[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1740":3,"related-1740":88},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":87},1740,"Spatio-Temporal Dynamics of Vegetation Response to Climatic Variability in Akinyele Local Government Area, Oyo State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1016-5660","Although vegetation is very sensitive to climatic variability, there is limited evidence on a local scale in many tropical areas. This study has evaluated the spatio-temporal variability of vegetation response to climatic variability in Akinyele Local Government Area, Oyo State, Nigeria using NDVI, Rainfall, LST data of multi-temporal satellite image for the years 2015, 2020 and 2025. Google Earth Engine was used to process the sentinel-2 imagery, CHIRPS rainfall data, and MODIS LST products. NDVI was categorized into 3 classes (sparse, moderate and dense vegetation). The data set consisted of 5,991 sample points with which statistical analyses (Pearson correlation, multiple linear regression, ANOVA) were carried out. Results indicated that from 2015 to 2020 the density of vegetation decreased by 126.45 km² but increased by 158.55 km² until 2025, in accordance with the climatic changes. Mean NDVI decreased from 0.629 in 2015 to 0.611 in 2020, then increased to 0.654 in 2025. LST had a consistently high negative correlation (r = -0.650 to -0.669, p \u003C 0.001) with NDVI, whereas there was a relatively weak positive correlation with rainfall (r = 0.291–0.336). Regression models accounted for 43%–45% of the variability in NDVI, with LST being the most significant predictor (p \u003C 0.001). The results provide a basis for understanding how temperature is the dominant climatic factor controlling vegetation in the study area, and its implications for land use planning, climate change adaptation, sustainable agriculture, and urban green infrastructure in the urbanising landscapes of Nigeria.","尽管植被对气候变率非常敏感，但在许多热带地区，关于局部尺度的证据仍然有限。本研究利用2015年、2020年和2025年的多时相卫星影像数据，包括归一化植被指数（NDVI）、降雨量和地表温度（LST），评估了尼日利亚奥约州阿基涅尔地方政府区域植被对气候变率的时空响应。研究采用谷歌地球引擎（Google Earth Engine）处理哨兵二号（Sentinel-2）影像、CHIRPS降雨数据和MODIS地表温度产品。NDVI被划分为三个等级（稀疏植被、中等植被和茂密植被）。数据集包含5，991个样本点，并据此进行了统计分析（皮尔逊相关分析、多元线性回归、方差分析）。结果表明，从2015年到2020年，植被密度减少了126.45平方公里，但到2025年又增加了158.55平方公里，这与气候变化相一致。平均NDVI从2015年的0.629降至2020年的0.611，随后在2025年上升至0.654。地表温度与NDVI始终呈高度负相关（r = -0.650至-0.669，p \u003C 0.001），而降雨量与NDVI呈相对较弱的正相关（r = 0.291–0.336）。回归模型解释了NDVI变异的43%–45%，其中地表温度是最显著的预测因子（p \u003C 0.001）。研究结果为理解温度作为控制研究区植被的主导气候因子提供了基础，并揭示了其对尼日利亚城市化景观中土地利用规划、气候变化适应、可持续农业和城市绿色基础设施建设的启示意义。",null,"FUDMA Journal of Sciences","2026-09-03T00:00:00Z","论文",10,false,57,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},8,20,18,3,1,"地方尺度植被对气候响应的遥感研究，数据详实，但影响范围有限，时效性较低。",[24],{"name":10,"url":6},[26,27,28,29],"农业遥感","气候变化","NDVI","植被监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,58],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},7,true,[],[27,48],"climate change",[27],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":57},"H8",6,[],[55,56],"可持续农业","sustainable agriculture",[],{"code":59,"score":20,"supported":14,"title":60,"abstract":61,"tags":62},"I3",[],[],[28,26,29],[64,65],"农业遥感 植被监测 气候变化 NDVI","农业遥感 植被监测","农业遥感植被监测气候变化NDVI-1740",2,"10.33003\u002Ffjs-2026-1016-5660",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":78,"oa_url":79,"card":80,"direction":84,"ingested_from":86},"W7207552979",[72,74,76],{"name":73,"orcid":9},"Monday Ghandi Daniel",{"name":75,"orcid":9},"Ojodugbowa Shedrach Omachoko",{"name":77,"orcid":9},"Iyam Ubi Effiom",0,"https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F5660\u002F3848",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"利用遥感数据评估尼日利亚某地植被对气候变化的时空响应。","GEE处理Sentinel-2、CHIRPS、MODIS数据，统计相关与回归分析","温度是主导因子，与NDVI强负相关，降雨弱正相关。","农业遥感与作物表型","可延伸至热带城市化区域植被-气候耦合模型及适应性管理策略研究。","openalex","2026-09-05T23:30:27.920627Z",{"total":52,"page":21,"page_size":52,"items":89},[90,151,231,307,385,447],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"primary_topic":109,"secondary_topics":110,"classification":111,"search_phrases":130,"slug":133,"view_count":21,"doi":134,"paper":135,"created_at":150},4756,"ديناميكيات وحساسية المناخ والغطاء النباتي في العراق باستخدام بيانات رصد الأرض طويلة الأمد","https:\u002F\u002Fdoi.org\u002F10.66026\u002Fy7543b35","تؤدي تغيرات المناخ إلى تكثيف الإجهاد الحراري والمائي عبر المناطق الجافة، مما يزيد من الحاجة إلى رصد متسق مكانياً وطويل الأمد لاستجابات الغطاء النباتي. العراق معرض بشكل خاص لهذه التغيرات لأن التدرجات المناخية القوية، والجفاف المتكرر، ومحدودية توافر المياه تتزامن مع مساحات شاسعة من الأراضي المطرية والمروية. قامت هذه الدراسة بتقييم ديناميكيات المناخ والغطاء النباتي في العراق من عام 2001 إلى 2025 باستخدام مؤشر الاختلاف الغطائي النباتي الطبيعي (MODIS NDVI)، وبيانات هطول الأمطار (CHIRPS)، وبيانات درجة حرارة الهواء على ارتفاع مترين (ERA5-Land). تم تحليل متوسط هطول الأمطار للفترة من أكتوبر إلى مايو، واتجاهات مؤشر (NDVI) للفترة من يناير إلى مايو، وارتباطات المناخ مع مؤشر (NDVI) بعد إزالة الاتجاه (detrended) على شبكة مشتركة بدقة 0.1 درجة.تم بعد ذلك تصنيف البكسلات النباتية على أنها: حساسة لهطول الأمطار، أو حساسة للحرارة، أو حساسة لكليهما، أو لا تظهر علاقة متوقعة قوية، أو تظهر اتجاهاً قوياً آخر. أظهر هطول الأمطار في الموسم الرطب تدرجاً واضحاً من إجماليات عالية جداً في شمال وشمال شرق العراق إلى إجماليات منخفضة ومنخفضة جداً عبر الغرب والجنوب الغربي والجنوب. كان الاخضرار ذو الدلالة الإحصائية أكثر انتشاراً بشكل ملحوظ من الاسمرار، حيث تركزت أقوى اتجاهات مؤشر (NDVI) الإيجابية في المناطق الزراعية في الشمال والشمال الشرقي والوسط. بعد إزالة الاتجاهات الخطية، كانت ارتباطات هطول الأمطار مع مؤشر (NDVI) إيجابية في الغالب وواسعة النطاق مكانياً، في حين كانت ارتباطات درجة الحرارة مع مؤشر (NDVI) ذات دلالة سلبية بشكل رئيسي ومركّزة في شمال وشرق وجنوب شرق العراق. هيمنت حساسية هطول الأمطار على التصنيف المتكامل، بينما شكلت الحساسية المزدوجة لهطول الأمطار والحرارة مجموعات متميزة في شمال-وسط وجنوب شرق البلاد. توفر هذه النتائج خط أساس مكانياً وطنياً لتحديد مناطق الغطاء النباتي المرتبطة بتقلبات المياه والحرارة بين السنوات. وتمثل فئات الحساسية ارتباطات إحصائية وليست عزوًا سببيًا.","气候变化正在加剧干旱地区的高温与水分胁迫，从而增加了对植被响应进行空间一致、长期监测的需求。伊拉克尤其容易受到这些变化的影响，因为强烈的气候梯度、频繁的干旱以及有限的水资源可用性，与大片雨养和灌溉土地并存。本研究利用MODIS归一化植被指数（NDVI）、降雨数据（CHIRPS）以及2米高度气温数据（ERA5-Land），评估了2001年至2025年伊拉克的气候与植被动态。研究在0.1度分辨率的统一网格上，分析了10月至5月的平均降雨量、1月至5月的NDVI趋势，以及去趋势（detrended）后气候与NDVI的相关性。随后将植被像元分类为：对降雨敏感、对温度敏感、对两者均敏感、未表现出强预期关系，或表现出其他强趋势。湿季降雨呈现出从伊拉克北部和东北部极高总量向西、西南和南部低及极低总量的明显梯度。具有统计显著性的绿化现象明显比褐化更为广泛，其中最强的NDVI正趋势集中在北部、东北部和中部的农业区。去除线性趋势后，降雨与NDVI的相关性大多为正且在空间上广泛分布，而温度与NDVI的相关性主要为显著负相关，集中在伊拉克北部、东部和东南部。降雨敏感性在综合分类中占主导地位，而降雨与温度的双重敏感性在该国中北部和东南部形成了独特的集群。这些结果为识别与年际水热波动相关的植被区域提供了全国尺度的空间基线。敏感性类别代表统计关联，而非因果归因。","Journal Of Babylon Center for Humanities Studies","2026-10-09T00:00:00Z",70,{"impact":100,"substance":101,"depth":19,"authority":100,"freshness":52,"relevant":21,"comment":102},12,22,"基于MODIS、CHIRPS与ERA5-Land长时序数据评估伊拉克气候与植被敏感性，方法规范、结论有区域参考价值，但属境外基础研究，公共影响有限。",[104],{"name":96,"url":93},[27,28,106,107,108],"遥感监测","干旱区农业","植被覆盖",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":112},{"candidates":113,"agricultural_evidence":45},[114,119,126],{"code":31,"score":17,"supported":45,"title":115,"abstract":116,"tags":118},[],[27,117],"干旱",[107,27],{"code":120,"score":52,"supported":45,"title":121,"abstract":122,"tags":125},"H3",[],[123,124],"灌溉","水资源",[],{"code":59,"score":20,"supported":14,"title":127,"abstract":128,"tags":129},[],[],[28,108,106],[131,132],"伊拉克 NDVI 植被动态","MODIS CHIRPS ERA5-Land 遥感","伊拉克NDVI植被动态-4756","10.66026\u002Fy7543b35",{"doi":134,"openalex_id":136,"authors":137,"venue":96,"cited_by_count":78,"oa_url":144,"card":145,"direction":84,"ingested_from":86},"W7221306668",[138,140,142],{"name":139,"orcid":9},"خولة‌ حمه‌رشید عمر",{"name":141,"orcid":9},"سایە دلاوەر محمد",{"name":143,"orcid":9},"Daban Kadhim Omar","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4819\u002F4866",{"tldr":146,"method":147,"finding":148,"direction":84,"opportunity":149},"基于2001–2025年MODIS NDVI、CHIRPS降水与ERA5-Land温度数据，评估伊拉","MODIS NDVI、CHIRPS降水、ERA5-Land温度，0.1°网格去趋","植被绿化趋势多于褐化，降水敏感性主导，温度负相关集中于北部、东部和东南部。","可结合灌溉与土地覆盖数据，区分雨养与灌溉区植被对水热胁迫的差异化响应机制。","2026-10-12T23:30:27.995466Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":166,"tags":168,"primary_topic":172,"secondary_topics":177,"classification":179,"search_phrases":207,"slug":210,"view_count":211,"doi":212,"paper":213,"created_at":230},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":17,"substance":161,"depth":162,"authority":163,"freshness":164,"relevant":21,"comment":165},21,17,13,9,"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[167],{"name":157,"url":154},[26,27,169,170,171],"遥感","土壤碳汇","数字土壤制图",{"code":173,"slug":174,"name":175,"path":176,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[178],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":180},{"candidates":181,"agricultural_evidence":45},[182,188,192,198,202],{"code":173,"score":183,"supported":45,"title":184,"abstract":185,"tags":187},14,[174],[186,174],"土壤",[],{"code":31,"score":13,"supported":45,"title":189,"abstract":190,"tags":191},[],[27,117,48],[27],{"code":193,"score":20,"supported":45,"title":194,"abstract":195,"tags":197},"J1",[],[196],"农业规划",[],{"code":59,"score":67,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[26,169],{"code":203,"score":21,"supported":14,"title":204,"abstract":205,"tags":206},"H4",[],[],[170],[208,209],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191",5,"10.3389\u002Fsjss.2026.16733",{"doi":212,"openalex_id":214,"authors":215,"venue":157,"cited_by_count":78,"oa_url":154,"card":224,"direction":229,"ingested_from":86},"W7213971196",[216,218,220,222],{"name":217,"orcid":9},"Faroug A.H. Jadalla",{"name":219,"orcid":9},"Kolapo O. Oluwasemire",{"name":221,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":223,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":225,"method":226,"finding":227,"direction":84,"opportunity":228},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":241,"tags":243,"primary_topic":247,"secondary_topics":253,"classification":261,"search_phrases":282,"slug":285,"view_count":78,"doi":286,"paper":287,"created_at":306},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",77,{"impact":19,"substance":101,"depth":19,"authority":100,"freshness":44,"relevant":21,"comment":240},"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。",[242],{"name":237,"url":234},[26,244,27,245,246],"机器学习","玉米生产","莫桑比克",{"code":248,"slug":249,"name":250,"path":251,"parent_code":252},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[254,260],{"code":255,"slug":256,"name":257,"path":258,"parent_code":259},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":262},{"candidates":263,"agricultural_evidence":45},[264,270,274,278],{"code":255,"score":265,"supported":45,"title":266,"abstract":268,"tags":269},15,[267],"machine learning",[244,267],[244],{"code":248,"score":183,"supported":45,"title":271,"abstract":272,"tags":273},[249],[250,249],[],{"code":31,"score":100,"supported":45,"title":275,"abstract":276,"tags":277},[48],[117],[27],{"code":59,"score":21,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[26],[283,284],"农业遥感 机器学习 气候变化 玉米生产","农业遥感 机器学习","农业遥感机器学习气候变化玉米生产-1616","10.3389\u002Ffsufs.2026.1889991",{"doi":286,"openalex_id":288,"authors":289,"venue":237,"cited_by_count":78,"oa_url":298,"card":299,"direction":305,"ingested_from":86},"W7207867677",[290,293,295],{"name":291,"orcid":292},"Faustino Dinis","https:\u002F\u002Forcid.org\u002F0000-0002-5192-0144",{"name":294,"orcid":9},"Jianbo Hu",{"name":296,"orcid":297},"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":300,"method":301,"finding":302,"direction":303,"opportunity":304},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","2026-09-04T23:30:08.552197Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":315,"score_detail":316,"sources":320,"tags":322,"primary_topic":327,"secondary_topics":332,"classification":334,"search_phrases":365,"slug":368,"view_count":78,"doi":369,"paper":370,"created_at":384},5569,"A four-decadal (1981–2024) quality-assured ground-based soybean phenology dataset from Northeast China: a FAIR-compliant resource for climate impact and agricultural adaptation studies","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735138","Although satellite remote sensing has been widely used to monitor land surface phenology, long-term, high-quality ground-based observations are essential for validating and calibrating these products, particularly for understanding agricultural ecosystem responses to climate change. However, such ground-truth datasets remain scarce, limiting the full potential of Earth system data applications. Here we present a comprehensive ground-based soybean phenology dataset for Northeast China, a region critical to national food security. The dataset integrates historical paper-based logs (1980s–1990s) with modern electronic records (2000s onwards) from 43 national agrometeorological stations operated by the China Meteorological Administration (CMA), spanning a 15° latitudinal gradient (38–53°N) across Heilongjiang, Jilin, and Liaoning provinces over 44 years (1981–2024). It provides the timing (day of year, DOY) of eight key phenophases (sowing, emergence, three-leaf, branching, flowering, pod formation, seed filling, maturity) and the durations of six intervening growth intervals. A multi-tiered quality assurance framework combining automated consistency checks, statistical outlier detection, and expert arbitration against agronomic principles and auxiliary climate data ensures high data integrity and completeness (>99%; paper era: 93.8%; electronic era: 98.1%). To facilitate reuse, we provide pre-computed kernel density estimates (probability distributions) and univariate linear regression trends for each station and phenological variable, along with 656 diagnostic plots. The dataset is openly available in the Science Data Bank under a CC BY 4.0 license as primary data tables (XLSX) and diagnostic plots (JPEG, 1200 dpi) (https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245). As a long-term, spatially explicit, FAIR-compliant ground-truth resource, this dataset is valuable for validating satellite-derived phenology products, calibrating crop modules, and investigating climate-driven impacts on agricultural systems.","尽管卫星遥感已被广泛用于监测地表物候，但长期、高质量的地面观测对于验证和校准这些产品至关重要，尤其是对于理解农业生态系统对气候变化的响应而言。然而，此类地面真值数据集仍然稀缺，限制了地球系统数据应用潜力的充分发挥。本文介绍了一套面向中国东北地区的综合性大豆物候地面数据集，该地区对国家粮食安全至关重要。该数据集整合了中国气象局（CMA）运营的43个国家农业气象站的历史纸质记录（20世纪80—90年代）与现代电子记录（2000年代至今），覆盖黑龙江、吉林和辽宁三省跨越15°纬度梯度（38—53°N）、时间跨度44年（1981—2024年）。数据集提供了8个关键物候期（播种、出苗、三叶、分枝、开花、结荚、鼓粒、成熟）的发生时间（年积日，DOY）以及6个中间生长间隔的持续时间。一套多层质量保证框架结合了自动一致性检查、统计异常值检测以及依据农学原理和辅助气候数据进行的专家仲裁，确保了高数据完整性和完整性（>99%；纸质时代：93.8%；电子时代：98.1%）。为便于重复使用，我们提供了每个站点和物候变量的预计算核密度估计（概率分布）和单变量线性回归趋势，以及656幅诊断图。该数据集以CC BY 4.0许可在Science Data Bank公开提供，包括主数据表（XLSX）和诊断图（JPEG，1200 dpi）（https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245）。作为一个长期、空间明确、符合FAIR原则的地面真值资源，该数据集对于验证卫星反演的物候产品、校准作物模块以及研究气候驱动对农业系统的影响具有重要价值。","Big Earth Data","2026-10-21T00:00:00Z",89,{"impact":101,"substance":317,"depth":318,"authority":183,"freshness":13,"relevant":21,"comment":319},24,19,"覆盖东北44年、43站的大豆物候地面观测数据集，质控严格且FAIR开放，对遥感验证与气候影响研究有实质价值，值得入选。",[321],{"name":313,"url":310},[323,27,324,325,326],"大豆","东北农业","物候数据","遥感验证",{"code":328,"slug":329,"name":330,"path":331,"parent_code":252},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[333],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":335},{"candidates":336,"agricultural_evidence":45},[337,342,347,354,359],{"code":328,"score":265,"supported":45,"title":338,"abstract":340,"tags":341},[339],"soybean",[323,339],[323],{"code":31,"score":13,"supported":45,"title":343,"abstract":344,"tags":346},[],[27,345,48],"农业气象",[27],{"code":348,"score":52,"supported":45,"title":349,"abstract":350,"tags":353},"A8",[],[351,352],"粮食安全","food security",[],{"code":59,"score":52,"supported":45,"title":355,"abstract":356,"tags":358},[],[169,357],"remote sensing",[],{"code":360,"score":20,"supported":45,"title":361,"abstract":362,"tags":364},"H6",[],[363],"农业生态",[],[366,367],"东北 大豆 物候 数据集","大豆 物候 遥感 验证","东北大豆物候数据集-5569","10.1080\u002F20964471.2026.2735138",{"doi":369,"openalex_id":371,"authors":372,"venue":313,"cited_by_count":78,"oa_url":310,"card":379,"direction":84,"ingested_from":86},"W7223091615",[373,376],{"name":374,"orcid":375},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":377,"orcid":378},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":380,"method":381,"finding":382,"direction":84,"opportunity":383},"发布东北大豆1981–2024年44年地面物候数据集，含8个物候期与6个间隔期。","整合43个农气站纸质与电子记录，经自动校验、统计离群检测和专家仲裁质控。","数据完整度超99%，并提供核密度分布、线性趋势与656张诊断图。","可基于该地面真值校准遥感物候产品与作物模型，量化气候变化对大豆物候的影响。","2026-10-22T11:20:39.146445Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":9,"content":9,"source_name":390,"source_url":9,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":401,"secondary_topics":406,"classification":412,"search_phrases":434,"slug":437,"view_count":21,"doi":9,"paper":438,"created_at":446},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":19,"substance":101,"depth":19,"authority":183,"freshness":13,"relevant":21,"comment":393},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[395],{"name":390,"url":388},[397,26,398,399,400],"智慧农业","作物表型","无人机多光谱","小麦渍害",{"code":402,"slug":403,"name":404,"path":405,"parent_code":252},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[407,411],{"code":59,"slug":408,"name":409,"path":410,"parent_code":259},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":413},{"candidates":414,"agricultural_evidence":45},[415,420,424,429],{"code":402,"score":416,"supported":45,"title":417,"abstract":418,"tags":419},11,[404],[404],[],{"code":59,"score":416,"supported":45,"title":421,"abstract":422,"tags":423},[399],[],[398,26,399],{"code":31,"score":17,"supported":45,"title":425,"abstract":427,"tags":428},[426],"渍害",[],[],{"code":430,"score":21,"supported":14,"title":431,"abstract":432,"tags":433},"I1",[],[],[397],[435,436],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":439,"venue":9,"cited_by_count":78,"oa_url":9,"card":440,"direction":84,"ingested_from":445},[],{"tldr":441,"method":442,"finding":443,"direction":84,"opportunity":444},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":465,"classification":471,"search_phrases":520,"slug":523,"view_count":78,"doi":524,"paper":525,"created_at":548},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":163,"substance":19,"depth":265,"authority":100,"freshness":17,"relevant":21,"comment":457},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[459],{"name":453,"url":450},[27,461,106,462,463],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[466,467],{"code":173,"slug":174,"name":175,"path":176,"parent_code":35},{"code":120,"slug":468,"name":469,"path":470,"parent_code":35},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":38,"locked":14,"version":39,"method":40,"evidence":472},{"candidates":473,"agricultural_evidence":45},[474,479,483,488,492,496,502,509,515],{"code":31,"score":19,"supported":45,"title":475,"abstract":476,"tags":478},[48],[27,477,117,48],"气候适应",[27],{"code":173,"score":265,"supported":45,"title":480,"abstract":481,"tags":482},[174],[186,174],[461],{"code":120,"score":13,"supported":45,"title":484,"abstract":485,"tags":487},[],[123,124,468,486],"evapotranspiration",[462],{"code":59,"score":44,"supported":45,"title":489,"abstract":490,"tags":491},[],[169,357],[106],{"code":348,"score":52,"supported":45,"title":493,"abstract":494,"tags":495},[],[351,352],[],{"code":430,"score":52,"supported":45,"title":497,"abstract":498,"tags":501},[],[499,500],"精准农业","precision agriculture",[],{"code":503,"score":52,"supported":45,"title":504,"abstract":505,"tags":508},"I4",[],[506,507],"地理信息","geographic information",[],{"code":510,"score":20,"supported":45,"title":511,"abstract":512,"tags":514},"F3",[],[513],"养分管理",[],{"code":193,"score":20,"supported":45,"title":516,"abstract":517,"tags":519},[],[518],"农业政策",[],[521,522],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":524,"openalex_id":526,"authors":527,"venue":453,"cited_by_count":78,"oa_url":450,"card":543,"direction":84,"ingested_from":86},"W7222999158",[528,530,532,534,536,538,541],{"name":529,"orcid":9},"Asifa Naz",{"name":531,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":533,"orcid":9},"Syed Asghar Hussain Shah",{"name":535,"orcid":9},"Shahrish Nawaz",{"name":537,"orcid":9},"Muhmmad Irfan",{"name":539,"orcid":540},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":542,"orcid":9},"Muhammad Nouman Khalid",{"tldr":544,"method":545,"finding":546,"direction":84,"opportunity":547},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z"]