp increase in the moisture level, which contributed to the active germination of weeds in areas where cultivated plants were not planted. In the fall, all studied soils showed signs of moisture saturation, which improved conditions for the germination of winter crops, but at the same time delayed the timing of field sowing due to excessive moisture.","导致干旱化和荒漠化过程加剧的关键因素包括气候条件，如土壤水分不足、降水分布不均以及蒸发量增加。这些过程可能因轮作不规范、过度使用农药、集约耕作及其他负面影响而显著恶化。在本研究中，主要关注测试样地——即由于过度干燥而在晚春时期植被覆盖部分或完全缺失、或显著稀疏的土壤区域，这些现象引发了荒漠化的表现。样地的选取基于乌克兰文尼察州文尼察区领土的遥感数据。识别点的坐标与土壤覆盖图结合进行分析，从而区分出三种主要土壤类型：生草灰化土、灰色灰化土和灰化黑钙土。每种类型均以砂质和砂壤质机械组成的变体为代表。在选定的测试样地上，采用实验室重量法测定土壤湿度。砂质和砂壤质土壤的荒漠化过程在很大程度上取决于其在不同时期的水分水平，而这又由降水量决定。砂质土壤中可供植物利用的最低水分含量应超过5%，砂壤质土壤则不低于8%。春季（四月），由于水分亏缺，生草灰化土和灰色灰化土的砂质及砂壤质土壤在现有天气条件下无法为农作物种子萌发提供充足的水分供应。这导致此类土壤开始接近荒漠状态。七月，随着大量降水的到来，水分水平急剧上升，促进了未种植栽培植物区域杂草的活跃萌发。秋季，所有研究的土壤均表现出水分饱和的迹象，这改善了冬季作物的萌发条件，但同时也因水分过多而推迟了田间播种的时间。",null,"Agriculture and Forestry","2026-09-03T00:00:00Z","论文",10,false,52,{"impact":17,"substance":18,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},8,15,12,2,1,"研究乌克兰沙质土壤荒漠化与水分关系，对干旱监测有参考价值，但地域性强，时效性低。",[24],{"name":10,"url":6},[26,27,28],"干旱监测","土壤退化","遥感应用",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":34},"needs_review","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":39,"reason":73},[36,45,52,59,66],{"code":37,"score":38,"supported":39,"title":40,"abstract":41,"tags":44},"A7",6,true,[],[42,43],"轮作","crop rotation",[],{"code":46,"score":38,"supported":39,"title":47,"abstract":48,"tags":51},"H2",[],[49,50],"土壤","soil",[],{"code":53,"score":38,"supported":39,"title":54,"abstract":55,"tags":58},"I3",[],[56,57],"遥感","remote sensing",[],{"code":60,"score":61,"supported":39,"title":62,"abstract":63,"tags":65},"H7",4,[],[64],"干旱",[26],{"code":67,"score":68,"supported":39,"title":69,"abstract":70,"tags":72},"F4",3,[],[71],"农药",[],"多个产业证据相当，需要确认主归属",[75,76],"土壤退化 干旱监测 遥感应用","土壤退化 干旱监测","土壤退化干旱监测遥感应用-1641",0,"10.37128\u002F2707-5826-2026-3-8",{"doi":79,"openalex_id":81,"authors":82,"venue":10,"cited_by_count":78,"oa_url":6,"card":89,"direction":93,"ingested_from":95},"W7207811626",[83,86],{"name":84,"orcid":85},"Олександр Ткачук","https:\u002F\u002Forcid.org\u002F0000-0002-0647-6662",{"name":87,"orcid":88},"V. M. Kuzemsky","https:\u002F\u002Forcid.org\u002F0009-0001-5465-6932",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"研究乌克兰右岸森林草原沙质和沙壤土荒漠化过程与土壤湿度的关系。","利用遥感数据选点，结合土壤图分类，实验室称重法测土壤湿度。","沙土湿度低于5%、沙壤土低于8%时，作物难以发芽，导致荒漠化。","农业遥感与作物表型","可延伸研究基于遥感监测土壤湿度预警荒漠化，并开发精准灌溉决策模型。","openalex","2026-09-04T23:30:32.391898Z",{"total":38,"page":21,"page_size":38,"items":98},[99,164,240,307,375,447],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":111,"tags":113,"primary_topic":9,"secondary_topics":118,"classification":119,"search_phrases":142,"slug":145,"view_count":78,"doi":146,"paper":147,"created_at":163},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",61,{"impact":17,"substance":109,"depth":18,"authority":19,"freshness":17,"relevant":21,"comment":110},18,"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[112],{"name":105,"url":102},[114,115,26,116,117],"NDVI","遥感监测","NDMI","LST",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":120},{"candidates":121,"agricultural_evidence":14,"reason":141},[122,127,131,135],{"code":60,"score":18,"supported":39,"title":123,"abstract":125,"tags":126},[124],"drought",[64,124],[26],{"code":53,"score":17,"supported":39,"title":128,"abstract":129,"tags":130},[],[56,57],[114,115],{"code":46,"score":38,"supported":39,"title":132,"abstract":133,"tags":134},[],[49,50],[],{"code":136,"score":68,"supported":39,"title":137,"abstract":138,"tags":140},"H3",[],[139],"水资源",[],"农业相关性需要复核",[143,144],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":146,"openalex_id":148,"authors":149,"venue":105,"cited_by_count":78,"oa_url":156,"card":157,"direction":162,"ingested_from":95},"W7222954725",[150,153],{"name":151,"orcid":152},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":154,"orcid":155},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":158,"method":159,"finding":160,"direction":93,"opportunity":161},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"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":178,"tags":180,"primary_topic":184,"secondary_topics":190,"classification":196,"search_phrases":216,"slug":219,"view_count":78,"doi":220,"paper":221,"created_at":239},5320,"Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization","https:\u002F\u002Fdoi.org\u002F10.17485\u002Fijst\u002Fv19i36.1263","Background: Machine learning is increasingly applied to drought monitoring and forecasting, but studies differ widely in how drought is defined, what data are used, and how models are validated, which makes reported performance difficult to compare directly. Objectives: To explain why reported machine-learning drought-model performance varies across definitions, data sources and validation schemes, and to assess whether recent studies adequately address rare severe events and cross-domain transferability. Method: Journal articles published January 2018 to August 2026 were reviewed, starting from a 70-record high-relevance subset of a 359-record discovery export and extended through citation chaining. Publisher and DOI verification produced a final evidence base of 55 articles: 48 primary studies and 7 reviews. Target definition, input data, model family, validation design, metrics, class-imbalance treatment, explainability and external testing were compared across studies. Findings: Reported performance varied with the modelling task rather than with any single algorithm: Random Forest reached 97.7% AUC-ROC for agricultural-drought mapping in Queensland, SVM reached average R²=0.90 for hydrological drought in Algeria, and a CatBoost stacking model reached R²=0.9065 for remote-sensing-based drought monitoring. These scores are not directly comparable because targets, data and validation protocols differ. Recent studies increasingly test cross-product and cross-region transfer, use SHAP\u002FLIME explainability, and adopt extreme-aware losses, yet class-wise reporting of severe and extreme drought, ordinal-aware evaluation, and leakage-resistant temporal validation remain inconsistently applied across the 48 primary studies. Novelty: The review jointly evaluates predictive accuracy alongside rare-event sensitivity, ordinal drought severity, leakage-resistant validation, explainability, uncertainty and cross-domain generalization, criteria largely absent from earlier machine-learning drought reviews. Keywords: Drought Forecasting, Machine Learning, Remote Sensing, Explainable AI, Extreme Drought","背景：机器学习正日益应用于干旱监测与预报，但各研究在干旱定义、所用数据和模型验证方式上差异很大，导致所报告的模型性能难以直接比较。目标：解释为何已报告的机器学习干旱模型性能会因定义、数据来源和验证方案的不同而变化，并评估近期研究是否充分处理了罕见的严重事件和跨域可迁移性问题。方法：综述了2018年1月至2026年8月发表的期刊论文，起始于359条发现导出记录中70条高相关性记录的子集，并通过引文追溯加以扩展。经出版商和DOI核验，最终证据基础为55篇文章：48篇原始研究和7篇综述。比较了各研究的目标定义、输入数据、模型族、验证设计、指标、类别不平衡处理、可解释性和外部测试。发现：所报告的性能随建模任务而非任何单一算法而变化：随机森林在昆士兰农业干旱制图中达到97.7%的AUC-ROC，SVM在阿尔及利亚水文干旱中达到平均R²=0.90，CatBoost堆叠模型在基于遥感的干旱监测中达到R²=0.9065。这些分数不能直接比较，因为目标、数据和验证方案不同。近期研究越来越多地测试跨产品和跨区域迁移，使用SHAP\u002FLIME可解释性，并采用极端事件感知损失函数，然而在48篇原始研究中，严重和极端干旱的分类别报告、序数感知评估以及防泄漏的时间验证仍应用不一致。新颖性：本综述将预测准确性与罕见事件敏感性、序数干旱严重程度、防泄漏验证、可解释性、不确定性和跨域泛化等标准共同评估，这些标准在以往的机器学习干旱综述中大多缺失。关键词：干旱预报，机器学习，遥感，可解释人工智能，极端干旱","Indian Journal of Science and Technology","2026-10-18T00:00:00Z",71,{"impact":19,"substance":174,"depth":175,"authority":19,"freshness":176,"relevant":21,"comment":177},21,17,9,"系统综述机器学习干旱分析与预测，指出评估口径不一、极端事件敏感性与可解释性不足，对农业旱情监测信息化有参考价值，但属学术综述、影响面偏细分领域。",[179],{"name":170,"url":167},[181,182,183,56,26],"农业人工智能","可解释AI","极端天气",{"code":185,"slug":186,"name":187,"path":188,"parent_code":189},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[191],{"code":60,"slug":192,"name":193,"path":194,"parent_code":195},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":197,"locked":14,"version":32,"method":33,"evidence":198},"classified",{"candidates":199,"agricultural_evidence":39},[200,207,211],{"code":185,"score":201,"supported":39,"title":202,"abstract":204,"tags":206},16,[203],"machine learning",[205,203],"机器学习",[181,182],{"code":60,"score":18,"supported":39,"title":208,"abstract":209,"tags":210},[124],[64,124],[26],{"code":53,"score":212,"supported":39,"title":213,"abstract":214,"tags":215},7,[],[56,57],[56],[217,218],"机器学习 干旱预测","遥感 干旱监测","机器学习干旱预测-5320","10.17485\u002Fijst\u002Fv19i36.1263",{"doi":220,"openalex_id":222,"authors":223,"venue":170,"cited_by_count":78,"oa_url":167,"card":233,"direction":93,"ingested_from":95},"W7222882764",[224,227,230],{"name":225,"orcid":226},"Ritik Mallik","https:\u002F\u002Forcid.org\u002F0009-0000-2785-0265",{"name":228,"orcid":229},"Dayal Kumar Behera","https:\u002F\u002Forcid.org\u002F0000-0002-9740-8066",{"name":231,"orcid":232},"Satarupa Mohanty","https:\u002F\u002Forcid.org\u002F0000-0002-1434-7158",{"tldr":234,"method":235,"finding":236,"direction":237,"opportunity":238},"综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。","系统综述2018-2026年55篇文献，比较目标定义、数据、模型、验证与可解释性","模型性能差异主要源于任务与验证设计而非算法，极端事件与泄漏防护评估仍不足。","农业人工智能与决策模型","可构建面向极端干旱的序数感知评估与抗泄漏时序验证框架，提升跨区域泛化。","2026-10-19T23:30:22.840943Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":246,"source_url":243,"published_at":247,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":253,"tags":255,"primary_topic":9,"secondary_topics":258,"classification":259,"search_phrases":277,"slug":280,"view_count":21,"doi":281,"paper":282,"created_at":306},5012,"Evaluating Multi-Index Remote Sensing Frameworks for Drought Monitoring in Seasonally Dry Dipterocarp Deciduous Forests: A Case Study of Yok Don National Park, Vietnam","https:\u002F\u002Fdoi.org\u002F10.5755\u002Fj01.erem.82.3.44241","This study examined the effectiveness of the normalized difference vegetation index (NDVI), land surface temperature (LST), and their integrated indices in capturing dry-season drought dynamics in the dipterocarp deciduous forest ecosystems of Yok Don National Park from 2014 to 2024. Results indicate that individual remote-sensing indices captured drought processes but reflected different dimensions of drought conditions, with NDVI indicating canopy greenness and LST detecting early thermal stress. Integrated indices partially mitigated the limitations of single indicators during confirmed drought years by showing stronger correlations with reference data. The effectiveness of the integrated indices varied significantly depending on the integration framework. The spatial-normalization–based integration of NDVI and LST showed consistently strong correlations with surface evapotranspiration (ET), with correlation coefficients of approximately 0.8 across years, and was less sensitive to land-cover heterogeneity. In contrast, the triangular NDVI–LST space integration exhibited variable relationships with ET due to its sensitivity to edge definition and phenological shifts in dipterocarp deciduous forests. Phase-dependent behavior was observed, with integrated indices maintaining more stable relationships with ET during drying periods, while NDVI showed greater sensitivity under wetter conditions. The results also indicate that VHI demonstrated a more robust and stable performance than TVDI, suggesting its suitability as a primary indicator in heterogeneous forest environments. These findings provide practical guidance for drought monitoring in heterogeneous forest landscapes by identifying adaptive integration strategies tailored to different hydroclimatic conditions.","本研究考察了归一化差异植被指数（NDVI）、地表温度（LST）及其综合指数在捕捉2014至2024年 Yok Don 国家公园龙脑香落叶林生态系统旱季干旱动态方面的有效性。结果表明，单一遥感指数能够捕捉干旱过程，但反映的是干旱状况的不同维度，其中NDVI指示冠层绿度，LST探测早期热胁迫。综合指数在已确认的干旱年份中通过与参考数据表现出更强的相关性，部分缓解了单一指标的局限性。综合指数的有效性因集成框架的不同而差异显著。基于空间标准化的NDVI与LST集成与地表蒸散发（ET）表现出持续较强的相关性，各年份相关系数约为0.8，且对土地覆盖异质性较不敏感。相比之下，三角NDVI–LST空间集成与ET的关系多变，这是由于其边缘定义及龙脑香落叶林物候变化较为敏感。研究观察到相位依赖行为，综合指数在干旱期与ET保持更稳定的关系，而NDVI在较湿润条件下表现出更高的敏感性。结果还表明，VHI比TVDI表现出更稳健和稳定的性能，表明其适合作为异质森林环境中的主要指标。这些发现通过识别适应不同水文气候条件的自适应集成策略，为异质森林景观的干旱监测提供了实践指导。","Environmental Research Engineering and Management","2026-10-13T00:00:00Z",67,{"impact":17,"substance":250,"depth":175,"authority":251,"freshness":176,"relevant":21,"comment":252},20,13,"越南干旱区遥感监测方法学研究，方法新颖数据扎实，对农业遥感干旱监测有参考价值，但属区域性案例，公共影响有限。",[254],{"name":246,"url":243},[256,114,115,26,257],"农业遥感","森林生态",[],{"status":31,"locked":14,"version":32,"method":33,"evidence":260},{"candidates":261,"agricultural_evidence":14,"reason":141},[262,266,271],{"code":60,"score":18,"supported":39,"title":263,"abstract":264,"tags":265},[124],[64,124],[26],{"code":53,"score":267,"supported":39,"title":268,"abstract":269,"tags":270},14,[57],[56],[114,256,115],{"code":136,"score":38,"supported":39,"title":272,"abstract":273,"tags":276},[],[274,275],"蒸散发","evapotranspiration",[],[278,279],"Yok Don 国家公园 遥感 干旱","NDVI LST 干旱监测 森林","YokDon国家公园遥感干旱-5012","10.5755\u002Fj01.erem.82.3.44241",{"doi":281,"openalex_id":283,"authors":284,"venue":246,"cited_by_count":78,"oa_url":300,"card":301,"direction":93,"ingested_from":95},"W7221717828",[285,288,291,293,295,297],{"name":286,"orcid":287},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":289,"orcid":290},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":292,"orcid":9},"Hung Manh Nguyen",{"name":294,"orcid":9},"Huy Xuan Chu",{"name":296,"orcid":9},"Hoa Thanh Nguyen",{"name":298,"orcid":299},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":302,"method":303,"finding":304,"direction":93,"opportunity":305},"评估NDVI、LST及其集成指数在越南干旱季风落叶林干旱监测中的效果。","利用2014-2024年NDVI、LST遥感数据，构建空间归一化和三角空间集成指","空间归一化集成指数与ET相关性稳定（约0.8），VHI比TVDI更稳健，集成效果依赖框架。","可探索自适应集成框架在不同水热条件和异质景观下的干旱监测普适性。","2026-10-15T23:30:32.336137Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":9,"content":9,"source_name":312,"source_url":310,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":329,"classification":331,"search_phrases":351,"slug":354,"view_count":20,"doi":355,"paper":356,"created_at":374},4650,"A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105011","A process-oriented framework for daily dynamic monitoring of summer maize drought: From meteorological stress to agricultural impact。Agricultural Systems","Agricultural Systems","2026-10-11T00:00:00Z",81,{"impact":109,"substance":174,"depth":109,"authority":267,"freshness":13,"relevant":21,"comment":316},"核心期刊提出的面向过程的夏玉米干旱逐日动态监测框架，方法新颖、面向农业信息化应用，时效性强，值得进入每日精选。",[318],{"name":312,"url":310},[320,256,321,322,26],"智慧农业","夏玉米","农业气象",{"code":324,"slug":325,"name":326,"path":327,"parent_code":328},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[330],{"code":60,"slug":192,"name":193,"path":194,"parent_code":195},{"status":197,"locked":14,"version":32,"method":33,"evidence":332},{"candidates":333,"agricultural_evidence":39},[334,338,342,347],{"code":60,"score":251,"supported":39,"title":335,"abstract":336,"tags":337},[124],[124],[322,26],{"code":324,"score":19,"supported":39,"title":339,"abstract":340,"tags":341},[325],[325],[321],{"code":343,"score":21,"supported":14,"title":344,"abstract":345,"tags":346},"I1",[],[],[320],{"code":53,"score":21,"supported":14,"title":348,"abstract":349,"tags":350},[],[],[256],[352,353],"夏玉米 干旱 动态监测","气象胁迫 农业影响 监测框架","夏玉米干旱动态监测-4650","10.1016\u002Fj.agsy.2026.105011",{"doi":355,"openalex_id":357,"authors":358,"venue":312,"cited_by_count":78,"oa_url":310,"card":9,"direction":93,"ingested_from":95},"W7221308782",[359,361,363,365,368,370,372],{"name":360,"orcid":9},"Yuanda Zhang",{"name":362,"orcid":9},"Peijuan Wang",{"name":364,"orcid":9},"Jun Zhu",{"name":366,"orcid":367},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0001-9021-0201",{"name":369,"orcid":9},"Dianchen Han",{"name":371,"orcid":9},"Dingrong Wu",{"name":373,"orcid":9},"Jianping Guo","2026-10-11T23:30:03.734429Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":9,"source_name":381,"source_url":378,"published_at":382,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":396,"classification":397,"search_phrases":428,"slug":431,"view_count":21,"doi":432,"paper":433,"created_at":446},4506,"Spatial assessment of soil degradation under intensive agricultural pressure in the central loess plateau","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1940953","Soil degradation (SD) threatens the sustainability of semi-arid agricultural systems in the Central Loess Plateau, where intensive cultivation and repeated land-use cycles accelerate soil deterioration. This study developed a spatially explicit and uncertainty-aware framework for assessing and mapping SD by integrating soil quality indicators (SQIs), environmental data, remote sensing, and Random Forest (RF) modelling. A total of 350 surface-soil samples were collected and analyzed. Principal component analysis and the minimum data set (MDS) approach were used to identify representative soil-quality indicators. The final MDS included clay, soil bulk density, pH, soil organic carbon, calcium carbonate equivalent, cation exchange capacity, potassium, and sodium adsorption ratio. Among these indicators, pH, SAR, CEC, SOC, K, and clay received the greatest weights, highlighting the importance of soil chemical conditions, sodicity, nutrient retention, organic matter, and texture in soil quality assessment. Soil degradation was quantified by comparing SQI values of agricultural soils with reference values from natural ecosystems. Environmental predictors were then incorporated into an RF model to estimate the spatial distribution of SD and assess predictor importance. The model showed good predictive performance, with R 2 values of 0.83 for training and 0.76 for validation, and RMSE values ranging from 0.031 to 0.034. Severe degradation was mainly concentrated in the central and southwestern regions, reflecting the cumulative effects of intensive cultivation, nutrient depletion, and SOC loss. Prediction-interval analysis identified areas with greater uncertainty and priority for additional sampling. The proposed framework provides a practical basis for spatially targeted soil conservation, sustainable agricultural management, and long-term ecosystem resilience. Future research should incorporate biological indicators and temporal monitoring to improve SD prediction under changing climatic and land-use conditions.","土壤退化（SD）威胁着黄土高原中部半干旱农业系统的可持续性，该地区集约化耕作和反复的土地利用周期加速了土壤恶化。本研究通过整合土壤质量指标（SQIs）、环境数据、遥感技术和随机森林（RF）建模，开发了一个空间明确且具有不确定性感知的土壤退化评估与制图框架。共采集并分析了350个表层土壤样品。采用主成分分析和最小数据集（MDS）方法识别代表性土壤质量指标。最终的最小数据集包括黏粒、土壤容重、pH、土壤有机碳、碳酸钙当量、阳离子交换量、钾和钠吸附比。在这些指标中，pH、SAR、CEC、SOC、K和黏粒获得了最大的权重，凸显了土壤化学条件、钠质化、养分保持、有机质和质地在土壤质量评估中的重要性。通过比较农业土壤的SQI值与自然生态系统的参考值，对土壤退化进行了量化。随后将环境预测因子纳入随机森林模型，以估算土壤退化的空间分布并评估预测因子的重要性。该模型表现出良好的预测性能，训练集R²值为0.83，验证集R²值为0.76，RMSE值在0.031至0.034之间。严重退化主要集中在中部和西南部地区，反映了集约化耕作、养分耗竭和有机碳损失的累积效应。预测区间分析识别了不确定性较大且需要优先补充采样的区域。所提出的框架为空间针对性的土壤保护、可持续农业管理和长期生态系统恢复力提供了实践基础。未来研究应纳入生物指标和时间监测，以改进气候变化和土地利用变化条件下的土壤退化预测。","Frontiers in Environmental Science","2026-10-08T00:00:00Z",76,{"impact":18,"substance":385,"depth":109,"authority":251,"freshness":17,"relevant":21,"comment":386},22,"基于350个样点与随机森林模型的空间化土壤退化评估，方法扎实、结论可靠，对黄土高原耕地保护有参考价值，但属区域学术研究，公共影响有限。",[388],{"name":381,"url":378},[390,391,115,27,392],"随机森林","耕地保护","黄土高原",{"code":46,"slug":50,"name":394,"path":395,"parent_code":195},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":197,"locked":14,"version":32,"method":33,"evidence":398},{"candidates":399,"agricultural_evidence":39},[400,404,408,413,419,424],{"code":46,"score":267,"supported":39,"title":401,"abstract":402,"tags":403},[50],[49,50],[],{"code":53,"score":212,"supported":39,"title":405,"abstract":406,"tags":407},[],[56,57],[115],{"code":60,"score":38,"supported":39,"title":409,"abstract":410,"tags":412},[],[411,64],"气候变化",[],{"code":414,"score":68,"supported":39,"title":415,"abstract":416,"tags":418},"H8",[],[417],"可持续农业",[],{"code":420,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},"H1",[],[],[391],{"code":185,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[390],[429,430],"黄土高原 土壤退化 遥感","土壤质量 随机森林 评估","黄土高原土壤退化遥感-4506","10.3389\u002Ffenvs.2026.1940953",{"doi":432,"openalex_id":434,"authors":435,"venue":381,"cited_by_count":78,"oa_url":378,"card":441,"direction":93,"ingested_from":95},"W7221006056",[436,438],{"name":437,"orcid":9},"Xiandong Hou",{"name":439,"orcid":440},"Tao Bai","https:\u002F\u002Forcid.org\u002F0000-0002-2034-8026",{"tldr":442,"method":443,"finding":444,"direction":93,"opportunity":445},"构建融合土壤指标、遥感与随机森林的空间显式框架，评估黄土高原中部高强度农业下的土壤退化。","350个表层土样，PCA与最小数据集筛选指标，随机森林建模并做预测区间分析。","pH、SAR、CEC、SOC、钾和黏粒权重最高；严重退化集中于中部和西南部。","可引入生物指标与多时相遥感监测，提升气候变化和土地利用变化下土壤退化预测的时空精度。","2026-10-09T23:30:25.996370Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":465,"classification":474,"search_phrases":496,"slug":499,"view_count":68,"doi":500,"paper":501,"created_at":518},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":201,"substance":385,"depth":109,"authority":267,"freshness":176,"relevant":21,"comment":457},"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。",[459],{"name":453,"url":450},[461,115,27,462,463],"Sentinel-2","盐渍化","土地退化评估",{"code":46,"slug":50,"name":394,"path":395,"parent_code":195},[466,470],{"code":136,"slug":467,"name":468,"path":469,"parent_code":195},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"code":53,"slug":471,"name":472,"path":473,"parent_code":189},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":197,"locked":14,"version":32,"method":33,"evidence":475},{"candidates":476,"agricultural_evidence":39},[477,481,487,491],{"code":46,"score":18,"supported":39,"title":478,"abstract":479,"tags":480},[50],[49,50],[462],{"code":136,"score":176,"supported":39,"title":482,"abstract":483,"tags":486},[],[484,139,485],"灌溉","water resource",[],{"code":53,"score":17,"supported":39,"title":488,"abstract":489,"tags":490},[],[56,57],[461,115],{"code":60,"score":68,"supported":39,"title":492,"abstract":493,"tags":495},[],[494],"气候适应",[],[497,498],"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":500,"openalex_id":502,"authors":503,"venue":453,"cited_by_count":78,"oa_url":512,"card":513,"direction":93,"ingested_from":95},"W7214801255",[504,507,510],{"name":505,"orcid":506},"Z. Z. Shamsiev","https:\u002F\u002Forcid.org\u002F0000-0002-0323-9741",{"name":508,"orcid":509},"L. F. Sulyukova","https:\u002F\u002Forcid.org\u002F0000-0002-7150-4359",{"name":511,"orcid":9},"Rasul Shamsiev","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71433-1_reference.pdf",{"tldr":514,"method":515,"finding":516,"direction":93,"opportunity":517},"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"]