[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4215":3,"related-4215":82},{"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":47,"slug":50,"view_count":15,"doi":51,"paper":52,"created_at":81},4215,"Spectral index and machine learning-based evaluation of coastal urban thermal dynamics using google earth engine","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-73768-1","Abstract This present study introduces a cloud-based geospatial and machine learning framework for assessing coastal urban heat island (UHI) dynamics in Thoothukudi coastal region, Southern India, using 12 seasonal Landsat composites (2010–2025) processed through Google Earth Engine (GEE). Seasonal classification followed Indian Meteorological Department definitions: Summer (March-June), Northeast Monsoon (October-November) and Winter (December-February). Multi-sensor Landsat Collection 2 Tier 1 Level-2 data included Landsat 5\u002F7 (2010), Landsat 8 (2015) and Landsat 8\u002F9 (2020, 2025). Land surface temperature (LST) was retrieved using USGS Collection 2 scale-factor protocol, capped to 0–65 °C to suppress cloud-contaminated pixels. UHI intensity (UHI = LST − P₂₅(LST)) and Urban Thermal Field Variance Index (UTFVI) were computed alongside four spectral indices (NDVI, NDBI, NDWI, NDMI). Summer was the hottest season in three of four observation years (mean LST: 38.69–48.67 °C). The 2020 Northeast Monsoon composite was excluded due to cloud contamination. Mean UHI intensity ranged from 2.03 to 5.17 °C. Pearson correlation confirmed NDBI as the strongest positive LST predictor ( r = 0.30–0.60), while NDMI mirrors this inversely. Random Forest and Gradient Tree Boosting models were trained on seven predictors using dual validation (70\u002F30 split and 2 km spatial block cross-validation). Model performance yielded R² of 0.301–0.476 and RMSE of 2.74–6.23 °C with physically realistic values. GTB permutation feature importance identified NDBI and SWIR1 as dominant predictors. Ensemble scenario illustrations for 2030 and 2050, presented as linear extrapolations rather than climate-model forecasts, suggest sustained thermal stress in industrial and port zones with explicit uncertainty quantification (± 2.75 to ± 8.61 °C). The study demonstrates that rigorously validated Landsat time-series analysis provides actionable coastal urban thermal intelligence for heat adaptation planning aligned with SDGs 11, 13 and 3. The framework is applied here as the first four-epoch, IMD-season-resolved thermal characterisation of the Thoothukudi coast and the 2030 and 2050 outputs are intended to support the spatial targeting of persistent thermal hotspots rather than the prediction of absolute future temperatures.","摘要 本研究提出了一种基于云的地理空间与机器学习框架，用于评估印度南部Thoothukudi沿海地区的沿海城市热岛（UHI）动态。研究使用Google Earth Engine（GEE）处理了12期季节性Landsat合成影像（2010–2025年）。季节划分遵循印度气象局的定义：夏季（3–6月）、东北季风季（10–11月）和冬季（12–2月）。多传感器Landsat Collection 2 Tier 1 Level-2数据包括Landsat 5\u002F7（2010年）、Landsat 8（2015年）和Landsat 8\u002F9（2020年、2025年）。地表温度（LST）采用USGS Collection 2比例因子协议反演，并限定在0–65 °C以抑制受云污染的像元。在计算城市热岛强度（UHI = LST − P₂₅(LST)）和城市热场变异指数（UTFVI）的同时，还计算了四个光谱指数（NDVI、NDBI、NDWI、NDMI）。在四个观测年份中的三年，夏季均为最热季节（平均LST：38.69–48.67 °C）。2020年东北季风季合成影像因云污染而被排除。平均UHI强度范围为2.03至5.17 °C。Pearson相关分析证实，NDBI是LST最强的正向预测因子（r = 0.30–0.60），而NDMI则呈相反的镜像关系。随机森林和梯度树提升模型基于七个预测因子进行训练，并采用双重验证（70\u002F30划分和2 km空间块交叉验证）。模型性能为R² 0.301–0.476，RMSE为2.74–6.23 °C，数值具有物理合理性。GTB置换特征重要性识别出NDBI和SWIR1为主导预测因子。2030年和2050年的集合情景示意以线性外推而非气候模型预测的形式呈现，表明工业和港口区域将持续存在热胁迫，并明确给出了不确定性量化（± 2.75至± 8.61 °C）。研究表明，经过严格验证的Landsat时间序列分析可为与可持续发展目标11、13和3相一致的高温适应规划提供可操作的沿海城市热环境情报。该框架在此作为Thoothukudi海岸首个四期、按IMD季节解析的热特征刻画而应用，2030年和2050年的输出旨在支持持续热点的空间定位，而非预测未来绝对温度。",null,"Scientific Reports","2026-10-03T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究聚焦印度海岸城市热岛遥感与机器学习建模，属城市气候与遥感领域，与三农、农业信息化、智慧农业无直接关联，不进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","城市热岛","海岸带",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":46},[31,40],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":39},"I2",14,true,[36],"machine learning",[38,36],"机器学习",[],{"code":41,"score":42,"supported":14,"title":43,"abstract":44,"tags":45},"I3",1,[],[],[21],"农业相关性需要复核",[48,49],"Thoothukudi 海岸 城市热岛","Google Earth Engine 地表温度","Thoothukudi海岸城市热岛-4215","10.1038\u002Fs41598-026-73768-1",{"doi":51,"openalex_id":53,"authors":54,"venue":10,"cited_by_count":15,"oa_url":72,"card":73,"direction":79,"ingested_from":80},"W7218522584",[55,57,59,62,65,67,70],{"name":56,"orcid":9},"A. Ernest Amita Roy",{"name":58,"orcid":9},"John Prince Soundaranayagam",{"name":60,"orcid":61},"A. Antony Alosanai Promilton","https:\u002F\u002Forcid.org\u002F0000-0002-6762-0103",{"name":63,"orcid":64},"Neelam Sidhu","https:\u002F\u002Forcid.org\u002F0000-0002-5612-1085",{"name":66,"orcid":9},"V. Stephen Pitchaimani",{"name":68,"orcid":69},"Hazratullah Paktin","https:\u002F\u002Forcid.org\u002F0009-0008-0780-7639",{"name":71,"orcid":9},"Abhishek Sharma","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-73768-1_reference.pdf",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"基于GEE与机器学习评估印度沿海城市2010–2025年热岛动态并外推2030\u002F2050情景。","Landsat时序合成、LST反演、NDVI\u002FNDBI\u002FNDWI\u002FNDMI指数、","NDBI是LST最强正相关因子，工业与港口区热胁迫持续，模型R²为0.30–0.48。","农业遥感与作物表型","可将该GEE+ML热环境框架迁移至农业区，耦合作物物候与灌溉，评估农田热胁迫与产量风险。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:09.368529Z",{"total":83,"page":42,"page_size":83,"items":84},6,[85,138,209,270,324,389],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":91,"source_url":88,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":93,"sources":95,"tags":97,"primary_topic":100,"secondary_topics":105,"classification":106,"search_phrases":117,"slug":120,"view_count":15,"doi":121,"paper":122,"created_at":137},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":94},"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。",[96],{"name":91,"url":88},[21,98,22,99],"地表温度","MODIS",{"code":41,"slug":101,"name":102,"path":103,"parent_code":104},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":107,"locked":14,"version":27,"method":28,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":34},[110],{"code":41,"score":111,"supported":34,"title":112,"abstract":113,"tags":116},8,[],[114,115],"遥感","remote sensing",[98,21],[118,119],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":121,"openalex_id":123,"authors":124,"venue":91,"cited_by_count":15,"oa_url":88,"card":132,"direction":77,"ingested_from":80},"W7222794736",[125,128,130],{"name":126,"orcid":127},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":129,"orcid":9},"Shubham Mor",{"name":131,"orcid":9},"Gaurav Kalotra",{"tldr":133,"method":134,"finding":135,"direction":77,"opportunity":136},"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":139,"title":140,"url":141,"summary":142,"summary_zh":143,"content":9,"source_name":10,"source_url":141,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":145,"sources":147,"tags":149,"primary_topic":9,"secondary_topics":151,"classification":152,"search_phrases":165,"slug":168,"view_count":15,"doi":169,"paper":170,"created_at":208},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。","2026-10-13T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":146},"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。",[148],{"name":10,"url":141},[21,98,22,150],"土地利用变化",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":153},{"candidates":154,"agricultural_evidence":14,"reason":46},[155,160],{"code":41,"score":156,"supported":14,"title":157,"abstract":158,"tags":159},2,[],[],[98,21],{"code":161,"score":42,"supported":14,"title":162,"abstract":163,"tags":164},"H6",[],[],[150],[166,167],"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":169,"openalex_id":171,"authors":172,"venue":10,"cited_by_count":15,"oa_url":202,"card":203,"direction":77,"ingested_from":80},"W7221689409",[173,175,177,180,183,185,188,191,194,197,200],{"name":174,"orcid":9},"Rui Li",{"name":176,"orcid":9},"Yifei Yuan",{"name":178,"orcid":179},"Zainab Tahir","https:\u002F\u002Forcid.org\u002F0000-0002-1459-3527",{"name":181,"orcid":182},"Muhammad Haseeb","https:\u002F\u002Forcid.org\u002F0000-0002-0697-6894",{"name":184,"orcid":9},"Syed Amer Mahmoood",{"name":186,"orcid":187},"Aqil Tariq","https:\u002F\u002Forcid.org\u002F0000-0003-1196-1248",{"name":189,"orcid":190},"Khadeijah Yahya Faqeih","https:\u002F\u002Forcid.org\u002F0009-0002-4475-3453",{"name":192,"orcid":193},"Somayah Moshrif Alamri","https:\u002F\u002Forcid.org\u002F0009-0003-4601-9620",{"name":195,"orcid":196},"Eman Rafi Alamery","https:\u002F\u002Forcid.org\u002F0009-0006-7052-3239",{"name":198,"orcid":199},"Bilal Kemal","https:\u002F\u002Forcid.org\u002F0009-0002-2790-3229",{"name":201,"orcid":9},"Kucher E. Dmitry","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75473-5_reference.pdf",{"tldr":204,"method":205,"finding":206,"direction":77,"opportunity":207},"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":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":217,"sources":219,"tags":221,"primary_topic":9,"secondary_topics":223,"classification":224,"search_phrases":236,"slug":239,"view_count":42,"doi":240,"paper":241,"created_at":269},4830,"Spatiotemporal dynamics and drivers of surface urban heat island intensity in Dhaka, Bangladesh using geospatial remote sensing","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00767-0","Abstract Rapid urbanization has a profound impact on urban thermal environments, particularly in densely populated megacities. This study aims to assess the spatiotemporal dynamics of land surface temperature (LST), surface urban heat island (SUHI) intensity, land use\u002Fland cover (LULC) changes, and their primary driving factors in Dhaka City Corporation (DCC), Bangladesh, during the pre-monsoon season from 1990 to 2024. Using multi-temporal Landsat imagery and ancillary spatial datasets on Google Earth Engine (GEE), we observed a 194.54% increase in urban land, alongside substantial declines in vegetation (45.99%) and water bodies (75.57%). These classifications were validated with strong accuracy metrics (Overall Accuracy > 92%, Kappa > 89%, K-fold > 95%). Concurrently, LST rose dramatically, with areas exceeding 40 °C expanding to 70.06% by 2024, and traditional cool zones nearly disappearing. SUHI analysis showed a growing heat island effect, with recent nocturnal SUHI values surpassing daytime levels, indicating increased nighttime heat retention. Correlation and multiple linear regression analyses (R² = 0.801) identified NDBI, population density, and nighttime light intensity as the dominant positive drivers of LST, while vegetation and water bodies offered significant cooling potential. These findings highlight the urgent need to integrate ecological benchmarks (such as the 3-30-300 rule) and Sponge City designderscorn paradigms into Dhaka’s climate-resilient urban planning framework. Ultimately, this cloud-based multi-factor modeling framework provides a highly replicable, open-access blueprint to help rapidly growing megacities worldwide diagnose thermal vulnerability and mitigate escalating heat stress.","摘要 快速城市化对城市热环境产生了深远影响，尤其是在人口密集的超大城市中。本研究旨在评估1990年至2024年季风前季节孟加拉国达卡市辖区（DCC）地表温度（LST）、地表城市热岛（SUHI）强度、土地利用\u002F土地覆盖（LULC）变化及其主要驱动因素的时空动态。利用Google Earth Engine（GEE）平台上的多时相Landsat影像和辅助空间数据集，我们观察到城市用地增加了194.54%，同时植被（45.99%）和水体（75.57%）大幅减少。这些分类结果通过了严格的精度验证（总体精度>92%，Kappa>89%，K折>95%）。与此同时，LST急剧上升，超过40 °C的区域到2024年扩大至70.06%，传统凉爽区几乎消失。SUHI分析显示热岛效应不断增强，近期夜间SUHI值超过白天水平，表明夜间热量滞留加剧。相关分析和多元线性回归分析（R² = 0.801）确定NDBI、人口密度和夜间灯光强度是LST的主要正向驱动因素，而植被和水体则提供了显著的降温潜力。这些发现凸显了将生态基准（如3-30-300规则）和海绵城市设计范式纳入达卡气候韧性城市规划框架的迫切需求。最终，这一基于云的多因素建模框架提供了一个高度可复制、开放获取的蓝图，有助于全球快速发展的超大城市诊断热脆弱性并缓解不断升级的热胁迫。","Discover Geoscience","2026-10-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":218},"研究孟加拉国达卡城市热岛与地表温度遥感监测，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。",[220],{"name":215,"url":212},[21,22,150,222],"孟加拉国",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":225},{"candidates":226,"agricultural_evidence":14,"reason":46},[227,232],{"code":41,"score":228,"supported":34,"title":229,"abstract":230,"tags":231},9,[115],[],[21],{"code":161,"score":42,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[150],[237,238],"达卡 城市热岛 遥感","Landsat 地表温度 城市扩张","达卡城市热岛遥感-4830","10.1007\u002Fs44288-026-00767-0",{"doi":240,"openalex_id":242,"authors":243,"venue":215,"cited_by_count":15,"oa_url":263,"card":264,"direction":77,"ingested_from":80},"W7221469610",[244,247,250,253,256,258,260],{"name":245,"orcid":246},"Shacin Chandra Saha","https:\u002F\u002Forcid.org\u002F0009-0004-4007-145X",{"name":248,"orcid":249},"Ummya Ayman Satil","https:\u002F\u002Forcid.org\u002F0009-0002-1639-7156",{"name":251,"orcid":252},"Nitun Paul","https:\u002F\u002Forcid.org\u002F0009-0008-3515-8496",{"name":254,"orcid":255},"Dipok Chandra Nath","https:\u002F\u002Forcid.org\u002F0009-0006-8250-7244",{"name":257,"orcid":9},"Farhana Rahman Nepa",{"name":259,"orcid":9},"Mir Md. Tasnim Alam",{"name":261,"orcid":262},"Sukhen Goswami","https:\u002F\u002Forcid.org\u002F0009-0007-8342-6822","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00767-0.pdf",{"tldr":265,"method":266,"finding":267,"direction":77,"opportunity":268},"基于Landsat与GEE分析达卡1990-2024年地表温度与热岛时空变化及驱动因素。","多时相Landsat影像、GEE云平台、LULC分类、相关与多元线性回归。","城市扩张194.54%，40°C以上区域达70.06%，NDBI、人口密度与夜间灯光是主要增温驱动。","可将该云平台多因子框架迁移至城郊农业区，探究热岛对农田蒸散与作物产量的耦合影响。","2026-10-13T23:30:25.362277Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":278,"sources":280,"tags":282,"primary_topic":9,"secondary_topics":285,"classification":286,"search_phrases":300,"slug":303,"view_count":42,"doi":304,"paper":305,"created_at":323},4363,"Spatiotemporal dynamics of urbanisation induced environmental quality in arid city using a grid-based analysis in Jodhpur India from 2001 to 2021","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01067-7","Rapid urbanisation in arid regions intensifies heat exposure, vegetation loss, and particulate pollution, yet medium-sized desert cities remain underrepresented in integrated environmental assessments. We quantified spatiotemporal changes in environmental quality in Jodhpur, India, from 2001 to 2021 using a novel grid-based Environmental Quality Index (EQI). Methodological innovation lies in three elements: incorporation of nighttime land surface temperature (LST) to capture urban heat retention relevant to population exposure; application of a uniform 32-grid intra-urban framework to detect micro-spatial heterogeneity; and integration of satellite-derived PM 2.5 with vegetation–thermal dynamics using a geometric mean to minimise compensatory bias. Indicators, LST (land degradation), NDVI-derived vegetation cover (ecosystem regulation), and PM 2.5 (atmospheric quality), were selected for direct biophysical relevance, long-term satellite consistency, and exposure sensitivity, rather than proxy measures such as built-up indices or population density. Annual MODIS, Landsat, and satellite model fused PM 2.5 datasets (1 km) were standardised (0–100); trends were analysed using Mann–Kendall and Sen’s slope estimators; interrelationships were assessed via multiple linear regression. Land-use classification from Landsat achieved > 85% overall accuracy. Seventy-one percent of grids showed significant nighttime warming ( p \u003C 0·05), with hotspots reaching + 1·36 °C. Core urban areas recorded vegetation decline (mean ΔVC − 0·02), whereas peri-urban grids showed moderate greening (ΔVC up to + 0·07). Mean PM 2.5 ranged 4–7 μg\u002Fm 3 and was strongly associated with vegetation loss (R 2 = 0·66) and elevated LST (R 2 = 0·49). The proportion of city area classified as poor or very poor EQI increased from 15·1% (2001) to 41·5% (2021). Vegetation decline was the dominant predictor of PM 2.5 increase (β = − 0·44; p = 0·01). These findings indicate substantial environmental deterioration and provide a scalable framework for climate-resilient urban planning in arid cities.","干旱地区快速城市化加剧了热暴露、植被丧失和颗粒物污染，但中型沙漠城市在综合环境评估中仍代表性不足。我们采用一种新颖的基于网格的环境质量指数（EQI），量化了印度焦特布尔2001年至2021年环境质量的时空变化。方法学创新体现在三个方面：纳入夜间地表温度（LST）以捕捉与人口暴露相关的城市热量滞留；应用统一的32网格城市内部框架以检测微观空间异质性；以及利用几何平均整合卫星反演的PM2.5与植被—热力动态，以最小化补偿偏差。指标选取LST（土地退化）、NDVI衍生的植被覆盖度（生态系统调节）和PM2.5（大气质量），因其具有直接的生物物理相关性、长期卫星一致性和暴露敏感性，而非建成区指数或人口密度等替代指标。年度MODIS、Landsat和卫星模型融合PM2.5数据集（1 km）经标准化处理（0–100）；趋势采用Mann–Kendall和Sen's斜率估计器分析；相互关系通过多元线性回归评估。基于Landsat的土地利用分类总体精度超过85%。71%的网格显示显著的夜间增温（p \u003C 0.05），热点区域增温达+1.36 °C。核心城区记录到植被下降（平均ΔVC −0.02），而城市边缘网格则显示中度绿化（ΔVC最高达+0.07）。PM2.5均值范围为4–7 μg\u002Fm³，与植被丧失（R² = 0.66）和LST升高（R² = 0.49）密切相关。被归类为差或极差EQI的城市面积比例从15.1%（2001年）增至41.5%（2021年）。植被下降是PM2.5增加的主导预测因子（β = −0.44；p = 0.01）。这些发现表明环境质量显著恶化，并为干旱城市的气候韧性城市规划提供了一个可扩展框架。","Discover Environment","2026-10-05T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":279},"研究印度焦特布尔干旱城市环境质量时空变化，属城市环境遥感范畴，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。",[281],{"name":276,"url":273},[21,22,283,284],"植被覆盖","PM2.5",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":287},{"candidates":288,"agricultural_evidence":14,"reason":46},[289,296],{"code":290,"score":291,"supported":34,"title":292,"abstract":293,"tags":295},"H7",3,[],[294],"干旱",[],{"code":41,"score":156,"supported":14,"title":297,"abstract":298,"tags":299},[],[],[283,21],[301,302],"焦特布尔 环境质量指数","印度 干旱城市 城市化","焦特布尔环境质量指数-4363","10.1007\u002Fs44274-026-01067-7",{"doi":304,"openalex_id":306,"authors":307,"venue":276,"cited_by_count":15,"oa_url":317,"card":318,"direction":77,"ingested_from":80},"W7220453628",[308,311,314],{"name":309,"orcid":310},"Amjed Ali","https:\u002F\u002Forcid.org\u002F0000-0002-0866-2346",{"name":312,"orcid":313},"Kheraj Kheraj","https:\u002F\u002Forcid.org\u002F0000-0002-1107-5628",{"name":315,"orcid":316},"Arshad Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-5196-928X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01067-7.pdf",{"tldr":319,"method":320,"finding":321,"direction":77,"opportunity":322},"用网格化环境质量指数评估印度焦特布尔2001-2021年城市化引发的环境质量时空变化。","基于MODIS\u002FLandsat与PM2.5数据构建32格网EQI，用Mann-K","71%网格夜间显著升温，环境质量差区域从15.1%升至41.5%，植被减少是PM2.5上升主因。","可将该网格化EQI框架迁移至干旱区农业景观，耦合作物物候与夜间热暴露评估。","2026-10-07T23:30:28.927907Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":332,"sources":334,"tags":336,"primary_topic":9,"secondary_topics":340,"classification":341,"search_phrases":349,"slug":352,"view_count":156,"doi":353,"paper":354,"created_at":388},4100,"How extreme river discharge events shape coastal biogeochemical dynamics: the 2024 northern Adriatic case","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fsp-7-osr10-18-2026","In 2024, the northern Adriatic Sea experienced marked levels of eutrophication, as indicated by unusually high surface chlorophyll concentrations observed by remote sensing and in-situ measurements. In addition, exceptionally high Po river discharges, in the form of repeated and intense events, were recorded from March to June and in October. We used Copernicus Marine Service satellite and model products for the Mediterranean Sea, together with a high-resolution (500 m) Italian coastal model, to quantify chlorophyll anomalies in the area with respect to 1999–2022 climatology and investigated the marine ecosystem functioning in connection with river discharges, phosphate input and sea surface temperature as key drivers. We found that chlorophyll values exceeded 100 % above climatological levels in early spring, followed by sustained anomalies in summer and autumn. Primary production remained elevated from the early spring onward, with a significant anomaly peaking in early summer, concurrent with renewed Po river discharges and rising sea surface temperatures. Our findings highlight the central importance of riverine freshwater and nutrient inputs in controlling the northern Adriatic ecosystem, in combination with circulation and thermal variability. Moreover, this study shows how forecasting systems play a pivotal role in monitoring eutrophication and potential biomass accumulation in the context of episodic river extremes and increasing climate variability, with high-resolution coastal modeling providing additional valuable insights into spatial patterns and localized eutrophication dynamics.","2024年，北亚得里亚海出现了明显的富营养化现象，遥感与原位观测均记录到异常高的表层叶绿素浓度。此外，3月至6月及10月期间，波河径流量异常偏高，表现为多次重复且强度较大的事件。我们利用哥白尼海洋服务提供的地中海卫星与模型产品，结合意大利高分辨率（500 m）近岸模型，量化了该区域叶绿素相对于1999—2022年气候态的异常，并研究了海洋生态系统功能与河流径流、磷酸盐输入及海表温度等关键驱动因子之间的关系。我们发现，早春叶绿素浓度较气候态水平高出100%以上，随后在夏季和秋季持续出现异常。初级生产力自早春起持续升高，在初夏达到显著峰值，同时伴随波河径流的再次增强和海表温度的上升。研究结果凸显了河流淡水与营养盐输入在控制北亚得里亚海生态系统中的核心作用，并与环流和热力变化共同产生影响。此外，本研究表明，在极端河流事件频发和气候变率不断增加的背景下，预报系统在监测富营养化及潜在生物量积累方面发挥着关键作用，而高分辨率近岸建模为理解空间格局和局地富营养化动态提供了重要的补充认识。","State of the Planet","2026-09-30T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":333},"研究聚焦北亚得里亚海富营养化与河流径流，属海洋生态与遥感领域，与三农、农业信息化主题无关，不建议进入每日精选。",[335],{"name":330,"url":327},[21,23,337,338,339],"海洋生态","富营养化","河流径流",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":342},{"candidates":343,"agricultural_evidence":14,"reason":46},[344],{"code":41,"score":345,"supported":34,"title":346,"abstract":347,"tags":348},7,[],[114,115],[21],[350,351],"北亚得里亚海 叶绿素 波河","Copernicus Marine 海岸模型","北亚得里亚海叶绿素波河-4100","10.5194\u002Fsp-7-osr10-18-2026",{"doi":353,"openalex_id":355,"authors":356,"venue":330,"cited_by_count":15,"oa_url":381,"card":382,"direction":77,"ingested_from":80},"W7214910604",[357,360,363,366,369,372,375,378],{"name":358,"orcid":359},"Valeria Di Biagio","https:\u002F\u002Forcid.org\u002F0000-0002-6160-1825",{"name":361,"orcid":362},"Stefano Querin","https:\u002F\u002Forcid.org\u002F0000-0002-4658-860X",{"name":364,"orcid":365},"Carolina Amadio","https:\u002F\u002Forcid.org\u002F0000-0003-1565-4165",{"name":367,"orcid":368},"Giorgio Bolzon","https:\u002F\u002Forcid.org\u002F0000-0003-3285-5049",{"name":370,"orcid":371},"Laura Feudale","https:\u002F\u002Forcid.org\u002F0000-0002-8631-2499",{"name":373,"orcid":374},"Stefano Piani","https:\u002F\u002Forcid.org\u002F0000-0002-5539-6669",{"name":376,"orcid":377},"Simone Spada","https:\u002F\u002Forcid.org\u002F0000-0002-6909-4290",{"name":379,"orcid":380},"Gianpiero Cossarini","https:\u002F\u002Forcid.org\u002F0000-0001-7803-8568","https:\u002F\u002Fsp.copernicus.org\u002Farticles\u002F7-osr10\u002F18\u002F2026\u002Fsp-7-osr10-18-2026.pdf",{"tldr":383,"method":384,"finding":385,"direction":386,"opportunity":387},"研究2024年北亚得里亚海极端河流排放如何驱动叶绿素异常与富营养化。","用哥白尼卫星与模型产品及500米意大利海岸模型，对比1999–2022气候态。","春季叶绿素超气候态100%以上，夏秋持续异常，初级生产显著升高。","农业绿色发展与碳","可延伸研究极端河流事件下近海碳汇与富营养化的耦合机制及高分辨率预报。","2026-10-03T23:30:37.707929Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":403,"tags":405,"primary_topic":9,"secondary_topics":409,"classification":410,"search_phrases":428,"slug":431,"view_count":291,"doi":432,"paper":433,"created_at":448},2665,"Assessing historical climatic impacts on wetland change using satellite imagery: a transferable framework demonstrated on the Texas coast","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmars.2026.1911273","Coastal wetlands provide critical ecosystem services worldwide but are increasingly threatened by climate-driven stressors, including sea-level rise, coastal flooding, drought, wave erosion, and extreme temperature events. Long-term, spatially consistent monitoring is needed to quantify wetland transformation and identify ecosystems most vulnerable to these stressors. The objective of the study was to develop tools that harmonize historical Landsat (5, 7, 8, 9) and Sentinel (1, 2) satellite datasets to map wetlands and identify wetland areas most vulnerable to climate-driven stress, while being readily transferable to other coastal regions. The Texas Coastal Bend Region is presented as a case study to demonstrate the utility of these tools in evaluating wetland change. To do this, the Multi Decadal Wetland Extent Tool (WET-MUD) was developed in Google Earth Engine, along with the Wetland Health Analysis: Multi-Source (WHAMS). WET-MUD is a semi-automated wetland classification tool that produces annual maps with pixels classified as wetland, upland, or water from 1984 to 2024. WHAMS is a Google Earth Engine tool and framework that evaluates changes to wetland plant health (using Normalized Difference Vegetation Index (NDVI) as a proxy) at a monthly level for the 41-year period of record. Total classification accuracy for the Coastal Bend Region of Texas averaged 93% for the 40-year study period, and wetland pixel classification averaged 82%. Annual wetland extent maps show that since 1984, the region has experienced approximately 193 km 2 of net wetland loss, representing 17% of the initial wetland extent, with most of these losses (95%) occurring between 2008 and 2016. Analyses of NDVI for wetland areas show that 43 km 2 of coastal palustrine (low-lying, freshwater) wetlands were especially sensitive to repeated drought events (2008-2010, 2011-2015), representing 22% of the total coastal wetland losses. The publicly available tools developed in this study provide insight into wetland resilience by rapidly measuring both wetland extents and health at a regional, multi-decadal scale in a way that can be reproduced for areas beyond the Coastal Bend of Texas.","沿海湿地为全球提供关键的生态系统服务，但日益受到气候驱动压力的威胁，包括海平面上升、海岸洪水、干旱、波浪侵蚀和极端温度事件。需要长期、空间一致的监测来量化湿地变化并识别最易受这些压力影响的生态系统。本研究的目标是开发工具，以协调历史Landsat（5、7、8、9）和Sentinel（1、2）卫星数据集，从而绘制湿地分布图并识别最易受气候驱动压力影响的湿地区域，同时能够方便地推广到其他沿海地区。本文以德克萨斯州沿海湾岸地区为案例研究，展示这些工具在评估湿地变化方面的实用性。为此，在Google Earth Engine中开发了多年代际湿地范围工具（WET-MUD），以及湿地健康分析：多源工具（WHAMS）。WET-MUD是一种半自动化湿地分类工具，可生成1984年至2024年期间像元被分类为湿地、高地或水体的年度地图。WHAMS是一个Google Earth Engine工具和框架，用于在41年记录期内按月评估湿地植物健康状况的变化（以归一化差异植被指数（NDVI）作为代理指标）。在40年研究期内，德克萨斯州沿海湾岸地区的总分类精度平均为93%，湿地像元分类精度平均为82%。年度湿地范围地图显示，自1984年以来，该地区经历了约193 km²的净湿地损失，占初始湿地范围的17%，其中大部分损失（95%）发生在2008年至2016年间。对湿地区域NDVI的分析表明，43 km²的沿海沼泽湿地（低洼淡水湿地）对反复干旱事件（2008-2010年、2011-2015年）尤为敏感，占沿海湿地总损失的22%。本研究开发的公开可用工具通过快速测量区域多年代际尺度上的湿地范围和健康状况，为理解湿地恢复力提供了见解，其方法可在德克萨斯州沿海湾岸以外的地区重现。","Frontiers in Marine Science","2026-09-15T00:00:00Z",80,{"impact":399,"substance":400,"depth":399,"authority":401,"freshness":228,"relevant":42,"comment":402},18,22,13,"基于Landsat与Sentinel长时序影像构建可迁移湿地制图与健康评估框架，方法新颖、数据规模大，对沿海湿地监测与农业生态信息化有参考价值。",[404],{"name":395,"url":392},[406,407,21,408,23],"农业遥感","气候变化","湿地保护",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":411},{"candidates":412,"agricultural_evidence":14,"reason":46},[413,418,424],{"code":290,"score":345,"supported":34,"title":414,"abstract":415,"tags":417},[],[294,416],"drought",[407],{"code":161,"score":419,"supported":34,"title":420,"abstract":421,"tags":423},4,[],[422],"生态系统服务",[408],{"code":41,"score":156,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[406,21],[429,430],"农业遥感 气候变化 湿地保护 遥感监测","农业遥感 气候变化","农业遥感气候变化湿地保护遥感监测-2665","10.3389\u002Ffmars.2026.1911273",{"doi":432,"openalex_id":434,"authors":435,"venue":395,"cited_by_count":15,"oa_url":442,"card":443,"direction":77,"ingested_from":80},"W7213245622",[436,438,440],{"name":437,"orcid":9},"John Malito",{"name":439,"orcid":9},"Vanessa Valenti",{"name":441,"orcid":9},"Katie Swanson","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1911273\u002Fpdf",{"tldr":444,"method":445,"finding":446,"direction":77,"opportunity":447},"开发可迁移框架，用卫星影像评估历史气候对湿地变化的影响。","Google Earth Engine中构建WET-MUD和WHAMS工具，融合","1984-2024年德州海岸湿地净损失193 km²，95%发生在2008-2016年，淡水湿地对干","可将该可迁移框架应用于其他沿海湿地，结合气候数据量化不同压力源的贡献。","2026-09-16T23:30:29.083410Z"]