[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3016":3,"related-3016":111},{"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":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":110},3016,"Afforestation enhances ecosystem stability in the farming-pastoral ecotone of northern China, but its effects are constrained by water availability","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtag232","Abstract The farming-pastoral ecotone of northern China (FPENC), a typical semi-agricultural and semi-pastoral ecologically fragile zone, has long faced controversy over afforestation due to its water-limited environment, raising concerns about how afforestation affects this fragile ecosystem. In this study, based on Google Earth Engine and a Random Forest model, we derive afforestation distribution (1985-2020), and further investigated ecosystem stability under afforestation from the perspectives of short-term responses and long-term dynamic changes. The results showed: (1) By 2020, 67,700 km2 had been afforested in FPENC (6.20% of its area). Afforestation survival strongly depended on precipitation, with relatively low retention rates (40%-70%) in the zones with \u003C 400 mm. (2) At short-term timescales, afforestation mitigated ecosystem state deviations caused by extreme drought events and improved surrounding ecosystems within a 150 m buffer. (3) Over long-term timescales, NDVI across the FPENC exhibited a significant increasing trend over the past four decades, accompanied by an overall enhancement in ecosystem recovery capacity. In zones with > 400 mm annual precipitation and with forest cover exceeding 40%, ecosystem recovery capacity increased significantly, showing there might be a potential linkage among afforestation, precipitation, and ecosystem recovery processes. This study revealed despite water scarcity and low tree survival, afforestation under suitable precipitation can effectively enhance regional ecosystem stability.","中国北方农牧交错带（FPENC）是典型的半农半牧生态脆弱区，由于环境水分受限，长期以来在造林问题上存在争议，引发了人们对造林如何影响这一脆弱生态系统的担忧。本研究基于Google Earth Engine和随机森林模型，反演了1985—2020年造林分布，并进一步从短期响应和长期动态变化两个角度研究了造林条件下的生态系统稳定性。结果表明：（1）截至2020年，FPENC已造林67,700 km²，占其面积的6.20%。造林存活强烈依赖降水，在降水量小于400 mm的区域，保存率相对较低（40%—70%）。（2）在短期时间尺度上，造林缓解了极端干旱事件引起的生态系统状态偏离，并改善了150 m缓冲区内周边生态系统。（3）在长期时间尺度上，过去四十年FPENC的NDVI呈显著增加趋势，同时生态系统恢复能力总体增强。在年降水量大于400 mm且森林覆盖率超过40%的区域，生态系统恢复能力显著提高，表明造林、降水与生态系统恢复过程之间可能存在潜在联系。本研究揭示，尽管存在水分短缺和树木存活率低的问题，但在适宜降水条件下，造林能够有效增强区域生态系统稳定性。",null,"Journal of Plant Ecology","2026-09-17T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于遥感与随机森林量化北方农牧交错带造林成效及水分约束，数据规模大、结论有新意，对生态修复与农业信息化有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"生态修复","遥感监测","造林","农牧交错带","水资源约束",{"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},[44,52,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},6,true,[],[49,50],"干旱","drought",[],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"H6",[],[],[26],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I3",[],[],[27],[63,64],"北方农牧交错带 造林 水资源","Google Earth Engine 造林 遥感","北方农牧交错带造林水资源-3016",3,"10.1093\u002Fjpe\u002Frtag232",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":101,"oa_url":102,"card":103,"direction":107,"ingested_from":109},"W7213445880",[71,74,77,79,81,83,85,87,89,91,94,96,99],{"name":72,"orcid":73},"Yuchao Luo","https:\u002F\u002Forcid.org\u002F0000-0003-0063-3505",{"name":75,"orcid":76},"Haile Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-5870-062X",{"name":78,"orcid":9},"Qianhe Wang",{"name":80,"orcid":9},"Yi Zhou",{"name":82,"orcid":9},"Xin Chen",{"name":84,"orcid":9},"Yuling Jin",{"name":86,"orcid":9},"Xingjie Yin",{"name":88,"orcid":9},"Guoliang Zhang",{"name":90,"orcid":9},"Haorui Sun",{"name":92,"orcid":93},"Jun Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1408-4271",{"name":95,"orcid":9},"Huiyao Shi",{"name":97,"orcid":98},"Zhihua Pan","https:\u002F\u002Forcid.org\u002F0000-0002-8187-1574",{"name":100,"orcid":9},"Pingli An",0,"https:\u002F\u002Facademic.oup.com\u002Fjpe\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjpe\u002Frtag232\u002F71179175\u002Frtag232.pdf",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"基于GEE和随机森林反演1985-2020年北方农牧交错带造林分布，评估其对生态系统稳定性的影响。","Google Earth Engine遥感数据与随机森林模型，分析造林分布及ND","造林可缓解干旱影响并提升恢复力，但存活率与效果受降水制约，400mm为关键阈值。","农业遥感与作物表型","可探究不同降水梯度下造林-水分-恢复力耦合机制，优化生态修复的水资源约束阈值。","openalex","2026-09-20T23:30:22.592517Z",{"total":45,"page":21,"page_size":45,"items":112},[113,210,322,382,444,505],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":137,"classification":145,"search_phrases":176,"slug":179,"view_count":180,"doi":181,"paper":182,"created_at":209},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":123,"substance":18,"depth":17,"authority":19,"freshness":124,"relevant":21,"comment":125},16,9,"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[127],{"name":119,"url":116},[129,130,26,27,131],"机器学习","土壤有机碳","干旱区农业",{"code":133,"slug":134,"name":135,"path":136,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[138,139],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":140,"slug":141,"name":142,"path":143,"parent_code":144},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":146},{"candidates":147,"agricultural_evidence":46},[148,154,160,165,172],{"code":133,"score":149,"supported":46,"title":150,"abstract":151,"tags":153},15,[134],[152,134],"土壤",[130],{"code":32,"score":13,"supported":46,"title":155,"abstract":156,"tags":159},[],[157,49,158],"气候变化","climate change",[131],{"code":140,"score":124,"supported":46,"title":161,"abstract":163,"tags":164},[162],"machine learning",[],[129],{"code":58,"score":166,"supported":46,"title":167,"abstract":168,"tags":171},7,[],[169,170],"遥感","remote sensing",[27],{"code":53,"score":21,"supported":14,"title":173,"abstract":174,"tags":175},[],[],[26],[177,178],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172",2,"10.1186\u002Fs13021-026-00522-5",{"doi":181,"openalex_id":183,"authors":184,"venue":119,"cited_by_count":101,"oa_url":116,"card":204,"direction":107,"ingested_from":109},"W7219075005",[185,187,190,192,195,197,200,202],{"name":186,"orcid":9},"Y. Chen",{"name":188,"orcid":189},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":191,"orcid":9},"Meng Zhu",{"name":193,"orcid":194},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":196,"orcid":9},"Yulian Hao",{"name":198,"orcid":199},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":201,"orcid":9},"Zhehui Wang",{"name":203,"orcid":9},"Bin Wang",{"tldr":205,"method":206,"finding":207,"direction":107,"opportunity":208},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":223,"tags":225,"primary_topic":229,"secondary_topics":230,"classification":237,"search_phrases":293,"slug":296,"view_count":101,"doi":297,"paper":298,"created_at":321},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":220,"substance":17,"depth":149,"authority":221,"freshness":20,"relevant":21,"comment":222},13,12,"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。",[224],{"name":216,"url":213},[157,226,27,227,228],"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},[231,232],{"code":133,"slug":134,"name":135,"path":136,"parent_code":36},{"code":233,"slug":234,"name":235,"path":236,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":39,"locked":14,"version":40,"method":41,"evidence":238},{"candidates":239,"agricultural_evidence":46},[240,245,249,256,260,267,274,281,287],{"code":32,"score":17,"supported":46,"title":241,"abstract":242,"tags":244},[158],[157,243,49,158],"气候适应",[157],{"code":133,"score":149,"supported":46,"title":246,"abstract":247,"tags":248},[134],[152,134],[226],{"code":233,"score":13,"supported":46,"title":250,"abstract":251,"tags":255},[],[252,253,234,254],"灌溉","水资源","evapotranspiration",[227],{"code":58,"score":166,"supported":46,"title":257,"abstract":258,"tags":259},[],[169,170],[27],{"code":261,"score":45,"supported":46,"title":262,"abstract":263,"tags":266},"A8",[],[264,265],"粮食安全","food security",[],{"code":268,"score":45,"supported":46,"title":269,"abstract":270,"tags":273},"I1",[],[271,272],"精准农业","precision agriculture",[],{"code":275,"score":45,"supported":46,"title":276,"abstract":277,"tags":280},"I4",[],[278,279],"地理信息","geographic information",[],{"code":282,"score":66,"supported":46,"title":283,"abstract":284,"tags":286},"F3",[],[285],"养分管理",[],{"code":288,"score":66,"supported":46,"title":289,"abstract":290,"tags":292},"J1",[],[291],"农业政策",[],[294,295],"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":297,"openalex_id":299,"authors":300,"venue":216,"cited_by_count":101,"oa_url":213,"card":316,"direction":107,"ingested_from":109},"W7222999158",[301,303,305,307,309,311,314],{"name":302,"orcid":9},"Asifa Naz",{"name":304,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":306,"orcid":9},"Syed Asghar Hussain Shah",{"name":308,"orcid":9},"Shahrish Nawaz",{"name":310,"orcid":9},"Muhmmad Irfan",{"name":312,"orcid":313},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":315,"orcid":9},"Muhammad Nouman Khalid",{"tldr":317,"method":318,"finding":319,"direction":107,"opportunity":320},"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":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":330,"sources":332,"tags":334,"primary_topic":338,"secondary_topics":342,"classification":348,"search_phrases":364,"slug":367,"view_count":101,"doi":368,"paper":369,"created_at":381},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":101,"substance":101,"depth":101,"authority":101,"freshness":101,"relevant":101,"comment":331},"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。",[333],{"name":328,"url":325},[335,27,336,337],"防灾减灾","洪水风险评估","GIS应用",{"code":58,"slug":339,"name":340,"path":341,"parent_code":144},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[343,347],{"code":275,"slug":344,"name":345,"path":346,"parent_code":144},"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":349},{"candidates":350,"agricultural_evidence":46},[351,355,360],{"code":58,"score":149,"supported":46,"title":352,"abstract":353,"tags":354},[170],[169,170],[27],{"code":275,"score":19,"supported":46,"title":356,"abstract":358,"tags":359},[357],"GIS",[278,357],[],{"code":32,"score":20,"supported":46,"title":361,"abstract":362,"tags":363},[],[49,50],[336,335],[365,366],"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":368,"openalex_id":370,"authors":371,"venue":328,"cited_by_count":101,"oa_url":375,"card":376,"direction":107,"ingested_from":109},"W7222885988",[372],{"name":373,"orcid":374},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":377,"method":378,"finding":379,"direction":107,"opportunity":380},"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":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":393,"tags":395,"primary_topic":399,"secondary_topics":400,"classification":401,"search_phrases":416,"slug":419,"view_count":101,"doi":420,"paper":421,"created_at":443},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":221,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":392},"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。",[394],{"name":388,"url":385},[157,27,396,397,398],"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":402},{"candidates":403,"agricultural_evidence":46},[404,408,412],{"code":32,"score":166,"supported":46,"title":405,"abstract":406,"tags":407},[],[157,158],[157],{"code":133,"score":45,"supported":46,"title":409,"abstract":410,"tags":411},[],[152,134],[],{"code":58,"score":21,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[27],[417,418],"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":420,"openalex_id":422,"authors":423,"venue":388,"cited_by_count":101,"oa_url":437,"card":438,"direction":107,"ingested_from":109},"W7221897424",[424,427,429,431,433,435],{"name":425,"orcid":426},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":428,"orcid":9},"Zhaoju Zheng",{"name":430,"orcid":9},"Xueming Zhao",{"name":432,"orcid":9},"Zhipeng Zhang",{"name":434,"orcid":9},"Xiaohui Cui",{"name":436,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":439,"method":440,"finding":441,"direction":107,"opportunity":442},"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":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":9,"source_name":450,"source_url":447,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":451,"score_detail":452,"sources":456,"tags":458,"primary_topic":463,"secondary_topics":464,"classification":465,"search_phrases":486,"slug":489,"view_count":21,"doi":490,"paper":491,"created_at":504},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":221,"substance":453,"depth":454,"authority":220,"freshness":20,"relevant":21,"comment":455},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。",[457],{"name":450,"url":447},[459,460,27,461,462],"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":466},{"candidates":467,"agricultural_evidence":46},[468,473,477,482],{"code":32,"score":469,"supported":46,"title":470,"abstract":471,"tags":472},4,[],[460],[460],{"code":58,"score":180,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[462,27],{"code":478,"score":21,"supported":14,"title":479,"abstract":480,"tags":481},"A2",[],[],[461],{"code":268,"score":21,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[459],[487,488],"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":490,"openalex_id":492,"authors":493,"venue":450,"cited_by_count":101,"oa_url":447,"card":499,"direction":107,"ingested_from":109},"W7221991562",[494,497],{"name":495,"orcid":496},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":498,"orcid":9},"O. V. Savitskaya",{"tldr":500,"method":501,"finding":502,"direction":107,"opportunity":503},"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":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":9,"source_name":511,"source_url":508,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":451,"score_detail":512,"sources":514,"tags":516,"primary_topic":519,"secondary_topics":520,"classification":522,"search_phrases":564,"slug":567,"view_count":21,"doi":568,"paper":569,"created_at":581},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":123,"depth":149,"authority":221,"freshness":124,"relevant":21,"comment":513},"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。",[515],{"name":511,"url":508},[459,517,518,157,27],"农业人工智能","可持续发展",{"code":140,"slug":141,"name":142,"path":143,"parent_code":144},[521],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":523},{"candidates":524,"agricultural_evidence":46},[525,532,536,540,544,551,557],{"code":140,"score":17,"supported":46,"title":526,"abstract":528,"tags":531},[527],"artificial intelligence",[529,129,162,530,527],"深度学习","deep learning",[517],{"code":32,"score":149,"supported":46,"title":533,"abstract":534,"tags":535},[158],[157,158],[157],{"code":268,"score":166,"supported":46,"title":537,"abstract":538,"tags":539},[],[271,272],[459],{"code":58,"score":166,"supported":46,"title":541,"abstract":542,"tags":543},[],[169,170],[27],{"code":545,"score":45,"supported":46,"title":546,"abstract":547,"tags":550},"H4",[],[548,549],"碳排放","carbon emission",[],{"code":53,"score":45,"supported":46,"title":552,"abstract":553,"tags":556},[],[554,555],"生物多样性","biodiversity",[],{"code":558,"score":45,"supported":46,"title":559,"abstract":560,"tags":563},"I5",[],[561,562],"物联网","internet of things",[],[565,566],"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":568,"openalex_id":570,"authors":571,"venue":511,"cited_by_count":101,"oa_url":508,"card":574,"direction":580,"ingested_from":109},"W7222092855",[572],{"name":573,"orcid":9},"Deepmala Suryavanshi",{"tldr":575,"method":576,"finding":577,"direction":578,"opportunity":579},"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"]