[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4752":3,"related-4752":78},{"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":25,"classification":26,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":77},4752,"Büyük Ölçekli GES Projelerinin Yerel Mikroklimaya Etkisi","https:\u002F\u002Fdoi.org\u002F10.54864\u002Fplanarch.1853178","Yenilenebilir enerji yatırımları düşük karbon dönüşümü açısından kritik bir rol üstlenmekle birlikte, kırsal ve yarı kurak bölgelerde kurulan büyük ölçekli fotovoltaik enerji santrallerinin yerel yüzey ısısı ve mikroklima göstergeleriyle ilişkisi, planlama ve tasarım literatüründe yeterince tartışılmamaktadır. Bu çalışma, Konya ilinin Karapınar ilçesindeki Kalyon Karapınar Güneş Enerjisi Santrali çevresindeki bitki örtüsü yoğunluğu, yapay veya çıplak yüzey desenleri ve yer yüzey sıcaklığındaki (LST) değişiklikleri incelemektedir. Güneş enerjisi santralinin kurulumundan önceki (2010–2020) ve sonraki (2021–2024) dönemleri temsil etmek üzere seçilen yaz Landsat görüntüleri kullanılarak, CBS tabanlı bir iş akışı içinde NDVI, NDBI ve LST analizleri üretilmiştir. Sonuçlar, yağış verileriyle birlikte yorumlanmıştır. Bulgular, güneş enerjisi santralinin içinde ve çevresinde bitki örtüsü yoğunluğunda azalma, yapay veya çıplak yüzey alanlarında artış ve yaz yüzey sıcaklıklarında yükselme ile karakterize edilen bir mekânsal deseni ortaya koymaktadır. Ortalama yaz yer yüzey sıcaklığı (LST) 36,2°C'den 38,7°C'ye yükselmiş, bazı bölgelerde ise 44°C'nin üzerinde sıcaklıklar gözlemlenmiştir. Bu bulgular, güneş enerjisi santralinin kurulumunun ardından çalışma alanında ve çevresinde yüzey termal değişikliklerinin meydana geldiğini göstermektedir. Bununla birlikte, bu bulgular nedensel bir ilişkinin doğrudan kanıtı olarak yorumlanmamalıdır. Çalışma, büyük ölçekli güneş enerjisi santrali yatırımları için planlama kararlarına mikro iklime duyarlı alan seçimi, bitki örtüsü tampon bölgeleri, geçirgen yüzey stratejileri ve uzun vadeli çevresel izlemenin dâhil edilmesinin gerekliliğini vurgulamaktadır.","可再生能源投资在低碳转型中发挥着关键作用，然而，关于干旱及半干旱农村地区建设的大型光伏电站与局地地表温度和微气候指标之间的关系，规划和设计文献中尚未充分讨论。本研究考察了科尼亚省卡拉皮纳尔区Kalyon卡拉皮纳尔太阳能电站周边植被密度、人工或裸露地表格局以及地表温度（LST）的变化。利用选取的夏季Landsat影像，分别代表太阳能电站建设前（2010—2020年）和建设后（2021—2024年）时期，在基于GIS的工作流程中生成了NDVI、NDBI和LST分析结果。研究结果结合降水数据进行了阐释。研究发现揭示了一种空间格局，其特征为太阳能电站内部及周边植被密度下降、人工或裸露地表面积增加以及夏季地表温度升高。夏季平均地表温度（LST）从36.2°C上升至38.7°C，部分地区观测到超过44°C的高温。这些发现表明，太阳能电站建设后，研究区及其周边发生了地表热变化。然而，这些发现不应被解释为因果关系的直接证据。本研究强调，对于大型太阳能电站投资，规划决策中有必要纳入微气候敏感型选址、植被缓冲带、透水面策略以及长期环境监测。",null,"PLANARCH - Design and Planning Research","2026-10-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究大型光伏电站对局地微气候与地表温度的影响，属能源规划与遥感领域，与三农、农业信息化主题不相关。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","地表温度","微气候","光伏电站",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":35,"reason":52},[32,40,46],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":39},"H7",3,true,[],[38],"干旱",[],{"code":41,"score":34,"supported":35,"title":42,"abstract":43,"tags":45},"I4",[],[44],"GIS",[],{"code":47,"score":48,"supported":14,"title":49,"abstract":50,"tags":51},"I3",2,[],[],[22,21],"多个产业证据相当，需要确认主归属",[54,55],"Konya Karapınar 光伏电站","光伏电站 地表温度 NDVI","KonyaKarapınar光伏电站-4752",1,"10.54864\u002Fplanarch.1853178",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":15,"oa_url":68,"card":69,"direction":75,"ingested_from":76},"W7221335332",[62,65],{"name":63,"orcid":64},"Merve Çakır","https:\u002F\u002Forcid.org\u002F0009-0003-6063-0987",{"name":66,"orcid":67},"Sinan Levend","https:\u002F\u002Forcid.org\u002F0000-0002-3382-0983","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5557267",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"分析土耳其Konya大型光伏电站建设前后对周边植被、地表类型与地表温度的微气候影响。","用2010–2020与2021–2024年Landsat夏季影像，在GIS中计算","电站内外植被减少、裸地增加，夏季平均LST由36.2°C升至38.7°C，局部超44°C。","农业绿色发展与碳","可延伸研究光伏电站微气候效应的因果归因、植被缓冲带与透水面设计对LST的缓解效果。","农业遥感与作物表型","openalex","2026-10-12T23:30:24.704109Z",{"total":79,"page":57,"page_size":79,"items":80},6,[81,134,206,278,330,382],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":9,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":89,"sources":91,"tags":93,"primary_topic":96,"secondary_topics":101,"classification":102,"search_phrases":113,"slug":116,"view_count":15,"doi":117,"paper":118,"created_at":133},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research","2026-10-17T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":90},"印度城市热岛遥感研究，属城市气候与环境领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[92],{"name":87,"url":84},[21,22,94,95],"城市热岛","MODIS",{"code":47,"slug":97,"name":98,"path":99,"parent_code":100},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":103,"locked":14,"version":28,"method":29,"evidence":104},"classified",{"candidates":105,"agricultural_evidence":35},[106],{"code":47,"score":107,"supported":35,"title":108,"abstract":109,"tags":112},8,[],[110,111],"遥感","remote sensing",[22,21],[114,115],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":117,"openalex_id":119,"authors":120,"venue":87,"cited_by_count":15,"oa_url":84,"card":128,"direction":75,"ingested_from":76},"W7222794736",[121,124,126],{"name":122,"orcid":123},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":125,"orcid":9},"Shubham Mor",{"name":127,"orcid":9},"Gaurav Kalotra",{"tldr":129,"method":130,"finding":131,"direction":75,"opportunity":132},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":9,"source_name":140,"source_url":137,"published_at":141,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":142,"sources":144,"tags":146,"primary_topic":9,"secondary_topics":148,"classification":149,"search_phrases":162,"slug":165,"view_count":15,"doi":166,"paper":167,"created_at":205},5011,"Thirty-five years of urban expansion and thermal transformation: Integrating LULC change climate variability and population growth","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75473-5","Abstract Rapid urban expansion can alter surface thermal conditions, yet these changes are often evaluated without accounting for climatic variability, demographic pressures, and uncertainty. This study investigated long-term land-use\u002Fland-cover (LULC) and thermal dynamics in Jhang District, Pakistan, for 1990, 2000, 2010, 2020, and 2025 using Landsat-based LULC classification, land surface temperature (LST), surface urban heat island (SUHI), Urban Thermal Field Variance Index (UTFVI), spectral indices, Getis–Ord Gi* hotspot analysis, Pearson correlation, and multiple linear regression (MLR). Landsat-derived LST was independently compared with MODIS, while ERA5-Land, CHIRPS, and GHS-POP were used to characterize climatic and demographic context. Urban land expanded from 62.67 km² (0.98%) to 341.36 km² (5.37%), with 219.53 km² of Vegetated Land converted to Urban land. Maximum LST increased from 49.09 °C to 55.01 °C, while 99% confidence hot spots expanded from 856.42 to 1,041.31 km². LST was negatively correlated with NDVI and NDMI and positively correlated with NDBI and NDBaI. MLR models explained 54.2–83.1% of spatial LST variability. MODIS comparison showed strong spatial correspondence (R 2 = 0.752–0.843) despite systematic absolute-temperature differences. Air temperature increased significantly by 0.199 °C decade⁻¹, while population rose from 1.58 to 3.44 million. The findings demonstrate that Jhang’s thermal evolution reflects interacting land-cover, climatic, and demographic pressures rather than urban growth alone.","摘要 快速的城市扩张会改变地表热环境状况，但相关评估往往未充分考虑气候变率、人口压力及不确定性。本研究以巴基斯坦章县（Jhang District）为对象，基于Landsat土地利用\u002F土地覆盖（LULC）分类、地表温度（LST）、地表城市热岛（SUHI）、城市热场变异指数（UTFVI）、光谱指数、Getis–Ord Gi*热点分析、Pearson相关分析及多元线性回归（MLR），对1990、2000、2010、2020和2025年的长期LULC与热动态进行了研究。将Landsat反演LST与MODIS数据进行了独立对比，同时利用ERA5-Land、CHIRPS和GHS-POP刻画气候与人口背景。城市用地由62.67 km²（0.98%）扩张至341.36 km²（5.37%），其中219.53 km²的植被用地转为城市用地。最高LST由49.09 °C升至55.01 °C，而99%置信度热点面积由856.42 km²扩大至1,041.31 km²。LST与NDVI和NDMI呈负相关，与NDBI和NDBaI呈正相关。MLR模型可解释54.2–83.1%的LST空间变异。与MODIS对比显示，尽管存在系统性绝对温度差异，二者仍具有较强空间一致性（R² = 0.752–0.843）。气温以0.199 °C decade⁻¹的速率显著上升，人口由158万增至344万。研究结果表明，章县的热环境演变反映的是土地覆盖、气候与人口压力的交互作用，而非仅由城市增长所致。","Scientific Reports","2026-10-13T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":143},"研究巴基斯坦城市扩张与热环境变化，属城市遥感与气候变化领域，与三农、农业信息化、数字乡村无直接关联，不建议进入每日精选。",[145],{"name":140,"url":137},[21,22,94,147],"土地利用变化",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":150},{"candidates":151,"agricultural_evidence":14,"reason":161},[152,156],{"code":47,"score":48,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[22,21],{"code":157,"score":57,"supported":14,"title":158,"abstract":159,"tags":160},"H6",[],[],[147],"农业相关性需要复核",[163,164],"Jhang District 城市扩张 地表温度","Landsat LST 城市热岛","JhangDistrict城市扩张地表温度-5011","10.1038\u002Fs41598-026-75473-5",{"doi":166,"openalex_id":168,"authors":169,"venue":140,"cited_by_count":15,"oa_url":199,"card":200,"direction":75,"ingested_from":76},"W7221689409",[170,172,174,177,180,182,185,188,191,194,197],{"name":171,"orcid":9},"Rui Li",{"name":173,"orcid":9},"Yifei Yuan",{"name":175,"orcid":176},"Zainab Tahir","https:\u002F\u002Forcid.org\u002F0000-0002-1459-3527",{"name":178,"orcid":179},"Muhammad Haseeb","https:\u002F\u002Forcid.org\u002F0000-0002-0697-6894",{"name":181,"orcid":9},"Syed Amer Mahmoood",{"name":183,"orcid":184},"Aqil Tariq","https:\u002F\u002Forcid.org\u002F0000-0003-1196-1248",{"name":186,"orcid":187},"Khadeijah Yahya Faqeih","https:\u002F\u002Forcid.org\u002F0009-0002-4475-3453",{"name":189,"orcid":190},"Somayah Moshrif Alamri","https:\u002F\u002Forcid.org\u002F0009-0003-4601-9620",{"name":192,"orcid":193},"Eman Rafi Alamery","https:\u002F\u002Forcid.org\u002F0009-0006-7052-3239",{"name":195,"orcid":196},"Bilal Kemal","https:\u002F\u002Forcid.org\u002F0009-0002-2790-3229",{"name":198,"orcid":9},"Kucher E. Dmitry","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75473-5_reference.pdf",{"tldr":201,"method":202,"finding":203,"direction":75,"opportunity":204},"基于35年Landsat数据，分析巴基斯坦Jhang地区城市扩张与地表热环境变化及其气候人口驱动。","Landsat LULC分类、LST、SUHI、UTFVI、热点分析、相关与多元","城市面积从62.67增至341.36 km²，最高LST升5.92°C，热环境受土地覆盖、气候和人口","可引入多源遥感与机器学习，量化气候变异和人口增长对热环境的非线性贡献，并拓展至农业区热风险研究。","2026-10-15T23:30:32.222123Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":9,"source_name":212,"source_url":209,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":219,"tags":221,"primary_topic":225,"secondary_topics":226,"classification":227,"search_phrases":253,"slug":256,"view_count":57,"doi":257,"paper":258,"created_at":277},4678,"Remote Sensing-based Integration of Land Use\u002FLand Cover, Vegetation Cover, and Land Surface Temperature with Hydrological Implications in Tikrit City, Iraq","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488352\u002Fijce-v13i9p115","This study examines the integrated relationship among Land Use\u002FLand Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), with hydrological implications, in Tikrit City, Iraq, using Landsat data acquired in 2017 and 2025. LULC, NDVI, and LST layers were prepared using the same spatial boundary, and 4124 paired sample points were extracted from identical locations to support temporal and statistical comparison. The LULC results showed a clear reduction in Soil from 70.52% in 2017 to 49.88% in 2025, whereas Vegetation increased from 25.49% to 44.12%. Built Area also increased from 3.08% to 4.90%, indicating moderate urban expansion. The dominant land-cover transition was from Soil to Vegetation, covering 66,014.66 ha. Mean NDVI increased significantly from 0.061 to 0.075, while mean LST remained statistically stable, changing only from 41.992°C to 42.031°C. However, non-parametric tests indicated that the spatial distribution of LST changed significantly. The NDVI–LST relationship was positive in both years, with Pearson correlation increasing from 0.670 in 2017 to 0.709 in 2025. Regression analysis showed that NDVI explained 44.9% and 50.2% of LST variation in 2017 and 2025, respectively. Class-based analysis indicated that the dominant heat-contributing class shifted from Soil in 2017 to Built Area in 2025. These findings show that surface thermal behavior in Tikrit City cannot be explained by vegetation cover alone, but depends on land-cover composition, mixed pixels, agricultural activity, soil exposure, surface moisture, and seasonal conditions. From a hydrological perspective, the reduction of exposed soil, expansion of vegetation, and growth of built-up areas have implications for infiltration opportunity, runoff potential, evapotranspiration tendency, and surface moisture retention.","本研究利用2017年和2025年获取的Landsat数据，考察了伊拉克提克里特市土地利用\u002F土地覆盖（LULC）、归一化植被指数（NDVI）和地表温度（LST）之间的综合关系及其水文意义。LULC、NDVI和LST图层采用相同的空间边界制备，并从相同位置提取了4124对样本点，以支持时间与统计比较。LULC结果显示，土壤占比从2017年的70.52%明显下降至2025年的49.88%，而植被占比从25.49%上升至44.12%。建成区面积也从3.08%增至4.90%，表明存在中等程度的城市扩张。主导性土地覆盖转变为由土壤转为植被，覆盖面积达66,014.66公顷。平均NDVI从0.061显著增至0.075，而平均LST在统计上保持稳定，仅从41.992°C变为42.031°C。然而，非参数检验表明LST的空间分布发生了显著变化。NDVI与LST的关系在两年中均为正相关，Pearson相关系数从2017年的0.670增至2025年的0.709。回归分析表明，NDVI在2017年和2025年分别解释了LST变异的44.9%和50.2%。基于类别的分析表明，主要的热贡献类别从2017年的土壤转变为2025年的建成区。这些发现表明，提克里特市的地表热行为不能仅由植被覆盖来解释，而是取决于土地覆盖组成、混合像元、农业活动、土壤裸露、地表湿度和季节条件。从水文角度看，裸露土壤减少、植被扩张和建成区增长对下渗机会、径流潜力、蒸散发趋势和地表水分保持具有影响。","International Journal of Civil Engineering",68,{"impact":107,"substance":215,"depth":216,"authority":217,"freshness":13,"relevant":57,"comment":218},20,17,13,"基于Landsat双期影像量化伊拉克提克里特市土地利用、植被与地表温度耦合关系及其水文效应，数据扎实、结论明确，对农业遥感与土地变化研究有参考价值，但地域性强、国内关联度有限。",[220],{"name":212,"url":209},[21,222,22,223,224],"土地利用","植被覆盖","水文响应",{"code":47,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":14,"version":28,"method":29,"evidence":228},{"candidates":229,"agricultural_evidence":35},[230,235,242,249],{"code":47,"score":231,"supported":35,"title":232,"abstract":233,"tags":234},11,[111],[],[22,223,21],{"code":236,"score":79,"supported":35,"title":237,"abstract":238,"tags":241},"H2",[],[239,240],"土壤","soil",[],{"code":243,"score":79,"supported":35,"title":244,"abstract":245,"tags":248},"H3",[],[246,247],"蒸散发","evapotranspiration",[],{"code":157,"score":57,"supported":14,"title":250,"abstract":251,"tags":252},[],[],[222],[254,255],"Landsat 土地利用 植被覆盖 地表温度","土地利用 地表温度 植被覆盖 水文响应","Landsat土地利用植被覆盖地表温度-4678","10.14445\u002F23488352\u002Fijce-v13i9p115",{"doi":257,"openalex_id":259,"authors":260,"venue":212,"cited_by_count":15,"oa_url":271,"card":272,"direction":75,"ingested_from":76},"W7221352158",[261,263,265,268],{"name":262,"orcid":9},"Omar Taher Nafe'e",{"name":264,"orcid":9},"Dalia Shaker Mahdi",{"name":266,"orcid":267},"Lubna B. Mahmood","https:\u002F\u002Forcid.org\u002F0009-0000-4428-4891",{"name":269,"orcid":270},"Asmaa Abdul Jabbar Jamel","https:\u002F\u002Forcid.org\u002F0000-0001-7602-3426","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJCE\u002F2026\u002FVolume13-Issue9\u002FIJCE-V13I9P115.pdf",{"tldr":273,"method":274,"finding":275,"direction":75,"opportunity":276},"基于Landsat 2017与2025年数据，分析伊拉克提克里特市LULC、NDVI与LST的耦合关","Landsat影像、4124个配对样点、NDVI与LST反演、相关与回归分析。","植被增加、裸土减少，LST均值稳定但空间分布显著变化，NDVI-LST正相关增强，主导热贡献由裸土转","可结合土壤湿度与蒸散数据，探究NDVI-LST正相关机制及混合像元对城市农业水文过程的影响。","2026-10-11T23:30:30.165387Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":140,"source_url":281,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":285,"sources":287,"tags":289,"primary_topic":292,"secondary_topics":293,"classification":294,"search_phrases":305,"slug":308,"view_count":57,"doi":309,"paper":310,"created_at":329},4513,"Spatio-temporal assessment of mining-induced environmental changes using remote sensing: a case study of the Talcher Coalfield, Angul District, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74815-7","This study evaluates the spatio-temporal impacts of coal mining on environmental conditions in the Talcher coalfield region, India, using multi-temporal Landsat data (2000, 2014, and 2023) at 30 m resolution. Land use land cover (LULC) was derived using supervised classification (Maximum Likelihood Algorithm), while vegetation dynamics and thermal characteristics were assessed using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). In addition, atmospheric column densities were incorporated to analyze key atmospheric pollutants SO₂, NO₂, and CO and to assess air quality variations associated with mining activities. The integrated assessment highlights substantial environmental changes associated with mining and land-use transformation. The LULC analysis (2000–2023) reveals a decline in forest cover (41.33% to 37.37%) and agricultural land (45.62% to 45.04%), accompanied by an increase in built-up areas (6.84% to 8.54%) and mining areas (2.12% to 4.94%), indicating intensified anthropogenic pressure. LST and NDVI analyses (2014–2023) show changes in surface thermal conditions and vegetation characteristics. LST results indicate higher maximum and mean temperatures in mining and built-up areas, with changes between 2014 and 2023 varying among land-use classes, with mining areas showing the highest thermal values, where mean summer LST increased from 42.97 °C (2014) to 46.28 °C (2023), and mean winter LST increased from 38.67 °C to 41.81 °C. NDVI analysis indicates a decline in maximum NDVI values over the study period. The results show a spatial association between LULC changes, NDVI, and LST, with areas of mining and built-up expansion generally corresponding to lower NDVI and higher surface temperatures. Analysis of satellite-derived atmospheric column densities indicates higher SO₂, NO₂, and CO values in mining and industrial regions, with higher values observed during winter.","本研究利用30 m分辨率的多时相Landsat数据（2000年、2014年和2023年），评估了印度塔尔切尔煤田地区煤矿开采对环境条件的时空影响。土地利用\u002F土地覆盖（LULC）通过监督分类（最大似然算法）获得，植被动态和热特征则分别采用归一化差异植被指数（NDVI）和地表温度（LST）进行评估。此外，研究还引入大气柱密度以分析主要大气污染物SO₂、NO₂和CO，并评估与采矿活动相关的空气质量变化。综合评估揭示了与采矿和土地利用转型相关的显著环境变化。LULC分析（2000—2023年）显示，森林覆盖面积（从41.33%降至37.37%）和农业用地（从45.62%降至45.04%）减少，而建成区面积（从6.84%增至8.54%）和采矿区域面积（从2.12%增至4.94%）增加，表明人为压力加剧。LST和NDVI分析（2014—2023年）显示地表热条件和植被特征发生变化。LST结果表明，采矿区和建成区的最高温度和平均温度较高，2014年至2023年的变化因土地利用类型而异，其中采矿区的热值最高，夏季平均LST从42.97 °C（2014年）升至46.28 °C（2023年），冬季平均LST从38.67 °C升至41.81 °C。NDVI分析表明，研究期内最大NDVI值下降。结果表明，LULC变化、NDVI和LST之间存在空间关联，采矿和建成区扩张区域通常对应较低的NDVI和较高的地表温度。卫星反演的大气柱密度分析表明，采矿和工业区域的SO₂、NO₂和CO值较高，且冬季观测值更高。","2026-10-07T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":286},"该研究聚焦印度煤矿区环境遥感监测，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。",[288],{"name":140,"url":281},[21,22,290,291],"矿区生态","土地覆盖变化",{"code":47,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":14,"version":28,"method":29,"evidence":295},{"candidates":296,"agricultural_evidence":35},[297,301],{"code":47,"score":13,"supported":35,"title":298,"abstract":299,"tags":300},[111],[],[22,21],{"code":157,"score":48,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[291,290],[306,307],"Talcher 煤田 遥感","Landsat NDVI LST 矿区","Talcher煤田遥感-4513","10.1038\u002Fs41598-026-74815-7",{"doi":309,"openalex_id":311,"authors":312,"venue":140,"cited_by_count":15,"oa_url":323,"card":324,"direction":75,"ingested_from":76},"W7220855962",[313,315,317,320],{"name":314,"orcid":9},"Sk Riyajul Islam",{"name":316,"orcid":9},"Shilpa Pakhira",{"name":318,"orcid":319},"Abhilash Gogineni","https:\u002F\u002Forcid.org\u002F0000-0002-8350-468X",{"name":321,"orcid":322},"Regasa Yadeta Sembeta","https:\u002F\u002Forcid.org\u002F0000-0002-9686-3155","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74815-7_reference.pdf",{"tldr":325,"method":326,"finding":327,"direction":75,"opportunity":328},"基于多时相Landsat遥感评估印度煤矿区2000-2023年环境变化。","Landsat多时相数据、最大似然监督分类、NDVI、LST及SO₂\u002FNO₂\u002FC","采矿与建设用地扩张导致林地耕地减少，矿区夏季均温升至46.28°C，NDVI下降，污染物冬季偏高。","可借鉴其多指标遥感框架，研究矿区周边农田退化与作物表型响应，构建农业环境风险监测模型。","2026-10-09T23:30:27.643699Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":338,"score_detail":339,"sources":344,"tags":346,"primary_topic":349,"secondary_topics":350,"classification":351,"search_phrases":366,"slug":369,"view_count":34,"doi":370,"paper":371,"created_at":381},4094,"Cambios en el uso y cobertura de la tierra y su relación con la temperatura superficial terrestre en la cuenca del río Rocha, Bolivia, 1992–2022","https:\u002F\u002Fdoi.org\u002F10.23881\u002Fidupbo.026.1-4i","This study analyzes land use and land cover changes and their relationship with land surface temperature (LST) in the Rocha River basin, Bolivia, during 1992–2022. Landsat imagery from six dates was used to classify seven land use and land cover categories, estimate LST using remote sensing techniques, and statistically evaluate thermal differences among categories. Land cover changes were analyzed using annual change rates and a transition matrix, while LST differences were assessed with Welch's test and Games–Howell post hoc comparisons, and linear regression models were fitted for each date. Between 1992 and 2022, urban area increased by 66.99 km² and agriculture by 56.80 km², whereas grassland decreased by 104.76 km². Agricultural dynamics were not uniform, showing negative rates until 2004–2010 and positive rates thereafter. Land use and land cover categories showed statistically significant differences in LST on all six dates. In 2022, the highest mean LST values occurred in agriculture (21.79 °C), shrubland (20.40 °C), bare soil (20.22 °C), and urban area (19.76 °C), whereas the lowest values occurred in water bodies (15.46 °C), grassland (17.46 °C), and forest (17.57 °C). The regression models yielded R² values between 0.2699 and 0.5256. The results indicate a statistical association between land use and land cover categories and LST, but do not establish causal effects or long-term climatic trends.","本研究分析了1992—2022年玻利维亚罗查河流域土地利用与土地覆盖变化及其与地表温度（LST）的关系。研究使用六个日期的Landsat影像，对七类土地利用与土地覆盖进行分类，利用遥感技术估算地表温度，并对各类别之间的热差异进行统计评估。土地覆盖变化采用年变化率和转移矩阵进行分析，地表温度差异则通过Welch检验和Games–Howell事后比较进行评估，并针对每个日期拟合线性回归模型。1992—2022年间，城市面积增加66.99 km²，农业面积增加56.80 km²，而草地面积减少104.76 km²。农业动态并不均匀，2004—2010年前呈负增长速率，此后转为正增长速率。六个日期上，土地利用与土地覆盖类别之间的地表温度均存在统计学显著差异。2022年，地表温度均值最高出现在农业（21.79 °C）、灌丛（20.40 °C）、裸土（20.22 °C）和城市区域（19.76 °C），最低值出现在水体（15.46 °C）、草地（17.46 °C）和林地（17.57 °C）。回归模型的R²值介于0.2699至0.5256之间。结果表明，土地利用与土地覆盖类别同地表温度之间存在统计关联，但并未确立因果效应或长期气候趋势。","Revista Investigación & Desarrollo","2026-10-01T00:00:00Z",67,{"impact":107,"substance":340,"depth":341,"authority":217,"freshness":342,"relevant":57,"comment":343},21,16,9,"基于Landsat遥感定量分析玻利维亚Rocha河流域土地利用与地表温度关系，方法规范、数据翔实，但属区域案例研究，对国内三农信息化参考价值有限。",[345],{"name":336,"url":333},[21,222,22,347,348],"流域治理","玻利维亚",{"code":47,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":14,"version":28,"method":29,"evidence":352},{"candidates":353,"agricultural_evidence":35},[354,358,362],{"code":47,"score":107,"supported":35,"title":355,"abstract":356,"tags":357},[],[110,111],[22,21],{"code":236,"score":34,"supported":35,"title":359,"abstract":360,"tags":361},[],[240],[],{"code":157,"score":57,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[222],[367,368],"土地利用 地表温度 流域治理 玻利维亚","土地利用 地表温度","土地利用地表温度流域治理玻利维亚-4094","10.23881\u002Fidupbo.026.1-4i",{"doi":370,"openalex_id":372,"authors":373,"venue":336,"cited_by_count":15,"oa_url":333,"card":376,"direction":75,"ingested_from":76},"W7216167048",[374],{"name":375,"orcid":9},"Benjamin Gossweiler Herrera",{"tldr":377,"method":378,"finding":379,"direction":75,"opportunity":380},"分析玻利维亚罗查河流域1992-2022年土地利用变化与地表温度的关系。","Landsat影像分类七类地物，遥感反演LST，Welch检验与线性回归。","城市和农业扩张、草地减少；农业、灌木、裸地LST最高，水体、草地、林地最低。","可结合多源遥感与因果推断，量化不同农业扩张模式对地表温度的贡献，支撑流域热环境调控。","2026-10-03T23:30:35.454291Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":337,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":389,"score_detail":390,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":404,"classification":405,"search_phrases":418,"slug":421,"view_count":48,"doi":422,"paper":423,"created_at":438},4086,"Drought, conflict, and recovery: Disentangling the drivers of vegetation dynamics and surface temperature change in Kirkuk Governorate, Iraq (2000-2024)","https:\u002F\u002Fdoi.org\u002F10.15243\u002Fjdmlm.2026.134.10955","This study presents a 24-year spatiotemporal analysis of vegetation dynamics and land surface temperature (LST) in Kirkuk Governorate, northern Iraq, from 2000 to 2024. MODIS NDVI (MOD13Q1), MODIS LST (MOD11A2), and CHIRPS rainfall data were processed in Google Earth Engine to assess environmental change across four phases: baseline (2000-2005), pre-conflict (2010-2013), ISIS conflict (2014-2017), and post-conflict recovery (2018-2024). Vegetation trends were evaluated using the Mann-Kendall test and Sen's Slope estimator. Mean annual NDVI increased by 21.6%, from 0.1995 during the baseline phase to 0.2426 during the recovery phase. Significant greening occurred across 56.2% of the governorate (5,470 km²), while only 0.8% (78 km²) experienced browning. Two major periods of vegetation decline were identified: the 2007-2009 regional drought, when mean NDVI fell to 0.1397, and the 2014-2017 ISIS conflict, during which vegetation declined in western districts despite near-average rainfall, indicating conflict as an independent driver of degradation. Mean annual LST decreased from 38.37 °C in 2000 to 32.33 °C in 2024, consistent with the inverse relationship between vegetation cover and surface temperature. The findings provide quantitative evidence of conflict-driven vegetation degradation followed by post-conflict ecological recovery, offering valuable insights for land management and agricultural recovery planning in semi-arid, post-conflict regions.","本研究对伊拉克北部基尔库克省2000年至2024年间的植被动态与地表温度（LST）进行了24年时空分析。在Google Earth Engine中处理了MODIS NDVI（MOD13Q1）、MODIS LST（MOD11A2）和CHIRPS降雨数据，以评估四个阶段的环境变化：基线期（2000—2005年）、冲突前期（2010—2013年）、ISIS冲突期（2014—2017年）和冲突后恢复期（2018—2024年）。植被趋势采用Mann-Kendall检验和Sen斜率估计法进行评估。年均NDVI增加了21.6%，从基线期的0.1995上升至恢复期的0.2426。显著绿化面积占全省的56.2%（5,470 km²），而仅0.8%（78 km²）出现褐化。研究识别出两个主要的植被退化时期：2007—2009年区域性干旱期间，平均NDVI降至0.1397；以及2014—2017年ISIS冲突期间，尽管降雨量接近平均水平，西部地区的植被仍然退化，表明冲突是植被退化的独立驱动因素。年均LST从2000年的38.37 °C降至2024年的32.33 °C，与植被覆盖和地表温度之间的反比关系一致。研究结果提供了冲突驱动植被退化及冲突后生态恢复的定量证据，为半干旱冲突后地区的土地管理和农业恢复规划提供了有价值的参考。","Journal of Degraded and Mining Lands Management",70,{"impact":107,"substance":391,"depth":392,"authority":217,"freshness":342,"relevant":57,"comment":393},22,18,"基于MODIS与CHIRPS的24年遥感长时序分析，量化干旱与冲突对植被和地表温度的影响，方法规范、数据扎实，对半干旱战后地区农业恢复规划有参考价值，但地域性强、与国内三农信息化关联有限。",[395],{"name":388,"url":385},[21,22,397,38,398],"植被动态","战后农业恢复",{"code":33,"slug":400,"name":401,"path":402,"parent_code":403},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":103,"locked":14,"version":28,"method":29,"evidence":406},{"candidates":407,"agricultural_evidence":35},[408,414],{"code":33,"score":409,"supported":35,"title":410,"abstract":412,"tags":413},14,[411],"drought",[38,411],[],{"code":47,"score":34,"supported":14,"title":415,"abstract":416,"tags":417},[],[],[22,397,21],[419,420],"基尔库克 植被动态 遥感","MODIS NDVI 地表温度","基尔库克植被动态遥感-4086","10.15243\u002Fjdmlm.2026.134.10955",{"doi":422,"openalex_id":424,"authors":425,"venue":388,"cited_by_count":15,"oa_url":432,"card":433,"direction":75,"ingested_from":76},"W7214972947",[426,429],{"name":427,"orcid":428},"Azhar Hussein Razuqi","https:\u002F\u002Forcid.org\u002F0009-0002-1202-5953",{"name":430,"orcid":431},"Mustafa Albdairi","https:\u002F\u002Forcid.org\u002F0009-0002-6673-363X","https:\u002F\u002Fjdmlm.ub.ac.id\u002Findex.php\u002Fjdmlm\u002Farticle\u002Fdownload\u002F17867\u002F2159",{"tldr":434,"method":435,"finding":436,"direction":75,"opportunity":437},"分析伊拉克基尔库克2000-2024年植被与地表温度动态，区分干旱与冲突的驱动作用。","MODIS NDVI\u002FLST与CHIRPS降雨数据，在GEE中用Mann-Ken","NDVI总体增21.6%，干旱和ISIS冲突分别导致植被下降，冲突是独立退化驱动因素。","可结合更高分辨率遥感与地面调查，量化冲突后农业恢复力及气候适应策略。","2026-10-03T23:30:32.505144Z"]