[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4100":3,"related-4100":86},{"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":26,"classification":27,"search_phrases":43,"slug":46,"view_count":47,"doi":48,"paper":49,"created_at":85},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。",null,"State of the Planet","2026-09-30T00:00:00Z","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。",[19],{"name":10,"url":6},[21,22,23,24,25],"遥感监测","海岸带","海洋生态","富营养化","河流径流",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":31},"needs_review","2026-10-22.2","rules",{"candidates":32,"agricultural_evidence":14,"reason":42},[33],{"code":34,"score":35,"supported":36,"title":37,"abstract":38,"tags":41},"I3",7,true,[],[39,40],"遥感","remote sensing",[21],"农业相关性需要复核",[44,45],"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",2,"10.5194\u002Fsp-7-osr10-18-2026",{"doi":48,"openalex_id":50,"authors":51,"venue":10,"cited_by_count":15,"oa_url":76,"card":77,"direction":83,"ingested_from":84},"W7214910604",[52,55,58,61,64,67,70,73],{"name":53,"orcid":54},"Valeria Di Biagio","https:\u002F\u002Forcid.org\u002F0000-0002-6160-1825",{"name":56,"orcid":57},"Stefano Querin","https:\u002F\u002Forcid.org\u002F0000-0002-4658-860X",{"name":59,"orcid":60},"Carolina Amadio","https:\u002F\u002Forcid.org\u002F0000-0003-1565-4165",{"name":62,"orcid":63},"Giorgio Bolzon","https:\u002F\u002Forcid.org\u002F0000-0003-3285-5049",{"name":65,"orcid":66},"Laura Feudale","https:\u002F\u002Forcid.org\u002F0000-0002-8631-2499",{"name":68,"orcid":69},"Stefano Piani","https:\u002F\u002Forcid.org\u002F0000-0002-5539-6669",{"name":71,"orcid":72},"Simone Spada","https:\u002F\u002Forcid.org\u002F0000-0002-6909-4290",{"name":74,"orcid":75},"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":78,"method":79,"finding":80,"direction":81,"opportunity":82},"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。","农业遥感与作物表型","openalex","2026-10-03T23:30:37.707929Z",{"total":87,"page":88,"page_size":87,"items":89},6,1,[90,153,218,300,383,441],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":98,"sources":100,"tags":102,"primary_topic":9,"secondary_topics":104,"classification":105,"search_phrases":120,"slug":123,"view_count":15,"doi":124,"paper":125,"created_at":152},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。","Scientific Reports","2026-10-03T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":99},"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。",[101],{"name":96,"url":93},[21,103,22],"城市热岛",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":106},{"candidates":107,"agricultural_evidence":14,"reason":42},[108,116],{"code":109,"score":110,"supported":36,"title":111,"abstract":113,"tags":115},"I2",14,[112],"machine learning",[114,112],"机器学习",[],{"code":34,"score":88,"supported":14,"title":117,"abstract":118,"tags":119},[],[],[21],[121,122],"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":124,"openalex_id":126,"authors":127,"venue":96,"cited_by_count":15,"oa_url":145,"card":146,"direction":151,"ingested_from":84},"W7218522584",[128,130,132,135,138,140,143],{"name":129,"orcid":9},"A. Ernest Amita Roy",{"name":131,"orcid":9},"John Prince Soundaranayagam",{"name":133,"orcid":134},"A. Antony Alosanai Promilton","https:\u002F\u002Forcid.org\u002F0000-0002-6762-0103",{"name":136,"orcid":137},"Neelam Sidhu","https:\u002F\u002Forcid.org\u002F0000-0002-5612-1085",{"name":139,"orcid":9},"V. Stephen Pitchaimani",{"name":141,"orcid":142},"Hazratullah Paktin","https:\u002F\u002Forcid.org\u002F0009-0008-0780-7639",{"name":144,"orcid":9},"Abhishek Sharma","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-73768-1_reference.pdf",{"tldr":147,"method":148,"finding":149,"direction":83,"opportunity":150},"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。","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","2026-10-05T23:30:09.368529Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":168,"tags":170,"primary_topic":9,"secondary_topics":174,"classification":175,"search_phrases":196,"slug":199,"view_count":200,"doi":201,"paper":202,"created_at":217},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":163,"substance":164,"depth":163,"authority":165,"freshness":166,"relevant":88,"comment":167},18,22,13,9,"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。",[169],{"name":159,"url":156},[171,172,21,173,22],"农业遥感","气候变化","湿地保护",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":176},{"candidates":177,"agricultural_evidence":14,"reason":42},[178,185,192],{"code":179,"score":35,"supported":36,"title":180,"abstract":181,"tags":184},"H7",[],[182,183],"干旱","drought",[172],{"code":186,"score":187,"supported":36,"title":188,"abstract":189,"tags":191},"H6",4,[],[190],"生态系统服务",[173],{"code":34,"score":47,"supported":14,"title":193,"abstract":194,"tags":195},[],[],[171,21],[197,198],"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",3,"10.3389\u002Ffmars.2026.1911273",{"doi":201,"openalex_id":203,"authors":204,"venue":159,"cited_by_count":15,"oa_url":211,"card":212,"direction":83,"ingested_from":84},"W7213245622",[205,207,209],{"name":206,"orcid":9},"John Malito",{"name":208,"orcid":9},"Vanessa Valenti",{"name":210,"orcid":9},"Katie Swanson","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1911273\u002Fpdf",{"tldr":213,"method":214,"finding":215,"direction":83,"opportunity":216},"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",{"id":219,"title":220,"url":221,"summary":222,"summary_zh":223,"content":9,"source_name":224,"source_url":221,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":226,"score_detail":227,"sources":231,"tags":233,"primary_topic":9,"secondary_topics":238,"classification":239,"search_phrases":270,"slug":273,"view_count":15,"doi":274,"paper":275,"created_at":299},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":163,"substance":164,"depth":228,"authority":229,"freshness":166,"relevant":88,"comment":230},17,12,"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。",[232],{"name":224,"url":221},[21,234,235,236,237],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":240},{"candidates":241,"agricultural_evidence":36,"reason":269},[242,248,252,257,263],{"code":186,"score":35,"supported":36,"title":243,"abstract":244,"tags":247},[],[245,246],"生物多样性","biodiversity",[234],{"code":34,"score":35,"supported":36,"title":249,"abstract":250,"tags":251},[],[39,40],[21],{"code":179,"score":87,"supported":36,"title":253,"abstract":254,"tags":256},[],[172,255],"climate change",[],{"code":258,"score":200,"supported":36,"title":259,"abstract":260,"tags":262},"H2",[],[261],"soil",[],{"code":264,"score":200,"supported":36,"title":265,"abstract":266,"tags":268},"I7",[],[267],"研究方法",[],"多个产业证据相当，需要确认主归属",[271,272],"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":274,"openalex_id":276,"authors":277,"venue":224,"cited_by_count":15,"oa_url":221,"card":294,"direction":83,"ingested_from":84},"W7223141286",[278,281,283,286,289,292],{"name":279,"orcid":280},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":282,"orcid":9},"Gabriel Henrique Santos Silva",{"name":284,"orcid":285},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":287,"orcid":288},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":290,"orcid":291},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":293,"orcid":9},"Sylvana Melo dos Santos",{"tldr":295,"method":296,"finding":297,"direction":83,"opportunity":298},"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":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":225,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":307,"score_detail":308,"sources":311,"tags":313,"primary_topic":318,"secondary_topics":322,"classification":323,"search_phrases":345,"slug":348,"view_count":15,"doi":349,"paper":350,"created_at":382},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":309,"substance":164,"depth":163,"authority":110,"freshness":166,"relevant":88,"comment":310},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。",[312],{"name":306,"url":303},[21,314,315,316,317],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":258,"slug":261,"name":319,"path":320,"parent_code":321},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":324,"locked":14,"version":29,"method":30,"evidence":325},"classified",{"candidates":326,"agricultural_evidence":36},[327,332,336,341],{"code":258,"score":110,"supported":36,"title":328,"abstract":329,"tags":331},[261],[330,261],"土壤",[],{"code":34,"score":35,"supported":36,"title":333,"abstract":334,"tags":335},[],[39,40],[21],{"code":179,"score":87,"supported":36,"title":337,"abstract":338,"tags":340},[],[339,182],"气候适应",[],{"code":186,"score":88,"supported":14,"title":342,"abstract":343,"tags":344},[],[],[316],[346,347],"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":349,"openalex_id":351,"authors":352,"venue":306,"cited_by_count":15,"oa_url":303,"card":377,"direction":83,"ingested_from":84},"W7223069171",[353,355,357,359,362,364,367,370,372,374],{"name":354,"orcid":9},"Yue Li",{"name":356,"orcid":9},"Mengfei Shi",{"name":358,"orcid":9},"Fei Zhang",{"name":360,"orcid":361},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":363,"orcid":9},"Gowhar Meraj",{"name":365,"orcid":366},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":368,"orcid":369},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":371,"orcid":9},"Yuanfang Chai",{"name":373,"orcid":9},"Hongtao Xu",{"name":375,"orcid":376},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":378,"method":379,"finding":380,"direction":83,"opportunity":381},"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":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":307,"score_detail":391,"sources":393,"tags":395,"primary_topic":400,"secondary_topics":406,"classification":407,"search_phrases":424,"slug":427,"view_count":15,"doi":428,"paper":429,"created_at":440},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":163,"substance":164,"depth":163,"authority":229,"freshness":166,"relevant":88,"comment":392},"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。",[394],{"name":389,"url":386},[396,21,397,398,399],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":324,"locked":14,"version":29,"method":30,"evidence":408},{"candidates":409,"agricultural_evidence":36},[410,415,420],{"code":401,"score":200,"supported":36,"title":411,"abstract":412,"tags":414},[],[413],"萝卜",[],{"code":416,"score":88,"supported":14,"title":417,"abstract":418,"tags":419},"I1",[],[],[396],{"code":34,"score":88,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[21],[425,426],"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":428,"openalex_id":430,"authors":431,"venue":389,"cited_by_count":15,"oa_url":386,"card":435,"direction":83,"ingested_from":84},"W7223098474",[432],{"name":433,"orcid":434},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":436,"method":437,"finding":438,"direction":83,"opportunity":439},"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",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":389,"source_url":444,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":447,"score_detail":448,"sources":450,"tags":452,"primary_topic":456,"secondary_topics":461,"classification":462,"search_phrases":497,"slug":500,"view_count":15,"doi":501,"paper":502,"created_at":513},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma",56,{"impact":229,"substance":309,"depth":165,"authority":87,"freshness":166,"relevant":88,"comment":449},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[451],{"name":389,"url":444},[396,453,21,454,455],"农业人工智能","风险管理","病虫害监测",{"code":457,"slug":458,"name":459,"path":460,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":463,"locked":14,"version":29,"method":30,"evidence":464},"partial",{"candidates":465,"agricultural_evidence":36,"reason":496},[466,472,478,486,492],{"code":109,"score":13,"supported":36,"title":467,"abstract":468,"tags":471},[],[114,112,469,470],"artificial intelligence","decision support",[453],{"code":34,"score":13,"supported":36,"title":473,"abstract":474,"tags":477},[],[39,40,475,476],"multispectral","hyperspectral",[21],{"code":479,"score":166,"supported":36,"title":480,"abstract":481,"tags":485},"I5",[],[482,483,484],"物联网","internet of things","IoT",[],{"code":416,"score":35,"supported":36,"title":487,"abstract":488,"tags":491},[],[489,490],"精准农业","precision agriculture",[396],{"code":179,"score":87,"supported":36,"title":493,"abstract":494,"tags":495},[],[172,255],[],"一级领域明确，二级方向需要细分",[498,499],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":501,"openalex_id":503,"authors":504,"venue":389,"cited_by_count":15,"oa_url":444,"card":507,"direction":151,"ingested_from":84},"W7223085400",[505],{"name":506,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":508,"method":509,"finding":510,"direction":511,"opportunity":512},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","2026-10-22T11:20:24.428080Z"]