[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2306":3,"related-2306":87},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":86},2306,"Geospatial assessment of urban flood resilience in rapidly growing Abbottabad City, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1922478","Urban flooding is one of the most recurrent and destructive natural hazards affecting rapidly urbanizing cities in developing countries. Pakistan faces a severe and growing urban flood challenge, yet spatially explicit, multi-dimensional resilience assessments remain scarce for fast-growing secondary cities. This study assessed urban flood resilience in Abbottabad City, Khyber Pakhtunkhwa, Pakistan, by quantifying flood hazard, exposure, susceptibility, and coping capacity through an integrated geospatial framework. The Urban Flood Resilience Model was applied, combining remote sensing data, GIS-based weighted overlay analysis, and the Analytical Hierarchy Process (AHP) to derive indicator weights and generate thematic component maps. Primary data on flood damages, economic capacity, institutional capacity, and educational level were collected through field surveys and questionnaires across flood-prone union councils. The final Urban Flood Resilience Index (UFRI) map showed marked regional variability. The northern and central parts of Abbottabad faced relatively high flood hazard, exposure, and susceptibility. Yet these areas showed high to very high resilience, because stronger infrastructure, institutional support, and coping ability offset the risk. By contrast, the eastern and south-eastern zones showed low to very low resilience. Weak coping capacity there worsened the effects of flood hazard and vulnerability. All AHP consistency ratios were within the acceptable threshold (Consistency Ratio \u003C 0.1), confirming internal consistency of the pairwise comparisons. This assessment quantified both physical and socioeconomic dimensions of flood resilience, and the resulting Urban Flood Resilience Index delineated priority intervention zones requiring targeted drainage infrastructure upgrades, institutional strengthening, and community preparedness programs. The findings provide spatially explicit evidence for disaster risk managers, urban planners, and policymakers in Abbottabad and comparable mountainous cities of South Asia.","城市洪涝是影响发展中国家快速城市化城市的最频繁且最具破坏性的自然灾害之一。巴基斯坦面临着严峻且不断加剧的城市洪涝挑战，然而针对快速增长的中小城市，空间显式的多维韧性评估仍然匮乏。本研究通过集成地理空间框架，量化洪涝危险性、暴露度、脆弱性和应对能力，评估了巴基斯坦开伯尔-普什图省阿伯塔巴德市的城市洪涝韧性。研究应用城市洪涝韧性模型，结合遥感数据、基于GIS的加权叠加分析和层次分析法（AHP）来推导指标权重并生成专题组分图。通过实地调查和问卷调查，在洪涝易发的联合委员会收集了洪涝损失、经济能力、机构能力和教育水平方面的原始数据。最终的城市洪涝韧性指数（UFRI）图显示出显著的区域差异。阿伯塔巴德北部和中部地区面临相对较高的洪涝危险性、暴露度和脆弱性。然而这些区域表现出高至极高的韧性，因为更强的基础设施、机构支持和应对能力抵消了风险。相比之下，东部和东南部区域表现出低至极低的韧性。这些地区薄弱的应对能力加剧了洪涝危险性和脆弱性的影响。所有AHP一致性比率均在可接受阈值范围内（一致性比率\u003C0.1），证实了成对比较的内部一致性。本评估量化了洪涝韧性的物理和社会经济两个维度，所得的城市洪涝韧性指数划定了需要针对性排水基础设施升级、机构强化和社区备灾项目的优先干预区域。研究结果为阿伯塔巴德及南亚类似山区城市的灾害风险管理者、城市规划者和政策制定者提供了空间显式的证据。",null,"Frontiers in Environmental Science","2026-09-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦巴基斯坦城市洪水韧性评估，属城市防灾与地理信息领域，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。",[19],{"name":10,"url":6},[21,22,23],"防灾减灾","遥感监测","城市内涝",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":54},[31,40,47],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":39},"I3",7,true,[],[37,38],"遥感","remote sensing",[22],{"code":41,"score":42,"supported":34,"title":43,"abstract":44,"tags":46},"H7",4,[],[45],"洪涝",[21],{"code":48,"score":49,"supported":34,"title":50,"abstract":51,"tags":53},"I4",3,[],[52],"GIS",[],"农业相关性需要复核",[56,57],"城市内涝 遥感监测 防灾减灾","城市内涝 遥感监测","城市内涝遥感监测防灾减灾-2306",2,"10.3389\u002Ffenvs.2026.1922478",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":15,"oa_url":77,"card":78,"direction":84,"ingested_from":85},"W7212140700",[64,66,68,71,74],{"name":65,"orcid":9},"Areej Khan",{"name":67,"orcid":9},"Atta-Ur Rahman",{"name":69,"orcid":70},"Ghani Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-5091-2337",{"name":72,"orcid":73},"Yazeed Alsubhi","https:\u002F\u002Forcid.org\u002F0000-0002-9875-6898",{"name":75,"orcid":76},"Hyun‐Han Kwon","https:\u002F\u002Forcid.org\u002F0000-0003-4465-2708","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1922478\u002Fpdf",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"用地理空间框架评估巴基斯坦阿伯塔巴德市城市洪水韧性并绘制韧性指数图。","遥感、GIS加权叠加与AHP，结合实地问卷获取社会经济数据。","北部中部风险高但韧性高，东部东南部韧性低，需优先干预。","农业遥感与作物表型","可延伸至农业区洪水韧性评估，融合遥感与农户调查，服务乡村防灾规划。","智慧农业 \u002F 农业物联网","openalex","2026-09-13T23:30:11.071017Z",{"total":88,"page":89,"page_size":88,"items":90},6,1,[91,161,230,338,418,497],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":99,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":111,"classification":121,"search_phrases":143,"slug":146,"view_count":15,"doi":147,"paper":148,"created_at":160},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":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":100},"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。",[102],{"name":97,"url":94},[21,22,104,105],"洪水风险评估","GIS应用",{"code":32,"slug":107,"name":108,"path":109,"parent_code":110},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[112,116],{"code":48,"slug":113,"name":114,"path":115,"parent_code":110},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":41,"slug":117,"name":118,"path":119,"parent_code":120},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":122,"locked":14,"version":27,"method":28,"evidence":123},"classified",{"candidates":124,"agricultural_evidence":34},[125,130,136],{"code":32,"score":126,"supported":34,"title":127,"abstract":128,"tags":129},15,[38],[37,38],[22],{"code":48,"score":131,"supported":34,"title":132,"abstract":133,"tags":135},14,[52],[134,52],"地理信息",[],{"code":41,"score":137,"supported":34,"title":138,"abstract":139,"tags":142},8,[],[140,141],"干旱","drought",[104,21],[144,145],"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":147,"openalex_id":149,"authors":150,"venue":97,"cited_by_count":15,"oa_url":154,"card":155,"direction":82,"ingested_from":85},"W7222885988",[151],{"name":152,"orcid":153},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":156,"method":157,"finding":158,"direction":82,"opportunity":159},"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":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":170,"sources":176,"tags":178,"primary_topic":182,"secondary_topics":183,"classification":185,"search_phrases":211,"slug":214,"view_count":15,"doi":215,"paper":216,"created_at":229},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":137,"substance":171,"depth":172,"authority":173,"freshness":174,"relevant":89,"comment":175},21,17,13,9,"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[177],{"name":167,"url":164},[179,21,22,180,181],"粮食安全","植被指数","农业干旱",{"code":41,"slug":117,"name":118,"path":119,"parent_code":120},[184],{"code":32,"slug":107,"name":108,"path":109,"parent_code":110},{"status":122,"locked":14,"version":27,"method":28,"evidence":186},{"candidates":187,"agricultural_evidence":34},[188,195,199,205],{"code":41,"score":189,"supported":34,"title":190,"abstract":191,"tags":194},18,[141],[192,193,140,141],"气候变化","气候适应",[21],{"code":32,"score":137,"supported":34,"title":196,"abstract":197,"tags":198},[],[37,38],[180,22],{"code":200,"score":33,"supported":34,"title":201,"abstract":202,"tags":204},"A8",[],[179,203],"food security",[179],{"code":48,"score":88,"supported":34,"title":206,"abstract":207,"tags":210},[],[208,209],"空间分析","spatial analysis",[],[212,213],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":215,"openalex_id":217,"authors":218,"venue":167,"cited_by_count":15,"oa_url":164,"card":224,"direction":82,"ingested_from":85},"W7221455809",[219,221],{"name":220,"orcid":9},"Murad Hedo Haji",{"name":222,"orcid":223},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":225,"method":226,"finding":227,"direction":82,"opportunity":228},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":34,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":246,"secondary_topics":247,"classification":254,"search_phrases":315,"slug":318,"view_count":49,"doi":319,"paper":320,"created_at":337},3542,"Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pclm.0001038","Climate change is intensifying drought vulnerability in northern Ghana, posing serious risks to crop production, food security, and rural livelihoods in semi-arid regions. This study develops an empirical modelling framework to assess drought vulnerability and impacts at three temporal horizons, 2026–2050, 2051–2075, and 2076–2100, under SSP1-2.6 and SSP5-8.5 scenarios. We integrated bias-corrected outputs from five CMIP6 models, MRI-ESM2–0, EC-Earth3, IPSL-CM6A-LR, CESM2, and HadGEM3-GC31-LL, downscaled to 0.25° resolution, with drought indices including the Standardized Precipitation Index SPI, Reconnaissance Drought Index RDI, Normalized Difference Drought Index NDDI, and Soil Moisture Index SMI. These were combined with MODIS-derived Normalized Difference Vegetation Index NDVI, population density, and land use\u002Fland cover using GIS-based tools to generate composite drought vulnerability maps. CMIP6 models were validated against reference datasets and showed strong performance for annual precipitation, r = 0.95, RMSE = 3.4 mm, and temperature, r = 0.99, RMSE = 0.47–0.74°C. Results project increasing drought severity, with mean temperature rises of 2.0–3.9°C and rainfall declines up to 19% by 2100. Crop yields for maize, millet, sorghum, and groundnuts are expected to fall by 25–60% by 2050, with maize showing the strongest negative correlation to drought severity, r = –0.72, p \u003C 0.01, confirmed by Mann-Kendall trends at the 95% confidence level. About 84% of the study area is classified as highly vulnerable due to limited adaptive capacity, over 80% dependence on rain-fed agriculture, more than 49% household food insecurity, less than 23% cropland use, and under 5% water body coverage. Significant uncertainties remain in precipitation projections, with some models showing divergent temperature trends, highlighting challenges in modelling climate impacts. The findings underscore the urgent need for district-level drought preparedness, solar-powered irrigation, index insurance, climate-smart agriculture, and Disaster Risk Reduction aligned with UNDRR and Sendai Framework priorities to build resilience in northern Ghana.","气候变化正在加剧加纳北部的干旱脆弱性，对半干旱地区的作物生产、粮食安全和农村生计构成严重风险。本研究构建了一个经验建模框架，用于评估2026—2050年、2051—2075年和2076—2100年三个时间尺度下SSP1-2.6和SSP5-8.5情景中的干旱脆弱性及其影响。我们将五个CMIP6模型（MRI-ESM2–0、EC-Earth3、IPSL-CM6A-LR、CESM2和HadGEM3-GC31-LL）的偏差校正输出降尺度至0.25°分辨率，并与标准化降水指数（SPI）、侦察干旱指数（RDI）、归一化差值干旱指数（NDDI）和土壤水分指数（SMI）等干旱指数相结合。这些数据与MODIS衍生的归一化差值植被指数（NDVI）、人口密度以及土地利用\u002F土地覆盖数据通过基于GIS的工具相结合，生成综合干旱脆弱性地图。CMIP6模型经参考数据集验证，在年降水量（r = 0.95，RMSE = 3.4 mm）和温度（r = 0.99，RMSE = 0.47–0.74°C）方面表现出色。结果预测干旱严重程度将加剧，到2100年平均气温上升2.0–3.9°C，降雨量下降高达19%。到2050年，玉米、小米、高粱和花生的作物产量预计将下降25–60%，其中玉米与干旱严重程度呈最强负相关（r = –0.72，p \u003C 0.01），并经Mann-Kendall趋势检验在95%置信水平上得到证实。约84%的研究区域被归类为高度脆弱，原因包括适应能力有限、超过80%依赖雨养农业、超过49%的家庭粮食不安全、不足23%的耕地利用率以及低于5%的水体覆盖率。降水预测仍存在显著不确定性，部分模型显示出不同的温度趋势，凸显了气候影响建模的挑战。研究结果强调迫切需要开展县级干旱备灾、太阳能灌溉、指数保险、气候智慧型农业以及与UNDRR和仙台框架优先事项一致的风险减灾工作，以增强加纳北部的韧性。","PLOS Climate","2026-09-25T00:00:00Z",82,{"impact":189,"substance":240,"depth":189,"authority":131,"freshness":174,"relevant":89,"comment":241},23,"基于CMIP6多模型与遥感指数的干旱脆弱性实证研究，数据扎实、结论明确，对半干旱区农业防灾与气候适应有参考价值。",[243],{"name":236,"url":233},[179,21,192,22,245],"干旱风险",{"code":41,"slug":117,"name":118,"path":119,"parent_code":120},[248],{"code":249,"slug":250,"name":251,"path":252,"parent_code":253},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",{"status":122,"locked":14,"version":27,"method":28,"evidence":255},{"candidates":256,"agricultural_evidence":34},[257,263,270,274,281,288,295,301,307,311],{"code":41,"score":258,"supported":34,"title":259,"abstract":260,"tags":262},19,[141],[192,140,261,141],"climate change",[192,21],{"code":249,"score":174,"supported":34,"title":264,"abstract":265,"tags":269},[],[266,267,268],"高粱","sorghum","millet",[],{"code":200,"score":33,"supported":34,"title":271,"abstract":272,"tags":273},[],[179,203],[179],{"code":275,"score":88,"supported":34,"title":276,"abstract":277,"tags":280},"A3",[],[278,279],"玉米","maize",[],{"code":282,"score":88,"supported":34,"title":283,"abstract":284,"tags":287},"H2",[],[285,286],"土壤","soil",[],{"code":289,"score":88,"supported":34,"title":290,"abstract":291,"tags":294},"H3",[],[292,293],"灌溉","irrigation",[],{"code":296,"score":49,"supported":34,"title":297,"abstract":298,"tags":300},"A4",[],[299],"花生",[],{"code":302,"score":49,"supported":34,"title":303,"abstract":304,"tags":306},"I1",[],[305],"smart agriculture",[],{"code":48,"score":49,"supported":34,"title":308,"abstract":309,"tags":310},[],[52],[],{"code":32,"score":89,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[22],[316,317],"加纳北部 干旱 玉米","CMIP6 干旱脆弱性 作物产量","加纳北部干旱玉米-3542","10.1371\u002Fjournal.pclm.0001038",{"doi":319,"openalex_id":321,"authors":322,"venue":236,"cited_by_count":15,"oa_url":233,"card":332,"direction":84,"ingested_from":85},"W7214399028",[323,326,329],{"name":324,"orcid":325},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":327,"orcid":328},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":330,"orcid":331},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":333,"method":334,"finding":335,"direction":82,"opportunity":336},"构建干旱脆弱性经验模型，评估加纳北部未来气候情景下作物与生计影响。","集成5个CMIP6模型、SPI\u002FRDI\u002FNDDI\u002FSMI指数与MODIS NDV","至2100年升温2.0-3.9°C、降水降19%，2050年作物减产25-60%，84%区域高度脆弱","可结合遥感干旱指数与作物模型，在数据稀缺区开展多情景脆弱性动态评估与适应策略优化。","2026-09-26T23:30:10.153994Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":351,"tags":353,"primary_topic":9,"secondary_topics":358,"classification":359,"search_phrases":388,"slug":391,"view_count":15,"doi":392,"paper":393,"created_at":417},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":189,"substance":348,"depth":172,"authority":349,"freshness":174,"relevant":89,"comment":350},22,12,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[352],{"name":344,"url":341},[22,354,355,356,357],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":360},{"candidates":361,"agricultural_evidence":34,"reason":387},[362,369,373,377,381],{"code":363,"score":33,"supported":34,"title":364,"abstract":365,"tags":368},"H6",[],[366,367],"生物多样性","biodiversity",[354],{"code":32,"score":33,"supported":34,"title":370,"abstract":371,"tags":372},[],[37,38],[22],{"code":41,"score":88,"supported":34,"title":374,"abstract":375,"tags":376},[],[192,261],[],{"code":282,"score":49,"supported":34,"title":378,"abstract":379,"tags":380},[],[286],[],{"code":382,"score":49,"supported":34,"title":383,"abstract":384,"tags":386},"I7",[],[385],"研究方法",[],"多个产业证据相当，需要确认主归属",[389,390],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":392,"openalex_id":394,"authors":395,"venue":344,"cited_by_count":15,"oa_url":341,"card":412,"direction":82,"ingested_from":85},"W7223141286",[396,399,401,404,407,410],{"name":397,"orcid":398},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":400,"orcid":9},"Gabriel Henrique Santos Silva",{"name":402,"orcid":403},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":405,"orcid":406},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":408,"orcid":409},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":411,"orcid":9},"Sylvana Melo dos Santos",{"tldr":413,"method":414,"finding":415,"direction":82,"opportunity":416},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":429,"tags":431,"primary_topic":436,"secondary_topics":439,"classification":440,"search_phrases":459,"slug":462,"view_count":15,"doi":463,"paper":464,"created_at":496},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":427,"substance":348,"depth":189,"authority":131,"freshness":174,"relevant":89,"comment":428},16,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[430],{"name":424,"url":421},[22,432,433,434,435],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":282,"slug":286,"name":437,"path":438,"parent_code":120},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":122,"locked":14,"version":27,"method":28,"evidence":441},{"candidates":442,"agricultural_evidence":34},[443,447,451,455],{"code":282,"score":131,"supported":34,"title":444,"abstract":445,"tags":446},[286],[285,286],[],{"code":32,"score":33,"supported":34,"title":448,"abstract":449,"tags":450},[],[37,38],[22],{"code":41,"score":88,"supported":34,"title":452,"abstract":453,"tags":454},[],[193,140],[],{"code":363,"score":89,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[434],[460,461],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":463,"openalex_id":465,"authors":466,"venue":424,"cited_by_count":15,"oa_url":421,"card":491,"direction":82,"ingested_from":85},"W7223069171",[467,469,471,473,476,478,481,484,486,488],{"name":468,"orcid":9},"Yue Li",{"name":470,"orcid":9},"Mengfei Shi",{"name":472,"orcid":9},"Fei Zhang",{"name":474,"orcid":475},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":477,"orcid":9},"Gowhar Meraj",{"name":479,"orcid":480},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":482,"orcid":483},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":485,"orcid":9},"Yuanfang Chai",{"name":487,"orcid":9},"Hongtao Xu",{"name":489,"orcid":490},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":492,"method":493,"finding":494,"direction":82,"opportunity":495},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":502,"content":9,"source_name":503,"source_url":500,"published_at":504,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":505,"sources":507,"tags":509,"primary_topic":514,"secondary_topics":520,"classification":521,"search_phrases":537,"slug":540,"view_count":15,"doi":541,"paper":542,"created_at":553},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":189,"substance":348,"depth":189,"authority":349,"freshness":174,"relevant":89,"comment":506},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[508],{"name":503,"url":500},[510,22,511,512,513],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":515,"slug":516,"name":517,"path":518,"parent_code":519},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":122,"locked":14,"version":27,"method":28,"evidence":522},{"candidates":523,"agricultural_evidence":34},[524,529,533],{"code":515,"score":49,"supported":34,"title":525,"abstract":526,"tags":528},[],[527],"萝卜",[],{"code":302,"score":89,"supported":14,"title":530,"abstract":531,"tags":532},[],[],[510],{"code":32,"score":89,"supported":14,"title":534,"abstract":535,"tags":536},[],[],[22],[538,539],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":541,"openalex_id":543,"authors":544,"venue":503,"cited_by_count":15,"oa_url":500,"card":548,"direction":82,"ingested_from":85},"W7223098474",[545],{"name":546,"orcid":547},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":549,"method":550,"finding":551,"direction":82,"opportunity":552},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z"]