[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3363":3,"related-3363":61},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":60},3363,"Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02576-x","Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India。Journal of the Indian Society of Remote Sensing","基于RUSLE、遥感和情景机器学习的土壤侵蚀驱动因素综合评估——以印度东部乔塔纳格普尔高原数据稀缺流域为例。《印度遥感学会杂志》",null,"Journal of the Indian Society of Remote Sensing","2026-09-23T00:00:00Z","论文",10,false,63,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,13,9,1,"方法组合有新意但属区域案例研究，影响范围有限，可作为遥感与水土保持主题的补充素材。",[25],{"name":10,"url":6},[27,28,29,30],"机器学习","遥感监测","土壤侵蚀","水土保持",[32,33],"Chota Nagpur Plateau 土壤侵蚀","RUSLE 遥感 机器学习","ChotaNagpurPlateau土壤侵蚀-3363",0,"10.1007\u002Fs12524-026-02576-x",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":53,"direction":57,"ingested_from":59},"W7214122637",[40,43,46,48,50],{"name":41,"orcid":42},"Mukesh Kumar Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-0385-4426",{"name":44,"orcid":45},"Prabhat Kumar Guru","https:\u002F\u002Forcid.org\u002F0000-0002-9294-2091",{"name":47,"orcid":9},"Sanjeet Kumar",{"name":49,"orcid":9},"Yogesh A. Rajwade",{"name":51,"orcid":52},"Narendra Singh Chandel","https:\u002F\u002Forcid.org\u002F0000-0003-4850-4702",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素","RUSLE模型、遥感数据与情景机器学习集成分析。","在数据稀缺流域识别出土壤侵蚀关键驱动因子并预测不同情景下的侵蚀风险。","农业遥感与作物表型","可探索缺数据区多源遥感与机器学习融合的土壤侵蚀动态监测与情景预警方法。","openalex","2026-09-24T23:30:21.065678Z",{"total":62,"page":22,"page_size":62,"items":63},6,[64,95,141,186,233,280],{"id":65,"title":66,"url":67,"summary":68,"summary_zh":9,"content":9,"source_name":69,"source_url":9,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":75,"tags":77,"search_phrases":81,"slug":84,"view_count":35,"doi":9,"paper":85,"created_at":94},3248,"Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37471110","MDPI 发表精准农业作物推荐方法系统综述：从183项研究中严格筛选129篇发表于2020-2026年的文章，使用PRISMA协议进行综合分析。研究表明集成学习方法（特别是随机森林和XGBoost）在各种农业数据集的预测性能上具有强大能力；支持向量机、决策树、k近邻等传统ML方法仍被广泛使用；同时CNN和LSTM被用于遥感和时间相关农业分析。最常用的数据集来源是Kaggle，典型输入包括土壤养分（NPK）、土壤pH、天气条件和NDVI、EVI等卫星指数。研究主要研究空白：有限的实时部署、低多数据源集成、低跨区域验证、低模型可解释性。研究表明可扩展、可解释的AI系统对农业实际应用具有重要意义。","MDPI","2026-09-22T00:00:00Z",81,{"impact":18,"substance":73,"depth":18,"authority":20,"freshness":13,"relevant":22,"comment":74},22,"基于PRISMA的129篇文献系统综述，梳理作物推荐主流方法与四大研究空白，对农业AI落地有参考价值。",[76],{"name":69,"url":67},[78,79,27,80,28],"智慧农业","农业人工智能","作物推荐",[82,83],"精准农业 作物推荐 系统综述","XGBoost 随机森林 作物推荐","精准农业作物推荐系统综述-3248",{"doi":9,"openalex_id":9,"authors":86,"venue":9,"cited_by_count":35,"oa_url":9,"card":87,"direction":91,"ingested_from":93},[],{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。","PRISMA协议系统综述，分析183项研究筛选出的129篇文献。","集成学习（随机森林、XGBoost）表现最强，主要空白为实时部署、多源集成、跨区域验证与可解释性。","农业人工智能与决策模型","可探索可解释、可跨区域泛化的实时作物推荐系统，并融合多源遥感与物联网数据。","agent","2026-09-23T00:04:33.331160Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":70,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":106,"tags":108,"search_phrases":111,"slug":114,"view_count":35,"doi":115,"paper":116,"created_at":140},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports",78,{"impact":104,"substance":73,"depth":18,"authority":104,"freshness":13,"relevant":22,"comment":105},14,"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[107],{"name":101,"url":98},[78,27,109,28,110],"芒果","作物估产",[112,113],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":115,"openalex_id":117,"authors":118,"venue":101,"cited_by_count":35,"oa_url":98,"card":135,"direction":57,"ingested_from":59},"W7213920056",[119,121,124,126,128,131,133],{"name":120,"orcid":9},"V. Raju",{"name":122,"orcid":123},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":125,"orcid":9},"Dr. V. S. Thorat",{"name":127,"orcid":9},"V. T. Shinde",{"name":129,"orcid":130},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":132,"orcid":9},"Alok Shrivastava",{"name":134,"orcid":9},"A. P. Chaudhary",{"tldr":136,"method":137,"finding":138,"direction":57,"opportunity":139},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":147,"source_url":144,"published_at":148,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":149,"score_detail":150,"sources":154,"tags":156,"search_phrases":159,"slug":162,"view_count":35,"doi":163,"paper":164,"created_at":185},3073,"Evolution of soil erosion and sediment delivery modelling over six decades: paradigms, limitations and new solutions","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2717245","Modelling sediment delivery, namely the linkage between hillslope erosion and yield in river systems, is still a challenge. In this study, 1,075 publications from Elsevier's Scopus were analysed to trace the evolution from lumped to spatially distributed approaches. Results confirm that no single model type dominates, while several studies rely on simplified approaches, and data- and model-related errors remain unresolved. Artificial intelligence (AI) and remote sensing (RS) can address such limitations. However, although AI has emerged as either a complementary tool for generating new climate or land-use scenarios or a standalone framework, its adoption has not been transformative so far. Similarly, RS is increasingly used to characterise topography and land cover, but is poorly leveraged to determine other factors influencing sediment dynamics. This overview of existing and evolving methods provides useful insights to support river basin management and model selection. Overall, this study underscores the need for further integration of diverse data sources and processing methods, while future research should prioritise the trade-offs among model complexity, accuracy, and scalability.","模拟泥沙输移，即坡面侵蚀与河流系统产沙之间的联系，仍然是一项挑战。本研究分析了来自Elsevier Scopus数据库的1，075篇文献，以追溯从集总式方法到空间分布式方法的演变历程。结果证实，尚无单一模型类型占据主导地位，而若干研究依赖于简化方法，且与数据和模型相关的误差仍未得到解决。人工智能（AI）和遥感（RS）能够应对这些局限性。然而，尽管AI已作为生成新气候或土地利用情景的补充工具或独立框架出现，但其应用迄今尚未带来变革性影响。同样，RS越来越多地用于表征地形和土地覆盖，但在确定影响泥沙动力学的其他因素方面利用不足。本综述对现有及不断演变的方法进行了概述，为支持流域管理和模型选择提供了有益见解。总体而言，本研究强调了进一步整合多样化数据来源和处理方法的必要性，同时未来研究应优先考虑模型复杂性、精度和可扩展性之间的权衡。","International Journal of River Basin Management","2026-09-19T00:00:00Z",66,{"impact":17,"substance":151,"depth":152,"authority":20,"freshness":17,"relevant":22,"comment":153},20,17,"基于1075篇文献的六十年土壤侵蚀与泥沙输移建模综述，指出AI与遥感应用尚未形成变革性突破，对农业水土保持与流域管理有参考价值，但属学术综述、影响面有限。",[155],{"name":147,"url":144},[79,157,29,30,158],"农业遥感","流域管理",[160,161],"土壤侵蚀 泥沙输移 模型","遥感 AI 流域管理","土壤侵蚀泥沙输移模型-3073","10.1080\u002F15715124.2026.2717245",{"doi":163,"openalex_id":165,"authors":166,"venue":147,"cited_by_count":35,"oa_url":179,"card":180,"direction":57,"ingested_from":59},"W7213758675",[167,170,173,176],{"name":168,"orcid":169},"Melissa Latella","https:\u002F\u002Forcid.org\u002F0000-0003-3678-6992",{"name":171,"orcid":172},"Monia Santini","https:\u002F\u002Forcid.org\u002F0000-0002-8041-8241",{"name":174,"orcid":175},"Pierfranco Costabile","https:\u002F\u002Forcid.org\u002F0000-0003-1147-9929",{"name":177,"orcid":178},"Roberta Padulano","https:\u002F\u002Forcid.org\u002F0000-0003-4881-4495","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15715124.2026.2717245?needAccess=true",{"tldr":181,"method":182,"finding":183,"direction":57,"opportunity":184},"分析1075篇文献，梳理六十年土壤侵蚀与泥沙输移模型从集总到分布式的演变。","Scopus文献计量分析，综述AI与遥感在泥沙输移建模中的应用。","无单一模型占主导，AI与遥感应用尚未变革性，需整合多源数据并权衡复杂度、精度与可扩展性。","可探索AI与多源遥感深度融合的分布式泥沙输移模型，兼顾精度与可扩展性。","2026-09-21T23:30:25.637249Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":198,"tags":200,"search_phrases":204,"slug":207,"view_count":35,"doi":208,"paper":209,"created_at":232},2946,"Forecasting cocoa export volumes in Indonesia using remote sensing, climate variability, and economic indicators: A comparative modeling approach","https:\u002F\u002Fdoi.org\u002F10.1177\u002F18747655261486415","Accurate forecasting of agricultural exports is essential for supporting production planning, trade management, and evidence-based policymaking. However, forecasting cocoa exports remains challenging because export volumes are influenced by interconnected environmental, climatic, and economic factors. This study proposes an integrated forecasting framework for Indonesian cocoa export volumes by incorporating remote sensing indicators, climatic indices, and economic variables into statistical, machine learning, deep learning, and hybrid forecasting approaches. The exogenous variables consist of Enhanced Vegetation Index (EVI), Land Surface Temperature (LST), rainfall, cocoa price, Oceanic Niño Index (ONI), Dipole Mode Index (DMI), and exchange rate. The study evaluates benchmark models (Naïve, Seasonal Naïve, and ETS), statistical time-series models (ARIMAX and SARIMAX), machine learning and deep learning models (XGBoost and LSTM), and a hybrid SARIMAX–XGBoost model. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) over an independent 17-month test horizon from November 2023 to March 2025. XGBoost achieved the overall best forecasting performance, obtaining the lowest test RMSE of 2,609,038 kg, MAE of 2,096,098 kg, and MAPE of 7.56%. Its advantage over ARIMAX, SARIMAX, and the SARIMAX–XGBoost hybrid was supported by pairwise comparisons using the modified Diebold–Mariano (DM) and Kolmogorov–Smirnov Predictive Accuracy (KSPA) tests. The comparison with LSTM showed no statistically significant difference according to the DM test, although the KSPA test indicated a significant difference. These findings demonstrate that XGBoost provided the strongest out-of-sample forecasting performance among the evaluated models.","农业出口的准确预测对于支持生产规划、贸易管理和循证政策制定至关重要。然而，可可出口预测仍然具有挑战性，因为出口量受到环境、气候和经济因素相互交织的影响。本研究提出了一种印度尼西亚可可出口量的集成预测框架，将遥感指标、气候指数和经济变量纳入统计、机器学习、深度学习和混合预测方法中。外生变量包括增强植被指数（EVI）、地表温度（LST）、降雨量、可可价格、海洋尼诺指数（ONI）、偶极子模态指数（DMI）和汇率。本研究评估了基准模型（Naïve、Seasonal Naïve和ETS）、统计时间序列模型（ARIMAX和SARIMAX）、机器学习和深度学习模型（XGBoost和LSTM）以及SARIMAX–XGBoost混合模型。模型性能通过均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）在2023年11月至2025年3月的独立17个月测试期内进行评估。XGBoost取得了总体最佳的预测性能，测试RMSE最低为2,609,038 kg，MAE为2,096,098 kg，MAPE为7.56%。其相对于ARIMAX、SARIMAX和SARIMAX–XGBoost混合模型的优势得到了使用修正Diebold–Mariano（DM）检验和Kolmogorov–Smirnov预测精度（KSPA）检验的成对比较的支持。与LSTM的比较显示，根据DM检验无统计学显著差异，尽管KSPA检验表明存在显著差异。这些发现表明，在所评估的模型中，XGBoost提供了最强的样本外预测性能。","Statistical Journal of the IAOS","2026-09-18T00:00:00Z",73,{"impact":196,"substance":73,"depth":152,"authority":20,"freshness":21,"relevant":22,"comment":197},12,"将遥感、气候指数与经济变量整合进可可出口预测框架，XGBoost 表现最优，方法新颖且结论可靠，对农产品贸易信息化有参考价值。",[199],{"name":192,"url":189},[27,28,201,202,203],"可可出口","农业预测","气候指数",[205,206],"印尼 可可 出口 预测","遥感 气候 可可 出口","印尼可可出口预测-2946","10.1177\u002F18747655261486415",{"doi":208,"openalex_id":210,"authors":211,"venue":192,"cited_by_count":35,"oa_url":9,"card":227,"direction":57,"ingested_from":59},"W7213558756",[212,215,217,219,222,225],{"name":213,"orcid":214},"Erna Nurmawati","https:\u002F\u002Forcid.org\u002F0009-0002-3385-673X",{"name":216,"orcid":9},"Neli Agustina",{"name":218,"orcid":9},"Robert Kurniawan",{"name":220,"orcid":221},"Prana Ugiana Gio","https:\u002F\u002Forcid.org\u002F0000-0002-8155-005X",{"name":223,"orcid":224},"Rayhan Abyasa","https:\u002F\u002Forcid.org\u002F0009-0006-0476-7999",{"name":226,"orcid":9},"Aditya Hari Kurnia Putra",{"tldr":228,"method":229,"finding":230,"direction":91,"opportunity":231},"融合遥感、气候与经济指标，比较多种模型预测印尼可可出口量，XGBoost表现最佳。","用EVI、LST、降水、ONI、DMI、价格、汇率等变量，比较ARIMAX、SA","XGBoost预测精度最高，MAPE为7.56%，显著优于统计模型和混合模型。","可探索多源遥感与气候指数融合的混合模型，提升农产品出口预测精度与可解释性。","2026-09-19T23:30:33.176995Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":246,"tags":248,"search_phrases":252,"slug":255,"view_count":35,"doi":256,"paper":257,"created_at":279},2871,"Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1948848","Introduction Systematically clarifying the spatiotemporal evolution of vegetation coverage and elucidating its response mechanisms to climatic fluctuations and human activities carries substantial practical implications for advancing the Dual Carbon Strategy and optimizing the pattern of territorial spatial development and conservation across Shanxi Province, China. Methods Based on kernel Normalized Difference Vegetation Index (kNDVI) datasets, multi-source meteorological, topographic and socioeconomic datasets, this study integrated Theil-Sen slope estimation, Mann-Kendall significance test, Hurst exponent, standard deviational ellipse and gravity center migration model to systematically characterize the spatiotemporal patterns of vegetation coverage for the period 2000 to 2024 and predict the potential persistence of its future evolution. Meanwhile, an interpretable XGBoost-SHAP machine learning framework was constructed to quantify the independent contributions, nonlinear marginal responses, and temporal evolutionary characteristics of seven driving factors: elevation, slope, aspect, annual mean temperature, annual mean precipitation, population density and nighttime light intensity. Results (1) Temporally, the multi-year average kNDVI of the whole province reached approximately 0.1699, presenting an extremely significant fluctuating upward trend with an annual growth rate of 0.0041. Annual kNDVI ranges from 0.1132 recorded in 2001 to a peak value of 0.2227 in 2024. (2) Spatially, vegetation coverage exhibited a remarkable differentiation pattern of “low values in the north and high values in the south”. Higher kNDVI values were predominantly distributed within the forest-covered mountainous regions of the Taihang, Lüliang, Zhongtiao, and Taiyue Mountains, whereas relatively low values were distributed across the sandy-hilly regions of northern Shanxi and urban agglomerations along the Fen River Valley. Vegetation restoration was observed across 98.02% of the study area, among which roughly 92.18% exhibited an extremely significant improvement. (3) Over 2000–2024, the major axis of vegetation spatial distribution maintained a stable northeast-southwest orientation, accompanied by moderate outward expansion of the standard deviational ellipse, and a general northwestward shift of the vegetation gravity centre. (4) The Hurst exponent indicated that the vegetation improvement trend in 98.03% of the region would persist, while scattered patches of industrial, mining and urban land (accounting for 1.97%) faced the potential vegetation degradation risk. (5) The regional vegetation driving system experienced three evolutionary stages: a single precipitation-dominated natural driving stage (2000–2005), a climate-human coupled transitional stage (2005–2010), and a multi-factor synergistic balanced stage (2010–2020). Precipitation and temperature acted as core climatic drivers, and elevation largely determined the vertical differentiation baseline of vegetation distribution. Population density and nighttime light intensity exerted persistent suppressive effects on vegetation growth. Empirically derived tentative thresholds are identified for major predictors: elevation ∼1,200 m, slope ∼8°, annual mean temperature ∼7.5 °C, annual mean precipitation ∼500 mm, population density 500 persons\u002Fkm 2 , and nighttime light intensity ∼5. These values can serve as reference boundaries for differentiating ecological conservation zones from human-disturbed zones. Conclusion This research identified the intertemporal differentiation and nonlinear coupling laws governing vegetation dynamics in Shanxi Province, a temperate transition zone on the Loess Plateau. Differentiated ecological governance strategies targeting climate adaptation, topographic zoning, and anthropogenic-pressure regulation are proposed, which provide observational scientific support for the construction of ecological security barriers on China’s Loess Plateau.","引言 系统厘清植被覆盖的时空演变规律并阐明其对气候波动与人类活动的响应机制，对推进双碳战略、优化山西省国土空间开发保护格局具有重要现实意义。方法 基于核归一化植被指数（kNDVI）数据集及多源气象、地形和社会经济数据，本研究综合运用Theil-Sen斜率估计、Mann-Kendall显著性检验、Hurst指数、标准差椭圆和重心迁移模型，系统刻画了2000—2024年植被覆盖的时空格局，并预测其未来演变的潜在持续性。同时，构建了可解释的XGBoost-SHAP机器学习框架，量化了高程、坡度、坡向、年平均气温、年平均降水量、人口密度和夜间灯光强度7个驱动因子的独立贡献、非线性边际响应及时间演变特征。结果 （1）时间上，全省多年平均kNDVI约为0.1699，呈极显著波动上升趋势，年增长率为0.0041。年kNDVI从2001年的0.1132变化至2024年的峰值0.2227。（2）空间上，植被覆盖呈现“北低南高”的显著分异格局。较高kNDVI值主要分布于太行山、吕梁山、中条山和太岳山等森林覆盖山区，而较低值分布于晋北沙丘丘陵区和汾河谷地城市群。研究区98.02%的区域植被呈恢复态势，其中约92.18%表现为极显著改善。（3）2000—2024年间，植被空间分布的主轴保持稳定的东北—西南走向，标准差椭圆呈中度向外扩张，植被重心总体向西北方向迁移。（4）Hurst指数表明，98.03%区域的植被改善趋势将持续，而零星斑块区域的工业","Frontiers in Environmental Science","2026-09-17T00:00:00Z",82,{"impact":18,"substance":243,"depth":244,"authority":20,"freshness":21,"relevant":22,"comment":245},23,19,"基于kNDVI与XGBoost-SHAP的山西植被时空演变与驱动力研究，方法新颖、数据扎实，对黄土高原生态治理有参考价值。",[247],{"name":239,"url":236},[27,249,28,250,251],"生态保护","植被覆盖","黄土高原",[253,254],"山西 kNDVI 植被覆盖","XGBoost-SHAP 植被驱动","山西kNDVI植被覆盖-2871","10.3389\u002Ffenvs.2026.1948848",{"doi":256,"openalex_id":258,"authors":259,"venue":239,"cited_by_count":35,"oa_url":273,"card":274,"direction":57,"ingested_from":59},"W7213469060",[260,262,264,266,268,271],{"name":261,"orcid":9},"Jie Chen",{"name":263,"orcid":9},"Yi Hou",{"name":265,"orcid":9},"Jianhua Xue",{"name":267,"orcid":9},"Jianhua Ni",{"name":269,"orcid":270},"Hao Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8903-0983",{"name":272,"orcid":9},"Pengxiang Gao","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1948848\u002Fpdf",{"tldr":275,"method":276,"finding":277,"direction":57,"opportunity":278},"基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。","kNDVI数据结合Theil-Sen、Mann-Kendall、Hurst指数与","山西植被呈显著上升趋势，98.02%区域改善，驱动因子具非线性与时空差异。","可引入多源遥感与作物物候数据，将kNDVI驱动分析拓展至农田尺度精准管理与碳汇评估。","2026-09-18T23:30:29.212735Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":10,"source_url":283,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":289,"tags":291,"search_phrases":294,"slug":297,"view_count":35,"doi":298,"paper":299,"created_at":322},2804,"A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02584-x","A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning。Journal of the Indian Society of Remote Sensing","一种基于NASA FIRMS和机器学习的新型数据驱动秸秆焚烧检测框架。《印度遥感学会杂志》",76,{"impact":19,"substance":151,"depth":18,"authority":20,"freshness":13,"relevant":22,"comment":288},"该论文提出基于NASA FIRMS卫星数据与机器学习的秸秆焚烧检测新框架，方法新颖、数据规模明确，对农业遥感监测有实质参考价值，但属细分领域学术进展，影响力限于专业圈层。",[290],{"name":10,"url":283},[79,27,28,292,293],"秸秆焚烧","卫星数据",[295,296],"农业人工智能 卫星数据 机器学习 秸秆焚烧","农业人工智能 卫星数据","农业人工智能卫星数据机器学习秸秆焚烧-2804","10.1007\u002Fs12524-026-02584-x",{"doi":298,"openalex_id":300,"authors":301,"venue":10,"cited_by_count":35,"oa_url":9,"card":317,"direction":91,"ingested_from":59},"W7213455429",[302,305,307,310,312,315],{"name":303,"orcid":304},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":306,"orcid":9},"Oshin Rastogi",{"name":308,"orcid":309},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":311,"orcid":9},"Raviya",{"name":313,"orcid":314},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":316,"orcid":9},"Shelza Dua",{"tldr":318,"method":319,"finding":320,"direction":57,"opportunity":321},"提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。","使用NASA FIRMS火点数据结合机器学习分类算法。","该框架能有效识别秸秆焚烧事件，提升检测精度。","可结合多源遥感与深度学习，提升小尺度焚烧检测与实时预警能力。","2026-09-17T23:30:59.313408Z"]