[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4086":3,"related-4086":83},{"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":39,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":82},4086,"Drought, conflict, and recovery: Disentangling the drivers of vegetation dynamics and surface temperature change in Kirkuk Governorate, Iraq (2000-2024)","https:\u002F\u002Fdoi.org\u002F10.15243\u002Fjdmlm.2026.134.10955","This study presents a 24-year spatiotemporal analysis of vegetation dynamics and land surface temperature (LST) in Kirkuk Governorate, northern Iraq, from 2000 to 2024. MODIS NDVI (MOD13Q1), MODIS LST (MOD11A2), and CHIRPS rainfall data were processed in Google Earth Engine to assess environmental change across four phases: baseline (2000-2005), pre-conflict (2010-2013), ISIS conflict (2014-2017), and post-conflict recovery (2018-2024). Vegetation trends were evaluated using the Mann-Kendall test and Sen's Slope estimator. Mean annual NDVI increased by 21.6%, from 0.1995 during the baseline phase to 0.2426 during the recovery phase. Significant greening occurred across 56.2% of the governorate (5,470 km²), while only 0.8% (78 km²) experienced browning. Two major periods of vegetation decline were identified: the 2007-2009 regional drought, when mean NDVI fell to 0.1397, and the 2014-2017 ISIS conflict, during which vegetation declined in western districts despite near-average rainfall, indicating conflict as an independent driver of degradation. Mean annual LST decreased from 38.37 °C in 2000 to 32.33 °C in 2024, consistent with the inverse relationship between vegetation cover and surface temperature. The findings provide quantitative evidence of conflict-driven vegetation degradation followed by post-conflict ecological recovery, offering valuable insights for land management and agricultural recovery planning in semi-arid, post-conflict regions.","本研究对伊拉克北部基尔库克省2000年至2024年间的植被动态与地表温度（LST）进行了24年时空分析。在Google Earth Engine中处理了MODIS NDVI（MOD13Q1）、MODIS LST（MOD11A2）和CHIRPS降雨数据，以评估四个阶段的环境变化：基线期（2000—2005年）、冲突前期（2010—2013年）、ISIS冲突期（2014—2017年）和冲突后恢复期（2018—2024年）。植被趋势采用Mann-Kendall检验和Sen斜率估计法进行评估。年均NDVI增加了21.6%，从基线期的0.1995上升至恢复期的0.2426。显著绿化面积占全省的56.2%（5,470 km²），而仅0.8%（78 km²）出现褐化。研究识别出两个主要的植被退化时期：2007—2009年区域性干旱期间，平均NDVI降至0.1397；以及2014—2017年ISIS冲突期间，尽管降雨量接近平均水平，西部地区的植被仍然退化，表明冲突是植被退化的独立驱动因素。年均LST从2000年的38.37 °C降至2024年的32.33 °C，与植被覆盖和地表温度之间的反比关系一致。研究结果提供了冲突驱动植被退化及冲突后生态恢复的定量证据，为半干旱冲突后地区的土地管理和农业恢复规划提供了有价值的参考。",null,"Journal of Degraded and Mining Lands Management","2026-10-01T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,22,18,13,9,1,"基于MODIS与CHIRPS的24年遥感长时序分析，量化干旱与冲突对植被和地表温度的影响，方法规范、数据扎实，对半干旱战后地区农业恢复规划有参考价值，但地域性强、与国内三农信息化关联有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","地表温度","植被动态","干旱","战后农业恢复",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},14,true,[49],"drought",[30,49],[],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I3",3,[],[],[28,29,27],[59,60],"基尔库克 植被动态 遥感","MODIS NDVI 地表温度","基尔库克植被动态遥感-4086",2,"10.15243\u002Fjdmlm.2026.134.10955",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":73,"oa_url":74,"card":75,"direction":79,"ingested_from":81},"W7214972947",[67,70],{"name":68,"orcid":69},"Azhar Hussein Razuqi","https:\u002F\u002Forcid.org\u002F0009-0002-1202-5953",{"name":71,"orcid":72},"Mustafa Albdairi","https:\u002F\u002Forcid.org\u002F0009-0002-6673-363X",0,"https:\u002F\u002Fjdmlm.ub.ac.id\u002Findex.php\u002Fjdmlm\u002Farticle\u002Fdownload\u002F17867\u002F2159",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"分析伊拉克基尔库克2000-2024年植被与地表温度动态，区分干旱与冲突的驱动作用。","MODIS NDVI\u002FLST与CHIRPS降雨数据，在GEE中用Mann-Ken","NDVI总体增21.6%，干旱和ISIS冲突分别导致植被下降，冲突是独立退化驱动因素。","农业遥感与作物表型","可结合更高分辨率遥感与地面调查，量化冲突后农业恢复力及气候适应策略。","openalex","2026-10-03T23:30:32.505144Z",{"total":84,"page":22,"page_size":84,"items":85},6,[86,143,263,324,386,446],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":99,"tags":101,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":118,"slug":121,"view_count":54,"doi":122,"paper":123,"created_at":142},3072,"APPLICATION OF SENTINEL-2 DATA TO ASSESS SEASONAL VEGETATION DYNAMICS IN THE IALPUG RIVER BASIN, SOUTHERN PART OF THE REPUBLIC OF MOLDOVA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.06","This study uses Sentinel-2 imagery to characterize seasonal vegetation dynamics in the analysed part of the Ialpug River Basin from March to October 2025.Monthly NDVI, month-to-month NDVI differences (ΔNDVI) and mean NDVI were calculated to describe vegetation greenness, the direction and magnitude of change between observations, and average greenness across the study period.The results show spring greening followed by spatially contrasting changes in early summer.Moderate vegetation cover occupied its largest share in April, while dense vegetation reached its maximum extent in June.The most widespread decline occurred between June and July, when 54.18% of the study area recorded an NDVI decrease exceeding 0.1.Very sparse vegetation reached its greatest extent in September, followed by limited greening in October.The mean NDVI classification placed 69.20% of the study area in the sparse vegetation class and 24.49% in the very sparse vegetation class, indicating predominantly low to intermediate average greenness across the selected observations.These classes do not establish persistent vegetation conditions or directly identify land-cover types.The summer decline coincided with documented regional drought conditions, but the analysis did not separate climatic effects from crop development, harvesting or other seasonal processes.The combined indicators provide complementary information for identifying areas with pronounced seasonal decline or low average greenness.Multi-year observations and independent climatic, land-cover and field evidence are required to determine the causes of these patterns and assess their longer-term environmental significance.","本研究利用Sentinel-2影像刻画了雅尔普格河（Ialpug River）流域分析区2025年3月至10月的季节性植被动态。通过计算逐月NDVI、月际NDVI差值（ΔNDVI）及平均NDVI，以描述植被绿度、观测间变化的方向与幅度以及研究期内的平均绿度。结果表明，春季出现绿化，随后在初夏呈现空间分异的变化。中度植被覆盖在4月占比最大，而茂密植被在6月达到最大范围。最广泛的下降发生在6月至7月之间，届时研究区54.18%的面积NDVI下降超过0.1。极稀疏植被在9月达到最大范围，随后在10月出现有限绿化。平均NDVI分类将研究区69.20%的面积归为稀疏植被类，24.49%归为极稀疏植被类，表明所选观测期内平均绿度以低至中等为主。这些类别并不确立持续的植被状况，也不直接识别土地覆盖类型。夏季下降与已记录的区域干旱条件相吻合，但分析未将气候影响与作物发育、收割或其他季节性过程区分开来。综合指标为识别季节性下降显著或平均绿度较低的区域提供了互补信息。要确定这些格局的成因并评估其长期环境意义，尚需多年观测以及独立的气候、土地覆盖和野外证据。","Geographia Technica","2026-09-19T00:00:00Z",61,{"impact":17,"substance":19,"depth":96,"authority":97,"freshness":17,"relevant":22,"comment":98},15,12,"基于Sentinel-2的流域植被季节性动态遥感研究，方法规范、数据翔实，但属区域性案例，对国内农业信息化仅有方法借鉴价值。",[100],{"name":92,"url":89},[102,27,29,103,104],"NDVI","干旱影响","摩尔多瓦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":108},{"candidates":109,"agricultural_evidence":47},[110,114],{"code":33,"score":84,"supported":47,"title":111,"abstract":112,"tags":113},[],[30,49],[],{"code":53,"score":54,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[102,29,27],[119,120],"摩尔多瓦 NDVI 植被动态","干旱影响 摩尔多瓦 植被动态 遥感监测","摩尔多瓦NDVI植被动态-3072","10.21163\u002Fgt_2027.221.06",{"doi":122,"openalex_id":124,"authors":125,"venue":92,"cited_by_count":73,"oa_url":136,"card":137,"direction":79,"ingested_from":81},"W7213674461",[126,129,131,134],{"name":127,"orcid":128},"Ecaterina MELNICENCO","https:\u002F\u002Forcid.org\u002F0000-0002-2706-9417",{"name":130,"orcid":9},"Viorica ANGHELUȚA",{"name":132,"orcid":133},"Iradion Jechiu","https:\u002F\u002Forcid.org\u002F0000-0001-9582-0615",{"name":135,"orcid":9},"Iurie BEJAN","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F06_melnicenco.pdf",{"tldr":138,"method":139,"finding":140,"direction":79,"opportunity":141},"用Sentinel-2影像分析摩尔多瓦Ialpug河流域2025年3-10月植被季节动态。","Sentinel-2月度NDVI、ΔNDVI与均值NDVI分类，覆盖2025年3","6-7月54.18%区域NDVI降幅超0.1，均值NDVI以稀疏植被为主（69.20%）。","可结合多年遥感与气候、物候及田间数据，分离干旱与作物管理对NDVI下降的贡献。","2026-09-21T23:30:25.567291Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":9,"source_name":149,"source_url":146,"published_at":150,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":152,"sources":154,"tags":156,"primary_topic":161,"secondary_topics":162,"classification":173,"search_phrases":234,"slug":237,"view_count":73,"doi":238,"paper":239,"created_at":262},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":20,"substance":19,"depth":96,"authority":97,"freshness":17,"relevant":22,"comment":153},"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。",[155],{"name":149,"url":146},[157,158,27,159,160],"气候变化","盐碱地治理","智慧灌溉","土壤盐渍化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[163,168],{"code":164,"slug":165,"name":166,"path":167,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":169,"slug":170,"name":171,"path":172,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":174},{"candidates":175,"agricultural_evidence":47},[176,182,187,194,201,208,215,222,228],{"code":33,"score":19,"supported":47,"title":177,"abstract":179,"tags":181},[178],"climate change",[157,180,30,178],"气候适应",[157],{"code":164,"score":96,"supported":47,"title":183,"abstract":184,"tags":186},[165],[185,165],"土壤",[158],{"code":169,"score":13,"supported":47,"title":188,"abstract":189,"tags":193},[],[190,191,170,192],"灌溉","水资源","evapotranspiration",[159],{"code":53,"score":195,"supported":47,"title":196,"abstract":197,"tags":200},7,[],[198,199],"遥感","remote sensing",[27],{"code":202,"score":84,"supported":47,"title":203,"abstract":204,"tags":207},"A8",[],[205,206],"粮食安全","food security",[],{"code":209,"score":84,"supported":47,"title":210,"abstract":211,"tags":214},"I1",[],[212,213],"精准农业","precision agriculture",[],{"code":216,"score":84,"supported":47,"title":217,"abstract":218,"tags":221},"I4",[],[219,220],"地理信息","geographic information",[],{"code":223,"score":54,"supported":47,"title":224,"abstract":225,"tags":227},"F3",[],[226],"养分管理",[],{"code":229,"score":54,"supported":47,"title":230,"abstract":231,"tags":233},"J1",[],[232],"农业政策",[],[235,236],"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":238,"openalex_id":240,"authors":241,"venue":149,"cited_by_count":73,"oa_url":146,"card":257,"direction":79,"ingested_from":81},"W7222999158",[242,244,246,248,250,252,255],{"name":243,"orcid":9},"Asifa Naz",{"name":245,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":247,"orcid":9},"Syed Asghar Hussain Shah",{"name":249,"orcid":9},"Shahrish Nawaz",{"name":251,"orcid":9},"Muhmmad Irfan",{"name":253,"orcid":254},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":256,"orcid":9},"Muhammad Nouman Khalid",{"tldr":258,"method":259,"finding":260,"direction":79,"opportunity":261},"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",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":271,"sources":273,"tags":275,"primary_topic":279,"secondary_topics":284,"classification":290,"search_phrases":306,"slug":309,"view_count":73,"doi":310,"paper":311,"created_at":323},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":272},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[274],{"name":269,"url":266},[276,27,277,278],"防灾减灾","洪水风险评估","GIS应用",{"code":53,"slug":280,"name":281,"path":282,"parent_code":283},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[285,289],{"code":216,"slug":286,"name":287,"path":288,"parent_code":283},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":291},{"candidates":292,"agricultural_evidence":47},[293,297,302],{"code":53,"score":96,"supported":47,"title":294,"abstract":295,"tags":296},[199],[198,199],[27],{"code":216,"score":46,"supported":47,"title":298,"abstract":300,"tags":301},[299],"GIS",[219,299],[],{"code":33,"score":17,"supported":47,"title":303,"abstract":304,"tags":305},[],[30,49],[277,276],[307,308],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":310,"openalex_id":312,"authors":313,"venue":269,"cited_by_count":73,"oa_url":317,"card":318,"direction":79,"ingested_from":81},"W7222885988",[314],{"name":315,"orcid":316},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":319,"method":320,"finding":321,"direction":79,"opportunity":322},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":332,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":342,"classification":343,"search_phrases":358,"slug":361,"view_count":73,"doi":362,"paper":363,"created_at":385},5172,"Forest green-up in the Pearl River Basin: trends, driving factors, and relationships with net primary productivity","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2738487","The spring green-up date (GUD), a critical indicator of ecosystem response to climate change, profoundly influences terrestrial productivity. However, the complex drivers of GUD remain elusive, while the contentious relationship between GUD and net primary productivity (NPP) challenges the universal validity of the hypothesis that earlier green-up enhances productivity. Using MODIS NDVI data (2000-2020), we quantified forest GUD dynamics in the Pearl River Basin and combined random forest, SHAP, and structural equation modelling to identify drivers and assess GUD-NPP relationships. GUD was advanced by approximately 5 days, with 81.8% of forestland pixels advancing and 18.2% delaying. Only 5.39% of advances showed significant trends, whereas 30.45% of delays were concentrated near croplands and developed areas. The random forest model explained 58.2% of GUD variation. Elevation, precipitation, and soil potassium and phosphorus were dominant; elevation-induced thermal gradients outweighed ambient temperature effects, with elevations above 1,200 m exhibiting markedly delayed GUD. Spatially, GUD had a positive correlation with NPP, indicating that later GUD was associated with higher forest NPP, possibly because of early-spring cold snaps and frost. Temporally, areas with stronger GUD delays tended to show greater NPP increases. This study identified key controls on subtropical forest phenology and revealed a GUD-NPP trade-off.","春季返青期（GUD）是生态系统响应气候变化的关键指标，深刻影响陆地生产力。然而，GUD的复杂驱动机制仍不明确，且GUD与净初级生产力（NPP）之间存在争议性关系，挑战了“提前返青提高生产力”这一假说的普适性。本研究利用MODIS NDVI数据（2000—2020年），量化了珠江流域森林GUD动态，并结合随机森林、SHAP和结构方程模型识别驱动因子并评估GUD-NPP关系。GUD整体提前约5天，其中81.8%的林地像元提前、18.2%推迟。仅5.39%的提前像元呈显著趋势，而30.45%的推迟像元集中在农田和建成区附近。随机森林模型解释了58.2%的GUD变异。海拔、降水以及土壤钾和磷为主导因子；海拔引起的热梯度效应超过环境温度的影响，海拔1200 m以上区域GUD显著推迟。空间上，GUD与NPP呈正相关，表明GUD越晚森林NPP越高，这可能与早春寒潮和霜冻有关。时间上，GUD推迟越强的区域往往表现为NPP增加越大。本研究识别了亚热带森林物候的关键控制因子，揭示了GUD-NPP之间的权衡关系。","International Journal of Digital Earth","2026-10-15T00:00:00Z",74,{"impact":97,"substance":18,"depth":19,"authority":46,"freshness":17,"relevant":22,"comment":334},"基于MODIS长时序数据的区域森林物候研究，方法组合新颖、结论有增量，但属区域基础研究，产业与政策影响有限，可作专业补充而非每日精选头条。",[336],{"name":330,"url":327},[157,27,338,339,340],"植被生产力","森林物候","珠江流域",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":344},{"candidates":345,"agricultural_evidence":47},[346,350,354],{"code":33,"score":195,"supported":47,"title":347,"abstract":348,"tags":349},[],[157,178],[157],{"code":164,"score":84,"supported":47,"title":351,"abstract":352,"tags":353},[],[185,165],[],{"code":53,"score":22,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[27],[359,360],"珠江流域 森林 返青期","MODIS NDVI 植被生产力","珠江流域森林返青期-5172","10.1080\u002F17538947.2026.2738487",{"doi":362,"openalex_id":364,"authors":365,"venue":330,"cited_by_count":73,"oa_url":379,"card":380,"direction":79,"ingested_from":81},"W7221897424",[366,369,371,373,375,377],{"name":367,"orcid":368},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":370,"orcid":9},"Zhaoju Zheng",{"name":372,"orcid":9},"Xueming Zhao",{"name":374,"orcid":9},"Zhipeng Zhang",{"name":376,"orcid":9},"Xiaohui Cui",{"name":378,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":381,"method":382,"finding":383,"direction":79,"opportunity":384},"基于MODIS NDVI量化珠江流域森林返青期动态，并解析其驱动因子及与NPP的关系。","MODIS NDVI（2000-2020）、随机森林、SHAP与结构方程模型。","返青期整体提前约5天，但延迟区NPP更高，揭示返青期与NPP的权衡关系。","可探究早春寒潮霜冻对返青-NPP权衡的调控机制，并拓展至其他亚热带流域验证普适性。","2026-10-17T23:30:29.750744Z",{"id":387,"title":388,"url":389,"summary":390,"summary_zh":391,"content":9,"source_name":392,"source_url":389,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":393,"sources":397,"tags":399,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":427,"slug":430,"view_count":22,"doi":431,"paper":432,"created_at":445},5087,"Method for ten-day assessment of the contribution of agrometeorological conditions to expected winter grain crop productivity based on satellite and ground-based information","https:\u002F\u002Fdoi.org\u002F10.37162\u002F2618-9631-2026-3-167-183","The paper presents a developed and tested method for 10-day assessment of the contribution of agrometeorological factors to winter grain crop productivity. The method is based on an integrated analysis of satellite spectral indices and ground-based meteorological data for the North Caucasus, Volga and Central Black Earth regions. Satellite data from MODIS (Terra satellite) and VIIRS (SUOMI NPP satellite) radiometers were used to calculate the NDVI, VCI, and VCNI vegetation indices. Ground-based information includes meteorological and agrometeorological indicators reflecting temperature and humidity conditions. The algorithm and software modules developed within the study allow for automated tracking of 10-day changes in agrometeorological conditions and quantitative ranking of factors based on their contribution to the expected winter grain crop yield, providing an information basis for informed planning of agricultural measures.","本文提出并验证了一种用于每旬评估农业气象因素对冬粮作物生产力贡献的方法。该方法基于对北高加索、伏尔加河沿岸和中央黑土区卫星光谱指数与地面气象数据的综合分析。利用MODIS（Terra卫星）和VIIRS（SUOMI NPP卫星）辐射计的卫星数据计算NDVI、VCI和VCNI植被指数。地面信息包括反映温湿状况的气象和农业气象指标。研究开发的算法和软件模块能够自动跟踪农业气象条件的每旬变化，并根据各因素对预期冬粮作物产量的贡献进行定量排序，为农业措施的合理规划提供信息依据。","Hydrometeorological research and forecasting",{"impact":97,"substance":394,"depth":395,"authority":20,"freshness":17,"relevant":22,"comment":396},20,17,"方法新颖、数据扎实的区域性遥感估产研究，对农业信息化有参考价值，但属细分领域学术成果，公共影响有限。",[398],{"name":392,"url":389},[400,401,27,402,403],"智慧农业","农业气象","冬小麦","作物估产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":407},{"candidates":408,"agricultural_evidence":47},[409,414,418,423],{"code":33,"score":410,"supported":47,"title":411,"abstract":412,"tags":413},4,[],[401],[401],{"code":53,"score":62,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[403,27],{"code":419,"score":22,"supported":14,"title":420,"abstract":421,"tags":422},"A2",[],[],[402],{"code":209,"score":22,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[400],[428,429],"MODIS VIIRS 冬小麦 遥感估产","农业气象 冬谷类作物 产量评估","MODISVIIRS冬小麦遥感估产-5087","10.37162\u002F2618-9631-2026-3-167-183",{"doi":431,"openalex_id":433,"authors":434,"venue":392,"cited_by_count":73,"oa_url":389,"card":440,"direction":79,"ingested_from":81},"W7221991562",[435,438],{"name":436,"orcid":437},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":439,"orcid":9},"O. V. Savitskaya",{"tldr":441,"method":442,"finding":443,"direction":79,"opportunity":444},"提出一种基于卫星与地面数据每十天评估农业气象因子对冬粮作物产量贡献的方法。","用MODIS和VIIRS计算NDVI、VCI、VCNI，结合气象与农业气象指标做","可自动追踪十天农业气象变化并量化排序各因子对预期产量的贡献。","可扩展至多作物多区域，融合机器学习提升因子贡献动态预测精度。","2026-10-16T23:30:26.817010Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":453,"sources":456,"tags":458,"primary_topic":461,"secondary_topics":466,"classification":468,"search_phrases":513,"slug":516,"view_count":22,"doi":517,"paper":518,"created_at":530},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":19,"substance":454,"depth":96,"authority":97,"freshness":21,"relevant":22,"comment":455},16,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[457],{"name":452,"url":449},[400,459,460,157,27],"农业人工智能","可持续发展",{"code":462,"slug":463,"name":464,"path":465,"parent_code":283},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[467],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":469},{"candidates":470,"agricultural_evidence":47},[471,480,484,488,492,499,506],{"code":462,"score":19,"supported":47,"title":472,"abstract":474,"tags":479},[473],"artificial intelligence",[475,476,477,478,473],"深度学习","机器学习","machine learning","deep learning",[459],{"code":33,"score":96,"supported":47,"title":481,"abstract":482,"tags":483},[178],[157,178],[157],{"code":209,"score":195,"supported":47,"title":485,"abstract":486,"tags":487},[],[212,213],[400],{"code":53,"score":195,"supported":47,"title":489,"abstract":490,"tags":491},[],[198,199],[27],{"code":493,"score":84,"supported":47,"title":494,"abstract":495,"tags":498},"H4",[],[496,497],"碳排放","carbon emission",[],{"code":500,"score":84,"supported":47,"title":501,"abstract":502,"tags":505},"H6",[],[503,504],"生物多样性","biodiversity",[],{"code":507,"score":84,"supported":47,"title":508,"abstract":509,"tags":512},"I5",[],[510,511],"物联网","internet of things",[],[514,515],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":517,"openalex_id":519,"authors":520,"venue":452,"cited_by_count":73,"oa_url":449,"card":523,"direction":529,"ingested_from":81},"W7222092855",[521],{"name":522,"orcid":9},"Deepmala Suryavanshi",{"tldr":524,"method":525,"finding":526,"direction":527,"opportunity":528},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","2026-10-16T23:30:15.059867Z"]