[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2681":3,"related-2681":89},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":21,"doi":67,"paper":68,"created_at":88},2681,"Forest encroachment prediction using multi-temporal satellite data and machine learning: a case study of Bandipur National Park, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70597-0","Abstract Forest encroachment poses a significant threat to protected forest ecosystems due to increasing human activities, including infrastructure development, agricultural expansion, and settlement growth. Continuous monitoring is therefore essential for effective conservation planning and sustainable forest management. This study presents an artificial intelligence-based framework for forest encroachment prediction in Bandipur National Park, India, using multi-temporal Sentinel- 2 satellite imagery integrated with topographic, land-cover, and anthropogenic variables. Multi temporal Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) were combined with elevation, slope, aspect, forest and water masks, and distance-to-road and distance-to-settlement variables. Three predictive models, namely Random Forest (RF), MultiLayer Perceptron (MLP), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture, were comparatively evaluated. RF achieved the highest classification accuracy of 94.17%, followed by MLP at 91.50% and CNN–LSTM at 78.69%. Future projections for 2026 and 2030 indicated relatively limited encroachment under RF and MLP, with maximum projected areas of 1.27 km² (0.15%) and 0.64 km² (0.07%), respectively. In contrast, CNN–LSTM projected substantially larger encroached areas of 115.76 km² (13.24%) in 2026 and 71.94 km² (8.23%) in 2030. Overall, RF demonstrated the strongest and most consistent classification performance under the adopted experimental framework, indicating its potential for supporting forest monitoring, encroachment hotspot identification, and evidencebased conservation planning.","摘要 森林侵占对受保护森林生态系统构成重大威胁，其驱动因素包括基础设施建设、农业扩张和聚落增长等日益加剧的人类活动。因此，持续监测对于有效的保护规划和可持续森林管理至关重要。本研究提出了一种基于人工智能的森林侵占预测框架，以印度班迪普尔国家公园为研究区，利用多时相Sentinel-2卫星影像，并结合地形、土地覆盖和人为变量。研究将多时相归一化植被指数（NDVI）和归一化燃烧比（NBR）与高程、坡度、坡向、森林和水体掩膜以及距道路距离和距聚落距离等变量相结合。研究对比评估了三种预测模型，即随机森林（RF）、多层感知机（MLP）以及混合卷积神经网络—长短期记忆网络（CNN–LSTM）架构。RF取得了最高的分类精度，为94.17%，其次是MLP的91.50%和CNN–LSTM的78.69%。2026年和2030年的未来预测表明，在RF和MLP下森林侵占相对有限，最大预测面积分别为1.27 km²（0.15%）和0.64 km²（0.07%）。相比之下，CNN–LSTM预测的侵占面积要大得多，2026年为115.76 km²（13.24%），2030年为71.94 km²（8.23%）。总体而言，在所采用的实验框架下，RF表现出最强且最一致的分类性能，表明其具有支持森林监测、侵占热点识别和循证保护规划的潜力。",null,"Scientific Reports","2026-09-16T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,22,18,14,1,"基于多时相Sentinel-2与机器学习预测森林侵占，方法对比扎实、结论明确，对农业遥感与生态监测有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","机器学习","NDVI","生态监测","森林保护",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},12,true,[48],"machine learning",[50],"artificial intelligence",[27],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I3",2,[],[],[28,26],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"H6",[],[],[29],[64,65],"农业遥感 机器学习 森林保护 生态监测","农业遥感 机器学习","农业遥感机器学习森林保护生态监测-2681","10.1038\u002Fs41598-026-70597-0",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":86,"ingested_from":87},"W7213307679",[71,73,75,77],{"name":72,"orcid":9},"Pushpa B. R",{"name":74,"orcid":9},"H. R. Chaitanya",{"name":76,"orcid":9},"Chandhana U. Shankar",{"name":78,"orcid":9},"R. Sudarshan",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"用多时相Sentinel-2影像和机器学习预测印度Bandipur国家公园的森林侵占。","Sentinel-2多时相NDVI\u002FNBR结合地形、土地覆盖和人为变量，比较RF","RF分类精度最高达94.17%，预测2026和2030年侵占面积有限，CNN-LSTM预测值显著偏大","农业遥感与作物表型","可探索多源遥感与深度学习融合提升侵占预测精度，并推广到其他保护区或农业扩张监测。","农业人工智能与决策模型","openalex","2026-09-16T23:30:46.701925Z",{"total":90,"page":21,"page_size":90,"items":91},6,[92,180,278,340,437,535],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":105,"tags":107,"primary_topic":111,"secondary_topics":117,"classification":124,"search_phrases":161,"slug":164,"view_count":54,"doi":165,"paper":166,"created_at":179},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere","2026-09-27T00:00:00Z",73,{"impact":45,"substance":18,"depth":19,"authority":102,"freshness":103,"relevant":21,"comment":104},13,8,"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[106],{"name":98,"url":95},[108,26,27,109,110],"智慧农业","土壤墒情","蒸散发估算",{"code":112,"slug":113,"name":114,"path":115,"parent_code":116},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[118,123],{"code":119,"slug":120,"name":121,"path":122,"parent_code":116},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":46},[127,135,140,145,152,157],{"code":112,"score":128,"supported":46,"title":129,"abstract":131,"tags":134},17,[130],"evapotranspiration",[132,133,130],"水资源","蒸散发",[],{"code":119,"score":17,"supported":46,"title":136,"abstract":137,"tags":139},[120],[138,120],"土壤",[109],{"code":32,"score":141,"supported":46,"title":142,"abstract":143,"tags":144},9,[48],[],[27],{"code":146,"score":147,"supported":46,"title":148,"abstract":149,"tags":151},"J1",3,[],[150],"农业规划",[],{"code":153,"score":21,"supported":14,"title":154,"abstract":155,"tags":156},"I1",[],[],[108],{"code":53,"score":21,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[26],[162,163],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804","10.3390\u002Fatmos17100941",{"doi":165,"openalex_id":167,"authors":168,"venue":98,"cited_by_count":79,"oa_url":95,"card":174,"direction":86,"ingested_from":87},"W7214588993",[169,172],{"name":170,"orcid":171},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":173,"orcid":9},"Devrim Alkaya",{"tldr":175,"method":176,"finding":177,"direction":86,"opportunity":178},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":9,"source_name":186,"source_url":183,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":191,"tags":193,"primary_topic":196,"secondary_topics":197,"classification":198,"search_phrases":250,"slug":253,"view_count":237,"doi":254,"paper":255,"created_at":277},3677,"A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04697-2","Abstract Sustainable development has become a key priority for the agricultural sector, both globally and in Quebec. Digital agriculture, as an important component of sustainable agricultural systems, represents a promising pathway toward achieving sustainable development goals. However, despite the recognized benefits of digital agriculture, the opportunities, limitations, and research gaps associated with its contribution to sustainable development in Quebec remain insufficiently understood. This study applies a systematic tracking approach to identify applications of digital technologies in Quebec’s agricultural sector and evaluate their potential contributions to provincial sustainable development objectives while highlighting gaps and opportunities for their enhanced deployment. A total of 59 peer-reviewed publications and grey literature reports were identified and analyzed. Overall, 14 categories of digital technologies were documented within Quebec’s agricultural sector. Their representation in the scientific literature was primarily concentrated in three dominant categories: web\u002Fmobile applications and software (31%), machine learning and sensors (14%), and satellite technologies (13%). The identified digital technologies have significant potential to support sustainable development objectives in Quebec agriculture through improved pesticide monitoring, soil health assessment and conservation, fertilizer management optimization, water-use efficiency, and biodiversity conservation. Collectively, these technologies could be combined in 65 distinct ways to support sustainable development goals; however, only 54% of this potential is currently being exploited in Quebec. This highlights substantial opportunities for expanding the role of digital agriculture in addressing sustainability challenges. The study also reveals important knowledge gaps, particularly regarding the social dimensions influencing the diffusion and adoption of digital technologies in agriculture. A limited understanding of the factors affecting Quebec farmers’ adoption decisions remains a major barrier to maximizing the benefits of digital agriculture. Addressing these social and behavioral dimensions will be essential for accelerating the transition toward more sustainable, digitally enabled agricultural systems in Quebec.","摘要 可持续发展已成为全球及魁北克农业部门的重要优先事项。数字农业作为可持续农业系统的重要组成部分，是实现可持续发展目标的一条充满前景的路径。然而，尽管数字农业的益处已得到认可，但其对魁北克可持续发展所作贡献的机遇、局限及研究空白仍未被充分理解。本研究采用系统性追踪方法，识别数字技术在魁北克农业部门中的应用，评估其对省级可持续发展目标的潜在贡献，同时揭示其加强部署的空白与机遇。共识别并分析了59篇同行评审出版物和灰色文献报告。总体而言，魁北克农业部门中记录到14类数字技术。其在科学文献中的呈现主要集中在三个主导类别：网络\u002F移动应用程序与软件（31%）、机器学习与传感器（14%）以及卫星技术（13%）。所识别的数字技术通过改善农药监测、土壤健康评估与保护、肥料管理优化、水资源利用效率以及生物多样性保护，具有支持魁北克农业可持续发展目标的显著潜力。总体而言，这些技术可以65种不同方式组合以支持可持续发展目标；然而，魁北克目前仅开发利用了其中54%的潜力。这凸显了扩大数字农业在应对可持续性挑战方面作用的重大机遇。本研究还揭示了重要的知识空白，特别是影响数字技术在农业中扩散与采纳的社会维度方面。对影响魁北克农民采纳决策的因素了解有限，仍是最大化数字农业效益的主要障碍。解决这些社会与行为维度对于加速魁北克向更可持续、数字化赋能的农业系统转型至关重要。","Discover Sustainability",78,{"impact":189,"substance":18,"depth":19,"authority":102,"freshness":141,"relevant":21,"comment":190},16,"基于59篇文献的系统梳理，量化了魁北克数字农业技术类别与可持续目标支撑潜力，指出仅54%潜力被利用及农户采纳的社会维度研究缺口，对区域数字农业政策有参考价值。",[192],{"name":186,"url":183},[194,108,26,27,195],"数字农业","农业可持续发展",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":199},{"candidates":200,"agricultural_evidence":46},[201,206,211,218,225,229,235,242,246],{"code":153,"score":189,"supported":46,"title":202,"abstract":204,"tags":205},[203],"digital agriculture",[194,203],[194,108],{"code":32,"score":207,"supported":46,"title":208,"abstract":209,"tags":210},7,[],[27,48],[27],{"code":212,"score":90,"supported":46,"title":213,"abstract":214,"tags":217},"F3",[],[215,216],"肥料","fertilizer",[],{"code":219,"score":90,"supported":46,"title":220,"abstract":221,"tags":224},"F4",[],[222,223],"农药","pesticide",[],{"code":119,"score":90,"supported":46,"title":226,"abstract":227,"tags":228},[],[138,120],[],{"code":59,"score":90,"supported":46,"title":230,"abstract":231,"tags":234},[],[232,233],"生物多样性","biodiversity",[],{"code":236,"score":237,"supported":46,"title":238,"abstract":239,"tags":241},"H8",4,[],[240],"可持续农业",[195],{"code":112,"score":147,"supported":46,"title":243,"abstract":244,"tags":245},[],[132],[],{"code":53,"score":21,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[26],[251,252],"魁北克 数字农业","魁北克 农业技术 应用","魁北克数字农业-3677","10.1007\u002Fs43621-026-04697-2",{"doi":254,"openalex_id":256,"authors":257,"venue":186,"cited_by_count":79,"oa_url":183,"card":271,"direction":275,"ingested_from":87},"W7214562996",[258,260,263,266,268],{"name":259,"orcid":9},"Sambiani D. Y. Tindjiete",{"name":261,"orcid":262},"Terence Épule Épule","https:\u002F\u002Forcid.org\u002F0000-0002-5756-382X",{"name":264,"orcid":265},"Daniel Etongo","https:\u002F\u002Forcid.org\u002F0000-0002-8237-0843",{"name":267,"orcid":9},"Changhui Peng",{"name":269,"orcid":270},"Paul Célicourt","https:\u002F\u002Forcid.org\u002F0000-0001-9297-6593",{"tldr":272,"method":273,"finding":274,"direction":275,"opportunity":276},"系统盘点魁北克数字农业技术应用及其对可持续发展的贡献与知识空白。","系统追踪59篇同行评议与灰色文献，归类14类数字技术并评估组合潜力。","技术可65种组合支撑可持续发展目标，但仅54%潜力被利用，社会采纳因素研究不足。","数字乡村与农业信息化","可深入研究农户数字技术采纳的社会行为因素，填补魁北克及类似地区扩散机制的研究空白。","2026-09-28T23:30:36.987716Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":297,"classification":298,"search_phrases":320,"slug":323,"view_count":54,"doi":324,"paper":325,"created_at":339},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z",72,{"impact":45,"substance":288,"depth":128,"authority":102,"freshness":141,"relevant":21,"comment":289},21,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[291],{"name":284,"url":281},[293,27,28,294,295],"Sentinel-2","遥感监测","建成区提取",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":299},{"candidates":300,"agricultural_evidence":46},[301,305,312,316],{"code":32,"score":45,"supported":46,"title":302,"abstract":303,"tags":304},[48],[27],[27],{"code":306,"score":90,"supported":46,"title":307,"abstract":308,"tags":311},"I4",[],[309,310],"空间分析","spatial analysis",[],{"code":119,"score":147,"supported":46,"title":313,"abstract":314,"tags":315},[],[120],[],{"code":53,"score":147,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[28,293,294],[321,322],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":324,"openalex_id":326,"authors":327,"venue":284,"cited_by_count":79,"oa_url":281,"card":334,"direction":86,"ingested_from":87},"W7214385607",[328,331],{"name":329,"orcid":330},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":332,"orcid":333},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":335,"method":336,"finding":337,"direction":84,"opportunity":338},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","2026-09-26T23:30:53.200163Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":348,"score_detail":349,"sources":351,"tags":353,"primary_topic":356,"secondary_topics":361,"classification":367,"search_phrases":390,"slug":393,"view_count":103,"doi":394,"paper":395,"created_at":436},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment","2026-09-22T00:00:00Z",64,{"impact":103,"substance":19,"depth":189,"authority":102,"freshness":141,"relevant":21,"comment":350},"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[352],{"name":346,"url":343},[108,26,354,27,355],"水稻","高光谱遥感",{"code":357,"slug":358,"name":354,"path":359,"parent_code":360},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[362,366],{"code":53,"slug":363,"name":364,"path":365,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":368},{"candidates":369,"agricultural_evidence":46},[370,378,382,386],{"code":53,"score":371,"supported":46,"title":372,"abstract":375,"tags":377},27,[373,374],"remote sensing","hyperspectral",[376,373,374],"遥感",[26,355],{"code":32,"score":17,"supported":46,"title":379,"abstract":380,"tags":381},[48],[27,48],[27],{"code":357,"score":45,"supported":46,"title":383,"abstract":384,"tags":385},[358],[358],[354],{"code":153,"score":21,"supported":14,"title":387,"abstract":388,"tags":389},[],[],[108],[391,392],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":394,"openalex_id":396,"authors":397,"venue":346,"cited_by_count":79,"oa_url":9,"card":431,"direction":84,"ingested_from":87},"W7214027889",[398,401,404,407,410,413,415,417,420,423,425,427,429],{"name":399,"orcid":400},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":402,"orcid":403},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":405,"orcid":406},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":408,"orcid":409},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":411,"orcid":412},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":414,"orcid":9},"Ying Luo",{"name":416,"orcid":9},"Jiaqian Zhang",{"name":418,"orcid":419},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":421,"orcid":422},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":424,"orcid":9},"Chaoliang Peng",{"name":426,"orcid":9},"Duan Tian",{"name":428,"orcid":9},"Weihao Wang",{"name":430,"orcid":9},"Jiaxin Liu",{"tldr":432,"method":433,"finding":434,"direction":84,"opportunity":435},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":9,"source_name":443,"source_url":440,"published_at":444,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":445,"sources":447,"tags":449,"primary_topic":452,"secondary_topics":458,"classification":465,"search_phrases":518,"slug":521,"view_count":54,"doi":522,"paper":523,"created_at":534},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna","2026-09-07T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":45,"freshness":54,"relevant":21,"comment":446},"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[448],{"name":443,"url":440},[108,450,26,27,451],"精准施肥","小农户",{"code":453,"slug":454,"name":455,"path":456,"parent_code":457},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[459,464],{"code":212,"slug":460,"name":461,"path":462,"parent_code":463},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":466},{"candidates":467,"agricultural_evidence":46},[468,473,479,484,490,494,501,505,512],{"code":453,"score":17,"supported":46,"title":469,"abstract":471,"tags":472},[470],"smallholder",[451,470],[451],{"code":212,"score":13,"supported":46,"title":474,"abstract":475,"tags":478},[],[476,477,216],"施肥","养分管理",[450],{"code":32,"score":13,"supported":46,"title":480,"abstract":481,"tags":483},[],[27,48,482],"decision support",[27],{"code":153,"score":207,"supported":46,"title":485,"abstract":486,"tags":489},[],[487,488],"精准农业","precision agriculture",[108],{"code":53,"score":207,"supported":46,"title":491,"abstract":492,"tags":493},[],[376,373],[26],{"code":495,"score":90,"supported":46,"title":496,"abstract":497,"tags":500},"B7",[],[498,499],"温室","greenhouse",[],{"code":119,"score":90,"supported":46,"title":502,"abstract":503,"tags":504},[],[138,120],[],{"code":506,"score":90,"supported":46,"title":507,"abstract":508,"tags":511},"I5",[],[509,510],"物联网","IoT",[],{"code":513,"score":147,"supported":46,"title":514,"abstract":515,"tags":517},"H4",[],[516],"carbon sequestration",[],[519,520],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":522,"openalex_id":524,"authors":525,"venue":443,"cited_by_count":79,"oa_url":440,"card":528,"direction":532,"ingested_from":87},"W7211915396",[526],{"name":527,"orcid":9},"Anuga Liyanage",{"tldr":529,"method":530,"finding":531,"direction":532,"opportunity":533},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","智慧农业 \u002F 农业物联网","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":536,"title":537,"url":538,"summary":539,"summary_zh":540,"content":9,"source_name":541,"source_url":538,"published_at":542,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":543,"score_detail":544,"sources":546,"tags":548,"primary_topic":552,"secondary_topics":557,"classification":564,"search_phrases":585,"slug":587,"view_count":79,"doi":588,"paper":589,"created_at":606},1616,"Climate change, vegetation dynamics, and maize production in Mozambique: evidence from method of moments quantile regression and machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1889991","This study examines the impacts of climate variability and vegetation dynamics on maize production across 10 provinces in Mozambique using a balanced panel dataset covering the period 2002–2023. Following confirmation of cross-sectional dependence, second-generation panel techniques are employed. The Kao and Westerlund cointegration tests reveal a stable long-run relationship among the variables. Results from the Method of Moments Quantile Regression (MMQREG) indicate significant distributional heterogeneity in climate–maize relationships. Rainfall and the Normalized Difference Vegetation Index (NDVI) positively influence maize output, with stronger effects observed in lower-producing provinces, whereas temperature exerts a negative and statistically significant impact, particularly at the median and upper quantiles. Mediation analysis further confirms that NDVI partially transmits the effects of climatic variables to maize production, providing empirical support for the proposed climate–vegetation–production pathway. Interaction analysis shows that adequate rainfall and healthier vegetation conditions help mitigate temperature-induced stress. In addition, regional analysis across Northern, Central, and Southern Mozambique reveals substantial spatial heterogeneity, with semi-arid provinces exhibiting greater climate sensitivity. Complementary machine learning analysis identifies the K-Nearest Neighbors (KNN) model as the most accurate predictor of maize production, while temperature, NDVI, and rainfall emerge as the most influential predictors. The findings underscore the importance of accounting for both distributional and spatial heterogeneity in climate–agriculture linkages and highlight the need for climate-smart, region-specific interventions and data-driven forecasting tools to strengthen maize productivity and resilience in Mozambique.","本研究利用2002—2023年间的平衡面板数据，考察了气候变率与植被动态对莫桑比克10个省份玉米产量的影响。在确认截面相依性后，采用第二代面板技术进行分析。Kao和Westerlund协整检验显示变量间存在稳定的长期关系。矩分位数回归（MMQREG）结果表明，气候—玉米关系存在显著的分位数异质性。降雨量和归一化植被指数（NDVI）对玉米产量具有正向影响，且在低产省份中效应更强；而温度则产生显著负向影响，尤其在中位数及上分位数处表现明显。中介分析进一步证实，NDVI部分传导了气候变量对玉米产量的影响，为所提出的“气候—植被—产量”路径提供了实证支持。交互作用分析表明，充足的降雨和良好的植被状况有助于缓解温度胁迫。此外，对莫桑比克北部、中部和南部的区域分析揭示了显著的空间异质性，半干旱省份表现出更高的气候敏感性。补充的机器学习分析识别出K近邻（KNN）模型为玉米产量预测精度最高的模型，而温度、NDVI和降雨量则是最具影响力的预测因子。研究结果强调了在气候—农业关联中考虑分布异质性与空间异质性的重要性，并凸显了采取气候智慧型、区域针对性干预措施及数据驱动的预测工具以增强莫桑比克玉米生产力和韧性的必要性。","Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z",77,{"impact":19,"substance":18,"depth":19,"authority":45,"freshness":207,"relevant":21,"comment":545},"论文运用多种计量与机器学习方法，揭示气候与植被对莫桑比克玉米产量的异质性影响，数据详实，结论具区域政策参考价值。",[547],{"name":541,"url":538},[26,27,549,550,551],"气候变化","玉米生产","莫桑比克",{"code":553,"slug":554,"name":555,"path":556,"parent_code":360},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[558,559],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":560,"slug":561,"name":562,"path":563,"parent_code":116},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":565},{"candidates":566,"agricultural_evidence":46},[567,571,575,581],{"code":32,"score":17,"supported":46,"title":568,"abstract":569,"tags":570},[48],[27,48],[27],{"code":553,"score":20,"supported":46,"title":572,"abstract":573,"tags":574},[554],[555,554],[],{"code":560,"score":45,"supported":46,"title":576,"abstract":578,"tags":580},[577],"climate change",[579],"干旱",[549],{"code":53,"score":21,"supported":14,"title":582,"abstract":583,"tags":584},[],[],[26],[586,65],"农业遥感 机器学习 气候变化 玉米生产","农业遥感机器学习气候变化玉米生产-1616","10.3389\u002Ffsufs.2026.1889991",{"doi":588,"openalex_id":590,"authors":591,"venue":541,"cited_by_count":79,"oa_url":600,"card":601,"direction":532,"ingested_from":87},"W7207867677",[592,595,597],{"name":593,"orcid":594},"Faustino Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-5192-0144",{"name":596,"orcid":9},"Jianbo Hu",{"name":598,"orcid":599},"Nazir Muhammad Abdullahi","https:\u002F\u002Forcid.org\u002F0000-0003-2149-0638","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1889991\u002Fpdf",{"tldr":602,"method":603,"finding":604,"direction":86,"opportunity":605},"研究气候变化与植被动态对莫桑比克玉米生产的影响，采用分位数回归和机器学习方法。","使用2002-2023年面板数据，矩分位数回归（MMQREG）和KNN机器学习模","降雨和NDVI正向影响玉米产量，温度负向影响，且存在分布和空间异质性。","可延伸研究气候变化下作物产量预测的机器学习模型优化，或区域适应性农业管理策略。","2026-09-04T23:30:08.552197Z"]