[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2946":3,"related-2946":105},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":44,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":104},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。",null,"Statistical Journal of the IAOS","2026-09-18T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","遥感监测","可可出口","农业预测","气候指数",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,65],{"code":33,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},15,true,[54],"remote sensing",[56,54],"遥感",[28],{"code":40,"score":13,"supported":52,"title":59,"abstract":60,"tags":64},[],[61,27,62,63],"深度学习","machine learning","deep learning",[27],{"code":66,"score":67,"supported":52,"title":68,"abstract":69,"tags":71},"B3",3,[],[70],"可可",[],[73,74],"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",0,"10.1177\u002F18747655261486415",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":76,"oa_url":9,"card":96,"direction":102,"ingested_from":103},"W7213558756",[81,84,86,88,91,94],{"name":82,"orcid":83},"Erna Nurmawati","https:\u002F\u002Forcid.org\u002F0009-0002-3385-673X",{"name":85,"orcid":9},"Neli Agustina",{"name":87,"orcid":9},"Robert Kurniawan",{"name":89,"orcid":90},"Prana Ugiana Gio","https:\u002F\u002Forcid.org\u002F0000-0002-8155-005X",{"name":92,"orcid":93},"Rayhan Abyasa","https:\u002F\u002Forcid.org\u002F0009-0006-0476-7999",{"name":95,"orcid":9},"Aditya Hari Kurnia Putra",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"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。","农业人工智能与决策模型","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。","农业遥感与作物表型","openalex","2026-09-19T23:30:33.176995Z",{"total":106,"page":22,"page_size":106,"items":107},6,[108,213,300,383,469,529],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":138,"search_phrases":187,"slug":190,"view_count":191,"doi":192,"paper":193,"created_at":212},4575,"A review of remote sensing for agricultural drought: technologies, indices, modelling advances and future prospects","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2738981","Agricultural drought is a complex hazard that jeopardises food security and progress toward the Sustainable Development Goals. We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z",81,{"impact":118,"substance":18,"depth":119,"authority":120,"freshness":121,"relevant":22,"comment":122},18,19,14,8,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[124],{"name":114,"url":111},[126,127,27,28,128],"智慧农业","粮食安全","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[131,137],{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":139},{"candidates":140,"agricultural_evidence":52},[141,145,151,156,163,170,176,182],{"code":33,"score":51,"supported":52,"title":142,"abstract":143,"tags":144},[54],[56,54],[28],{"code":132,"score":120,"supported":52,"title":146,"abstract":148,"tags":150},[147],"drought",[149,147],"干旱",[],{"code":40,"score":13,"supported":52,"title":152,"abstract":153,"tags":155},[],[61,27,62,63,154],"artificial intelligence",[27],{"code":157,"score":158,"supported":52,"title":159,"abstract":160,"tags":162},"A8",7,[],[127,161],"food security",[127],{"code":164,"score":106,"supported":52,"title":165,"abstract":166,"tags":169},"H2",[],[167,168],"土壤","soil",[],{"code":171,"score":67,"supported":52,"title":172,"abstract":173,"tags":175},"D",[],[174],"水产",[],{"code":177,"score":67,"supported":52,"title":178,"abstract":179,"tags":181},"H6",[],[180],"农业生态",[],{"code":183,"score":22,"supported":14,"title":184,"abstract":185,"tags":186},"I1",[],[],[126],[188,189],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575",2,"10.1080\u002F15481603.2026.2738981",{"doi":192,"openalex_id":194,"authors":195,"venue":114,"cited_by_count":76,"oa_url":205,"card":206,"direction":211,"ingested_from":103},"W7221081979",[196,199,202],{"name":197,"orcid":198},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":200,"orcid":201},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":203,"orcid":204},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":207,"method":208,"finding":209,"direction":102,"opportunity":210},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:14.933078Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":224,"tags":226,"primary_topic":231,"secondary_topics":232,"classification":234,"search_phrases":267,"slug":270,"view_count":22,"doi":271,"paper":272,"created_at":299},3830,"Current breakthroughs in biomass and carbon modelling of rubber plantations: A bibliometric analysis and literature review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15821","Rubber plantations (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.) are expanding tropical carbon (C) stores, storing an estimated 160–300 mg C ha-1 of total ecosystem C at maturity, but precise biomass and soil organic carbon (SOC) assessment remains difficult due to age disparities, historical land-use variations and data gaps. This review systematically analyses trends and research gaps in biomass accumulation of rubber systems. PRISMA 2020 guidelines were used to examine 104 studies in Scopus published between 1999 and 2025. The literature revealed a marked increase in research studies in China and Thailand post-2013 using a bibliometric approach presented by Biblioshiny. The most important methods are species-specific allometry (coefficient of determination (R2) > 0.9), age-based growth curves, remote sensing (RS) regressions (R2 0.2–0.7) and machine learning (ML) models like Random Forest\u002FXGBoost (R2 0.8–0.97, root mean square error (RMSE) 7–40 mg\u002Fha), which outperform simpler parametric models. Deep learning (DL) yields the highest accuracies (RMSE ~6 mg ha-1). Gaps persist in standardised protocols, South Indian datasets (e.g., Tamil Nadu\u002FWestern Ghats) and integrated lifecycle carbon accounting, including latex\u002FSOC. Forest-to-rubber conversion incurs net losses, while cropland-to-rubber\u002Fagroforestry offers gains. The authors recommend adopting harmonised Tier 2\u002F3 IPCC models, multi-decadal RS archives for reducing emissions from deforestation and forest degradation (REDD+) and ML extension to Indian smallholders. Policy integration with offset markets requires avoiding deforestation to realise climate-smart benefits.","橡胶林（Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg.）正在扩大热带碳（C）储量，据估计，成熟期生态系统总碳储量可达160–300 mg C ha⁻¹，但由于林龄差异、历史土地利用变化及数据缺口，精确评估生物量和土壤有机碳（SOC）仍然困难。本综述系统分析了橡胶系统生物量积累的趋势与研究空白。采用PRISMA 2020指南，对Scopus数据库中1999年至2025年间发表的104篇研究进行了审查。通过Biblioshiny文献计量方法分析发现，2013年后中国和泰国的研究数量显著增加。最重要的方法包括物种特异性异速生长方程（决定系数（R²）> 0.9）、基于林龄的生长曲线、遥感（RS）回归（R² 0.2–0.7）以及机器学习（ML）模型如随机森林\u002FXGBoost（R² 0.8–0.97，均方根误差（RMSE）7–40 mg\u002Fha），后者优于简单的参数模型。深度学习（DL）精度最高（RMSE约6 mg ha⁻¹）。在标准化方案、南印度数据集（如泰米尔纳德邦\u002F西高止山脉）以及涵盖乳胶\u002FSOC的综合生命周期碳核算方面仍存在空白。森林转橡胶林会导致净碳损失，而农田转橡胶林\u002F农林复合经营则带来碳增益。作者建议采用统一的IPCC第2\u002F3层级模型、多年代际遥感档案以支持减少毁林和森林退化所致排放（REDD+），并将机器学习应用扩展至印度小农户。与抵消市场的政策整合需要避免毁林，以实现气候智慧型效益。","Plant Science Today","2026-09-28T00:00:00Z",80,{"impact":118,"substance":18,"depth":118,"authority":20,"freshness":21,"relevant":22,"comment":223},"系统综述橡胶林生物量与碳建模研究进展，方法学与数据缺口梳理扎实，对农业碳汇与遥感监测领域有参考价值。",[225],{"name":219,"url":216},[27,28,227,228,229,230],"农业碳汇","生物量估算","橡胶林","碳汇计量",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[233],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":235},{"candidates":236,"agricultural_evidence":52},[237,244,248,252,256,262],{"code":238,"score":120,"supported":52,"title":239,"abstract":241,"tags":243},"I7",[240],"bibliometric",[242,240],"文献计量",[],{"code":40,"score":13,"supported":52,"title":245,"abstract":246,"tags":247},[],[61,27,62,63],[27],{"code":33,"score":121,"supported":52,"title":249,"abstract":250,"tags":251},[],[56,54],[228,28],{"code":164,"score":106,"supported":52,"title":253,"abstract":254,"tags":255},[],[167,168],[],{"code":257,"score":67,"supported":52,"title":258,"abstract":259,"tags":261},"G2",[],[260],"小农户",[],{"code":263,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},"H4",[],[],[227],[268,269],"橡胶林 生物量 碳储量","橡胶园 遥感 机器学习","橡胶林生物量碳储量-3830","10.14719\u002Fpst.15821",{"doi":271,"openalex_id":273,"authors":274,"venue":219,"cited_by_count":76,"oa_url":293,"card":294,"direction":211,"ingested_from":103},"W7214623646",[275,278,281,284,287,290],{"name":276,"orcid":277},"R M Dhinu","https:\u002F\u002Forcid.org\u002F0009-0008-3471-1093",{"name":279,"orcid":280},"M. Vijayabhama","https:\u002F\u002Forcid.org\u002F0000-0001-9437-9477",{"name":282,"orcid":283},"Balaji Kannan","https:\u002F\u002Forcid.org\u002F0000-0003-2330-0893",{"name":285,"orcid":286},"K Baranidharan","https:\u002F\u002Forcid.org\u002F0000-0001-9371-8588",{"name":288,"orcid":289},"M Kalpana","https:\u002F\u002Forcid.org\u002F0000-0002-0201-6968",{"name":291,"orcid":292},"M Sankavi","https:\u002F\u002Forcid.org\u002F0009-0006-1493-1123","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F15821\u002F16343",{"tldr":295,"method":296,"finding":297,"direction":102,"opportunity":298},"综述橡胶林生物量与碳建模研究进展，指出机器学习与深度学习精度最高但标准化和区域数据仍不足。","PRISMA 2020 文献计量分析 104 篇 Scopus 文献，比较异速生","机器学习\u002F深度学习精度优于传统模型，但标准协议、印度南部数据和全生命周期碳核算仍存空白。","可面向印度小农户构建多年代遥感与机器学习融合的橡胶林碳核算与REDD+监测体系。","2026-09-30T23:30:12.585164Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"primary_topic":316,"secondary_topics":322,"classification":325,"search_phrases":353,"slug":356,"view_count":67,"doi":357,"paper":358,"created_at":382},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","2026-09-22T00:00:00Z",78,{"impact":120,"substance":18,"depth":118,"authority":120,"freshness":13,"relevant":22,"comment":310},"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。",[312],{"name":306,"url":303},[126,27,314,28,315],"芒果","作物估产",{"code":317,"slug":318,"name":319,"path":320,"parent_code":321},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[323,324],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":326},{"candidates":327,"agricultural_evidence":52},[328,333,337,341,347],{"code":317,"score":51,"supported":52,"title":329,"abstract":331,"tags":332},[330],"orchard",[314,330],[314],{"code":40,"score":51,"supported":52,"title":334,"abstract":335,"tags":336},[62],[27,62],[27],{"code":33,"score":121,"supported":52,"title":338,"abstract":339,"tags":340},[],[56,54],[315,28],{"code":183,"score":342,"supported":52,"title":343,"abstract":344,"tags":346},4,[],[345],"精准农业",[126],{"code":348,"score":67,"supported":52,"title":349,"abstract":350,"tags":352},"J1",[],[351],"农业规划",[],[354,355],"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":357,"openalex_id":359,"authors":360,"venue":306,"cited_by_count":76,"oa_url":303,"card":377,"direction":102,"ingested_from":103},"W7213920056",[361,363,366,368,370,373,375],{"name":362,"orcid":9},"V. Raju",{"name":364,"orcid":365},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":367,"orcid":9},"Dr. V. S. Thorat",{"name":369,"orcid":9},"V. T. Shinde",{"name":371,"orcid":372},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":374,"orcid":9},"Alok Shrivastava",{"name":376,"orcid":9},"A. P. Chaudhary",{"tldr":378,"method":379,"finding":380,"direction":102,"opportunity":381},"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":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":396,"tags":398,"primary_topic":402,"secondary_topics":405,"classification":408,"search_phrases":423,"slug":426,"view_count":76,"doi":427,"paper":428,"created_at":468},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":17,"substance":393,"depth":394,"authority":20,"freshness":121,"relevant":22,"comment":395},20,16,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[397],{"name":389,"url":386},[27,56,399,400,401],"土壤碳","Landsat","数字土壤制图",{"code":164,"slug":168,"name":403,"path":404,"parent_code":136},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[406,407],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":409},{"candidates":410,"agricultural_evidence":52},[411,415,419],{"code":164,"score":120,"supported":52,"title":412,"abstract":413,"tags":414},[168],[167,168],[],{"code":40,"score":21,"supported":52,"title":416,"abstract":417,"tags":418},[62],[],[27],{"code":33,"score":121,"supported":52,"title":420,"abstract":421,"tags":422},[],[56,54],[400,56],[424,425],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":427,"openalex_id":429,"authors":430,"venue":389,"cited_by_count":76,"oa_url":9,"card":463,"direction":102,"ingested_from":103},"W7222107492",[431,434,437,440,443,446,449,452,454,457,460],{"name":432,"orcid":433},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":435,"orcid":436},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":438,"orcid":439},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":441,"orcid":442},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":444,"orcid":445},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":447,"orcid":448},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":450,"orcid":451},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":453,"orcid":9},"A. E. Shatunov",{"name":455,"orcid":456},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":458,"orcid":459},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":461,"orcid":462},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":464,"method":465,"finding":466,"direction":102,"opportunity":467},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":470,"title":471,"url":472,"summary":473,"summary_zh":474,"content":9,"source_name":475,"source_url":472,"published_at":476,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":477,"sources":479,"tags":481,"primary_topic":486,"secondary_topics":487,"classification":489,"search_phrases":503,"slug":506,"view_count":76,"doi":507,"paper":508,"created_at":528},4897,"Wild boar collision data and satellite computer vision refine habitat suitability mapping across France","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10344-026-02160-1","Abstract Wild boar (Sus scrofa) populations have expanded rapidly across Europe, leading to escalating human-wildlife conflicts (HWCs), notably wildlife-vehicle collisions (WVCs), increased agricultural damage and disease transmission. In continental France these issues are compounded by the species’ ecological adaptability exhibiting increasing overlaps with urbanization and transportation networks. In this study, we leverage time-series environmental data, computer vision and Species Distribution Modeling (SDM) to predict wild boar habitat suitability and investigate its spatiotemporal drivers. We use presence-only data from WVC reports on the national railway and road networks, along with publicly available GBIF observation collections, as complementary occurrence streams in our SDMs, while addressing inherent sampling biases of these datasets with tailored corrections. A key innovation of this study is the integration of national scale, annually updated high-resolution land cover predictors explicitly focused on wild boar resource preference. By fine-tuning a multitemporal Vision Transformer foundational AI model on multispectral satellite remote sensing imagery we capture subtle seasonal phenological differences. Our results highlight clear spatial, seasonal and annual variations in wild boar habitat suitability. The multitemporal SDM pipeline yields meaningful predictions when extrapolating to novel environmental scenarios such as climate extremes. Adding rail and road WVC records to GBIF-based SDMs retained high performance metrics while altering predicted spatial patterns and feature importance distribution. The methodological and ecological insights gained through this study provide additional knowledge for French transportation planning, agriculture and wildlife management. Identifying regions with high seasonal habitat suitability can inform targeted interventions. More broadly, our results demonstrate that advanced, data-driven methods are becoming indispensable for proactively and sustainably addressing HWCs in an increasingly anthropogenic world.","摘要 野猪（Sus scrofa）种群在欧洲迅速扩张，导致人兽冲突（HWCs）不断升级，尤其是野生动物车辆碰撞（WVCs）、农业损害加剧和疾病传播。在法国大陆，该物种的生态适应性使其与城市化和交通网络的重叠日益增加，进一步加剧了这些问题。本研究利用时间序列环境数据、计算机视觉和物种分布模型（SDM）预测野猪栖息地适宜性，并探究其时空驱动因素。我们使用国家铁路和公路网络中WVC报告的存在-only数据，以及公开可用的GBIF观测集合，作为SDM中的互补出现数据流，同时通过针对性校正处理这些数据集固有的采样偏差。本研究的一个关键创新是整合了国家尺度、年度更新的高分辨率土地覆盖预测因子，明确聚焦于野猪的资源偏好。通过在多光谱卫星遥感影像上微调多时相视觉Transformer基础AI模型，我们捕捉到了细微的季节物候差异。我们的结果突出了野猪栖息地适宜性在空间、季节和年际上的明显变化。多时相SDM流程在推断至气候极端等新型环境情景时产生了有意义的预测。将铁路和公路WVC记录添加到基于GBIF的SDM中，在保持高性能指标的同时改变了预测的空间格局和特征重要性分布。本研究获得的方法学和生态学见解为法国交通规划、农业和野生动物管理提供了额外知识。识别具有高季节性栖息地适宜性的区域可为针对性干预提供依据。更广泛地说，我们的结果表明，在日益受到人类影响的世界中，先进的数据驱动方法正成为前瞻性和可持续地应对人兽冲突不可或缺的手段。","European Journal of Wildlife Research","2026-10-13T00:00:00Z",{"impact":118,"substance":18,"depth":118,"authority":120,"freshness":21,"relevant":22,"comment":478},"该研究将卫星遥感与计算机视觉用于野猪栖息地建模，方法新颖、数据规模大，对交通规划与农业野生动物管理有参考价值，但属法国区域研究，国内产业影响有限。",[480],{"name":475,"url":472},[482,28,483,484,485],"农业人工智能","栖息地评估","野生动物管理","人兽冲突",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[488],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":490},{"candidates":491,"agricultural_evidence":52},[492,498],{"code":40,"score":51,"supported":52,"title":493,"abstract":495,"tags":497},[494],"computer vision",[496,494],"计算机视觉",[482],{"code":33,"score":13,"supported":52,"title":499,"abstract":500,"tags":502},[],[56,54,501],"multispectral",[28],[504,505],"法国 野猪 栖息地 遥感","野猪 车辆碰撞 卫星影像","法国野猪栖息地遥感-4897","10.1007\u002Fs10344-026-02160-1",{"doi":507,"openalex_id":509,"authors":510,"venue":475,"cited_by_count":76,"oa_url":472,"card":523,"direction":102,"ingested_from":103},"W7221702437",[511,514,516,519,521],{"name":512,"orcid":513},"Adrian Meyer","https:\u002F\u002Forcid.org\u002F0000-0002-8652-1477",{"name":515,"orcid":9},"Théo Reibel",{"name":517,"orcid":518},"Кevin Мorelle","https:\u002F\u002Forcid.org\u002F0000-0001-9041-6541",{"name":520,"orcid":9},"Mathias Kneubühler",{"name":522,"orcid":9},"Denis Jordan",{"tldr":524,"method":525,"finding":526,"direction":102,"opportunity":527},"融合野猪碰撞数据与卫星视觉模型，预测法国野猪栖息地适宜性及其时空驱动因素。","时间序列环境数据、Vision Transformer卫星影像、物种分布模型与G","栖息地适宜性呈明显时空变化，加入碰撞记录改变预测格局与特征重要性。","可将碰撞数据与遥感表型结合，拓展至其他野生动物-农业冲突的时空预警与决策。","2026-10-14T23:30:27.244673Z",{"id":530,"title":531,"url":532,"summary":533,"summary_zh":534,"content":9,"source_name":535,"source_url":532,"published_at":536,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":537,"score_detail":538,"sources":541,"tags":543,"primary_topic":546,"secondary_topics":547,"classification":550,"search_phrases":573,"slug":576,"view_count":22,"doi":577,"paper":578,"created_at":598},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":394,"substance":539,"depth":118,"authority":20,"freshness":21,"relevant":22,"comment":540},21,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[542],{"name":535,"url":532},[126,544,482,28,545],"无人机","病害预警",{"code":317,"slug":318,"name":319,"path":320,"parent_code":321},[548,549],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":551},{"candidates":552,"agricultural_evidence":52},[553,560,564,568],{"code":317,"score":19,"supported":52,"title":554,"abstract":556,"tags":559},[555],"apple",[557,558,555],"苹果","葡萄",[],{"code":40,"score":51,"supported":52,"title":561,"abstract":562,"tags":563},[63],[61,63],[482],{"code":33,"score":17,"supported":52,"title":565,"abstract":566,"tags":567},[501],[501],[28],{"code":183,"score":158,"supported":52,"title":569,"abstract":570,"tags":572},[],[345,571],"precision agriculture",[126],[574,575],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":577,"openalex_id":579,"authors":580,"venue":535,"cited_by_count":76,"oa_url":592,"card":593,"direction":211,"ingested_from":103},"W7221558726",[581,584,587,589],{"name":582,"orcid":583},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":585,"orcid":586},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":588,"orcid":9},"Fatih Sinan Bir",{"name":590,"orcid":591},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":594,"method":595,"finding":596,"direction":102,"opportunity":597},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z"]