[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2525":3,"related-2525":103},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":21,"doi":71,"paper":72,"created_at":102},2525,"Greening of the Taklamakan Desert: Effects of climate change and ecological water conveyance","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115510","Oases in the Taklamakan Desert exist in an extremely arid region. Persistent droughts in the late 20th century nearly caused ecosystem collapse in the Lower Tarim River region. Since the implementation of ecological water conveyance in 2000, groundwater levels have increased, vegetation has flourished, and a marked “greening” trend has occurred throughout the oases. However, a quantitative analysis of the drivers of these changes is lacking. This study integrates multi-source remote sensing data and field monitoring, using random forest modeling and Geographical Detector to monitor shifts in vegetation cover and their causes in the lower Tarim River and surrounding oases since 2000. In this period, groundwater has increased by approximately 0.07 m·yr −1 , surface-water areas have expanded, and regional air temperatures, particularly in winter, have increased markedly. Oasis vegetation cover has also increased significantly, while cropland has expanded markedly. Although natural vegetation has generally improved, cropland growth has encroached on desert riparian forests, intensifying interspecific competition within these communities and increasing potential ecological risks. Moreover, groundwater recovery and cold-season warming are key influencing factors associated with oasis greening, while recurrent droughts and interactions between climatic and hydrological variables further shape vegetation growth. Cumulatively, this study shows that effective water management supports oasis greening in arid areas, while highlighting the ecological risks of over-farming. Overall, this study provides new insights into how arid ecosystems respond to coupled climatic and anthropogenic drivers, and offers scientific support for ecological water-delivery regulation, agricultural water-use optimization, and riparian habitat conservation.","塔克拉玛干沙漠中的绿洲处于极端干旱区。20世纪后期的持续干旱几乎导致塔里木河下游地区生态系统崩溃。自2000年实施生态输水以来，地下水位上升，植被恢复，整个绿洲呈现出明显的“绿化”趋势。然而，对这些变化驱动因素的定量分析仍然缺乏。本研究整合多源遥感数据和野外监测，采用随机森林模型和地理探测器，监测2000年以来塔里木河下游及周边绿洲植被覆盖的变化及其成因。在此期间，地下水以约0.07 m·yr⁻¹的速率上升，地表水面积扩大，区域气温尤其是冬季气温显著升高。绿洲植被覆盖度也显著增加，同时耕地面积明显扩张。尽管自然植被总体改善，但耕地扩张侵占了荒漠河岸林，加剧了群落内种间竞争，增加了潜在生态风险。此外，地下水恢复和冷季增温是与绿洲绿化相关的关键影响因素，而反复出现的干旱以及气候与水文变量之间的交互作用进一步影响植被生长。总体而言，本研究表明有效的水资源管理支持干旱区绿洲绿化，同时凸显了过度耕作带来的生态风险。综上，本研究为理解干旱生态系统如何响应气候与人为驱动因素的耦合作用提供了新见解，并为生态输水调控、农业用水优化和河岸生境保护提供了科学支撑。",null,"Ecological Indicators","2026-09-14T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于多源遥感与随机森林模型量化塔克拉玛干绿洲绿化驱动因素，结论对干旱区生态输水与农业用水优化有参考价值，但属区域研究，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","生态输水","荒漠绿洲","地下水恢复","农业用水",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,60],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},11,true,[48],"climate change",[50],"干旱",[],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"I3",7,[],[57,58],"遥感","remote sensing",[26],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":66},"H3",3,[],[65],"水资源",[],[68,69],"地下水恢复 农业用水 生态输水 荒漠绿洲","地下水恢复 农业用水","地下水恢复农业用水生态输水荒漠绿洲-2525","10.1016\u002Fj.ecolind.2026.115510",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":99,"ingested_from":101},"W7137834209",[75,77,80,83,85,87,89,91],{"name":76,"orcid":9},"Dinghao Li",{"name":78,"orcid":79},"Yasenjiang Kahaer","https:\u002F\u002Forcid.org\u002F0000-0002-2624-8317",{"name":81,"orcid":82},"Zhentao Dong","https:\u002F\u002Forcid.org\u002F0000-0003-4646-6079",{"name":84,"orcid":9},"Ruijia Liu",{"name":86,"orcid":9},"Anwar Abdureyim",{"name":88,"orcid":9},"Yulong Liu",{"name":90,"orcid":9},"Yanbo Wan",{"name":92,"orcid":93},"Qingdong Shi","https:\u002F\u002Forcid.org\u002F0000-0001-7369-6393",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"整合遥感与实地监测，量化塔克拉玛干绿洲2000年以来绿化驱动因素及生态风险。","多源遥感与实地监测，随机森林建模和地理探测器分析。","地下水回升与冬季增温驱动绿洲绿化，但耕地扩张侵占荒漠河岸林，生态风险上升。","农业遥感与作物表型","可延伸研究生态输水与农业用水优化协同调控，量化耕地扩张对河岸林生态风险的阈值。","openalex","2026-09-15T23:30:20.256810Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,174,288,350,412,472],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":10,"source_url":109,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":127,"search_phrases":144,"slug":147,"view_count":148,"doi":149,"paper":150,"created_at":173},3898,"Climate change and human activities reshape the water-food-ecology nexus and water-economy decoupling in oasis cities of the Hexi corridor, China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115599","In arid oasis systems, water allocation links agricultural production, ecological stability, and regional economic development, making the water-food-ecology (WFE) nexus highly sensitive to hydroclimatic stress and human activities. Yet county-scale evidence remains limited on how hydroclimatic and socioeconomic changes are associated with WFE interactions and water-economy decoupling in inland oasis cities. Focusing on the Hexi Corridor, we integrate multi-decadal hydroclimatic records, remote sensing ecological indicators, and county-level socioeconomic, water use, and crop data. Tapio decoupling, temporally purged and spatially blocked random-forest validation, Grey relational analysis (GRA), exploratory two-way fixed-effects piecewise path analysis, and scenario-based reliability-resilience-vulnerability assessment were combined to characterize water-economy relationships, crop–water associations, WFE interactions, and management outcomes. During 1981–2024, annual mean 2-m air temperature increased significantly by 0.038 °C\u002Fyear, annual precipitation showed a weak increase of 0.349 mm\u002Fyear, and December SPEI-12 decreased significantly by 0.0476\u002Fyear, indicating a deteriorating climatic water balance under pronounced warming. Strong water-economy decoupling emerged before 2020 and was associated with declining water use intensity, while industrial decoupling showed greater interannual variability during 2020–2022. Random-forest performance was moderate under temporal validation (R 2 = 0.673) and weaker under spatial validation (R 2 = 0.441), indicating stronger temporal generalization than spatial transferability. Crop composition showed pronounced spatial heterogeneity and county-specific similarity with agricultural water use. Piecewise path analysis revealed scale-dependent associations among socioeconomic activity, water use, hydroclimatic conditions, and vegetation. Within counties, land surface temperature (LST) was negatively associated with normalized difference vegetation index (NDVI), while surface water use was positively associated with vegetation condition and SPEI-12 was negatively associated with agricultural water use in the primary specification. Scenario results showed that agricultural-demand reduction and coordinated measures improved water-system reliability and resilience, whereas groundwater constraints applied alone increased unmet demand. These findings support differentiated local implementation within basin-scale governance and provide a transferable diagnostic workflow for comparable arid inland basins, although local parameters and management outcomes require region-specific recalibration.","在干旱绿洲系统中，水资源分配将农业生产、生态稳定与区域经济发展联系起来，使水—粮食—生态（WFE）纽带对水文气候胁迫和人类活动高度敏感。然而，关于内陆绿洲城市水文气候和社会经济变化如何与WFE相互作用及水—经济脱钩相关联的县域尺度证据仍然有限。本研究以河西走廊为对象，整合了多年代水文气候记录、遥感生态指标以及县级社会经济、用水和作物数据，综合运用Tapio脱钩模型、时间净化与空间分块的随机森林验证、灰色关联分析（GRA）、探索性双向固定效应分段路径分析以及基于情景的可靠性—恢复力—脆弱性评估，刻画水—经济关系、作物—水关联、WFE相互作用及管理成效。1981—2024年间，年平均2 m气温显著上升，速率为0.038 °C\u002F年；年降水量呈微弱增加趋势，为0.349 mm\u002F年；12月SPEI-12显著下降，速率为0.0476\u002F年，表明在显著增暖背景下气候水分平衡趋于恶化。2020年前出现了较强的水—经济脱钩，并与用水强度下降相关，而2020—2022年间产业脱钩表现出更大的年际变率。随机森林在时间验证下表现中等（R² = 0.673），在空间验证下表现较弱（R² = 0.441），表明其时间泛化能力强于空间可迁移性。作物组成表现出显著的空间异质性，并与农业用水存在县域特定的相似性。分段路径分析揭示了社会经济活动、用水、水文气候条件与植被之间尺度依赖的关联。在县域内部，地表温度（LST）与归一化植被指数（NDVI）呈负相关，而地表用水与植被状况呈正相关，且在基准设定中SPEI-12与农业用水呈负相关。情景结果表明，农业需求削减和协同措施提高了水系统的可靠性和恢复力，而单独实施地下水约束则增加了未满足需求。这些发现支持在流域尺度治理框架下进行差异化的地方实施。","2026-09-30T00:00:00Z",78,{"impact":115,"substance":18,"depth":17,"authority":116,"freshness":117,"relevant":21,"comment":118},16,13,9,"基于多源数据与多种方法揭示河西走廊绿洲水-粮-生态关联及水经济脱钩，方法新颖、数据跨度长，对干旱区农业水资源管理有参考价值。",[120],{"name":10,"url":109},[122,123,26,124,30],"气候变化","水资源管理","绿洲农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":128},{"candidates":129,"agricultural_evidence":46},[130,135,139],{"code":32,"score":131,"supported":46,"title":132,"abstract":133,"tags":134},12,[48],[50],[122],{"code":53,"score":54,"supported":46,"title":136,"abstract":137,"tags":138},[],[57,58],[26],{"code":61,"score":140,"supported":46,"title":141,"abstract":142,"tags":143},4,[],[65],[123],[145,146],"河西走廊 绿洲城市 水粮生态","河西走廊 农业用水 脱钩","河西走廊绿洲城市水粮生态-3898",2,"10.1016\u002Fj.ecolind.2026.115599",{"doi":149,"openalex_id":151,"authors":152,"venue":10,"cited_by_count":94,"oa_url":109,"card":167,"direction":99,"ingested_from":101},"W7214916060",[153,155,158,160,162,164],{"name":154,"orcid":9},"Jinbo Zhang",{"name":156,"orcid":157},"Zhixiang Lu","https:\u002F\u002Forcid.org\u002F0000-0002-9954-9321",{"name":159,"orcid":9},"Jiali Xie",{"name":161,"orcid":9},"Yan Luo",{"name":163,"orcid":9},"Li Anyuan",{"name":165,"orcid":166},"Qi Feng","https:\u002F\u002Forcid.org\u002F0009-0004-6561-3331",{"tldr":168,"method":169,"finding":170,"direction":171,"opportunity":172},"整合多源数据揭示河西走廊绿洲城市水-粮-生态关系及水经济脱钩的时空特征。","多年代水文气候、遥感生态与县级社会经济数据，结合Tapio脱钩、随机森林、GRA","变暖下气候水平衡恶化，2020年前水经济强脱钩，农业需水减少与协同措施提升水系统可靠性。","农业绿色发展与碳","可探索县域尺度水-粮-生态耦合机制与脱钩政策的跨区域可迁移性及地下水约束的优化路径。","2026-10-01T23:30:28.964236Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":188,"primary_topic":192,"secondary_topics":193,"classification":203,"search_phrases":259,"slug":262,"view_count":94,"doi":263,"paper":264,"created_at":287},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":116,"substance":17,"depth":184,"authority":131,"freshness":20,"relevant":21,"comment":185},15,"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。",[187],{"name":180,"url":177},[122,189,26,190,191],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[194,199],{"code":195,"slug":196,"name":197,"path":198,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":61,"slug":200,"name":201,"path":202,"parent_code":36},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":39,"locked":14,"version":40,"method":41,"evidence":204},{"candidates":205,"agricultural_evidence":46},[206,211,216,222,226,233,240,247,253],{"code":32,"score":17,"supported":46,"title":207,"abstract":208,"tags":210},[48],[122,209,50,48],"气候适应",[122],{"code":195,"score":184,"supported":46,"title":212,"abstract":213,"tags":215},[196],[214,196],"土壤",[189],{"code":61,"score":13,"supported":46,"title":217,"abstract":218,"tags":221},[],[219,65,200,220],"灌溉","evapotranspiration",[190],{"code":53,"score":54,"supported":46,"title":223,"abstract":224,"tags":225},[],[57,58],[26],{"code":227,"score":104,"supported":46,"title":228,"abstract":229,"tags":232},"A8",[],[230,231],"粮食安全","food security",[],{"code":234,"score":104,"supported":46,"title":235,"abstract":236,"tags":239},"I1",[],[237,238],"精准农业","precision agriculture",[],{"code":241,"score":104,"supported":46,"title":242,"abstract":243,"tags":246},"I4",[],[244,245],"地理信息","geographic information",[],{"code":248,"score":62,"supported":46,"title":249,"abstract":250,"tags":252},"F3",[],[251],"养分管理",[],{"code":254,"score":62,"supported":46,"title":255,"abstract":256,"tags":258},"J1",[],[257],"农业政策",[],[260,261],"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":263,"openalex_id":265,"authors":266,"venue":180,"cited_by_count":94,"oa_url":177,"card":282,"direction":99,"ingested_from":101},"W7222999158",[267,269,271,273,275,277,280],{"name":268,"orcid":9},"Asifa Naz",{"name":270,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":272,"orcid":9},"Syed Asghar Hussain Shah",{"name":274,"orcid":9},"Shahrish Nawaz",{"name":276,"orcid":9},"Muhmmad Irfan",{"name":278,"orcid":279},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":281,"orcid":9},"Muhammad Nouman Khalid",{"tldr":283,"method":284,"finding":285,"direction":99,"opportunity":286},"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":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":296,"sources":298,"tags":300,"primary_topic":304,"secondary_topics":309,"classification":315,"search_phrases":332,"slug":335,"view_count":94,"doi":336,"paper":337,"created_at":349},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":94,"substance":94,"depth":94,"authority":94,"freshness":94,"relevant":94,"comment":297},"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。",[299],{"name":294,"url":291},[301,26,302,303],"防灾减灾","洪水风险评估","GIS应用",{"code":53,"slug":305,"name":306,"path":307,"parent_code":308},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[310,314],{"code":241,"slug":311,"name":312,"path":313,"parent_code":308},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":316},{"candidates":317,"agricultural_evidence":46},[318,322,327],{"code":53,"score":184,"supported":46,"title":319,"abstract":320,"tags":321},[58],[57,58],[26],{"code":241,"score":19,"supported":46,"title":323,"abstract":325,"tags":326},[324],"GIS",[244,324],[],{"code":32,"score":20,"supported":46,"title":328,"abstract":329,"tags":331},[],[50,330],"drought",[302,301],[333,334],"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":336,"openalex_id":338,"authors":339,"venue":294,"cited_by_count":94,"oa_url":343,"card":344,"direction":99,"ingested_from":101},"W7222885988",[340],{"name":341,"orcid":342},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":345,"method":346,"finding":347,"direction":99,"opportunity":348},"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":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":356,"source_url":353,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":363,"primary_topic":367,"secondary_topics":368,"classification":369,"search_phrases":384,"slug":387,"view_count":94,"doi":388,"paper":389,"created_at":411},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":131,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":360},"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。",[362],{"name":356,"url":353},[122,26,364,365,366],"植被生产力","森林物候","珠江流域",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":370},{"candidates":371,"agricultural_evidence":46},[372,376,380],{"code":32,"score":54,"supported":46,"title":373,"abstract":374,"tags":375},[],[122,48],[122],{"code":195,"score":104,"supported":46,"title":377,"abstract":378,"tags":379},[],[214,196],[],{"code":53,"score":21,"supported":14,"title":381,"abstract":382,"tags":383},[],[],[26],[385,386],"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":388,"openalex_id":390,"authors":391,"venue":356,"cited_by_count":94,"oa_url":405,"card":406,"direction":99,"ingested_from":101},"W7221897424",[392,395,397,399,401,403],{"name":393,"orcid":394},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":396,"orcid":9},"Zhaoju Zheng",{"name":398,"orcid":9},"Xueming Zhao",{"name":400,"orcid":9},"Zhipeng Zhang",{"name":402,"orcid":9},"Xiaohui Cui",{"name":404,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":407,"method":408,"finding":409,"direction":99,"opportunity":410},"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":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":419,"score_detail":420,"sources":424,"tags":426,"primary_topic":431,"secondary_topics":432,"classification":433,"search_phrases":453,"slug":456,"view_count":21,"doi":457,"paper":458,"created_at":471},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",70,{"impact":131,"substance":421,"depth":422,"authority":116,"freshness":20,"relevant":21,"comment":423},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。",[425],{"name":418,"url":415},[427,428,26,429,430],"智慧农业","农业气象","冬小麦","作物估产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":434},{"candidates":435,"agricultural_evidence":46},[436,440,444,449],{"code":32,"score":140,"supported":46,"title":437,"abstract":438,"tags":439},[],[428],[428],{"code":53,"score":148,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[430,26],{"code":445,"score":21,"supported":14,"title":446,"abstract":447,"tags":448},"A2",[],[],[429],{"code":234,"score":21,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[427],[454,455],"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":457,"openalex_id":459,"authors":460,"venue":418,"cited_by_count":94,"oa_url":415,"card":466,"direction":99,"ingested_from":101},"W7221991562",[461,464],{"name":462,"orcid":463},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":465,"orcid":9},"O. V. Savitskaya",{"tldr":467,"method":468,"finding":469,"direction":99,"opportunity":470},"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":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":419,"score_detail":479,"sources":481,"tags":483,"primary_topic":486,"secondary_topics":491,"classification":493,"search_phrases":538,"slug":541,"view_count":21,"doi":542,"paper":543,"created_at":554},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":17,"substance":115,"depth":184,"authority":131,"freshness":117,"relevant":21,"comment":480},"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。",[482],{"name":478,"url":475},[427,484,485,122,26],"农业人工智能","可持续发展",{"code":487,"slug":488,"name":489,"path":490,"parent_code":308},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[492],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":494},{"candidates":495,"agricultural_evidence":46},[496,505,509,513,517,524,531],{"code":487,"score":17,"supported":46,"title":497,"abstract":499,"tags":504},[498],"artificial intelligence",[500,501,502,503,498],"深度学习","机器学习","machine learning","deep learning",[484],{"code":32,"score":184,"supported":46,"title":506,"abstract":507,"tags":508},[48],[122,48],[122],{"code":234,"score":54,"supported":46,"title":510,"abstract":511,"tags":512},[],[237,238],[427],{"code":53,"score":54,"supported":46,"title":514,"abstract":515,"tags":516},[],[57,58],[26],{"code":518,"score":104,"supported":46,"title":519,"abstract":520,"tags":523},"H4",[],[521,522],"碳排放","carbon emission",[],{"code":525,"score":104,"supported":46,"title":526,"abstract":527,"tags":530},"H6",[],[528,529],"生物多样性","biodiversity",[],{"code":532,"score":104,"supported":46,"title":533,"abstract":534,"tags":537},"I5",[],[535,536],"物联网","internet of things",[],[539,540],"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":542,"openalex_id":544,"authors":545,"venue":478,"cited_by_count":94,"oa_url":475,"card":548,"direction":553,"ingested_from":101},"W7222092855",[546],{"name":547,"orcid":9},"Deepmala Suryavanshi",{"tldr":549,"method":550,"finding":551,"direction":171,"opportunity":552},"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。","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"]