[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1773":3,"related-1773":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":58,"slug":61,"view_count":21,"doi":62,"paper":63,"created_at":77},1773,"The Cotton Line: Testing the Institutional Persistence of an Erased Colonial Land-Tenure Boundary Across Vidarbha's Agrarian and Vegetation Geography","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx51b86","Are there 150-year-old institutional legacies measurable in the 21st century as a spatial signature, in patterns of vegetation phenology (cotton signatures), agrarian labor structure, land use and land surface temperature? That is the core question this paper addresses and the design is not a 'before'-'after', it's a 'spatial discontinuity' test to see if there is still a measurable gap across an administrative line following a historical change. It was in 1853 when the British stormed the Berar region, consisting of the districts Amravati, Akola, Buldhana, Washim, and Yavatmal, and implemented a new land-tenure system in the region known as the \"Ryotwari\" system. The American Civil War brought an end to the export of cotton from America to Lancashire, and Lancashire pressured Berar to fill that gap, which led to Berar becoming a producer of cotton on a monoculture basis. The six districts in the East of Berar (Nagpur, Wardha, Chandrapur, Bhandara, Gondia and Gadchiroli), the heart of the Central Provinces, were administered on a somewhat different system of settlement which was known as \"Malguzari.\" The administrative boundary was officially renamed and discontinued in 1956, but it still corresponds, as would be the case today, to the current administrative division between Amravati Division and Nagpur Division. Two annual seasons of kharif-rabi NDVI reducing indicate a similar signature. The negative difference for Berar was 0.325 in its performance as compared with 0.255 of Central Provinces in year one, showing difference of 0.070 points across 68 talukas in Berar. Back in Year two, which was a new set of scenes, the picture was virtually the same: Berar scored 0.316 and Central Provinces 0.241, a difference of 0.075. I wasn't convinced by that repetition over a single unusual year, but by the repetition over two independent cycles. Before constructing the boundary was possible, it had to be built – a 384.7 km new line formed by joining 120 talukas in their two historical segregations and separating them by extracting the common frontier between the two – but only after double-checking that it did not assume that the frontier aligned with the old boundary because they shared geographical characteristics, but made sure also that it matched the documented old boundary from 1903–56. The headline null result was the failure to detect any significant level slope, collapse or shift along the historical trend line at any scale (immediate-boundary corridor of 32 talukas, or the extended scene footprint of 66-68 talukas) in the spatial-discontinuity regression (outcome ~ side + signed_dist_km + side×signed_dist_km + rainfall + elevation) of each of the outcomes. At the full footprint level, what the regression did reveal was a significant side×distance interaction for both EO outcomes (NDVI's decline: p = 0.0348; Land-Surface Temperature: p = 0.0105) — meaning the two sides follow different trends moving away from the old boundary, even without a sharp drop at it. The agricultural-laborer-to-cultivator ratio exhibited a similar slope difference at the corridor scale (p = 0.0361), with a similar direction for all 32 leave-one-out jackknife runs, but despite all this the slope difference at the full-footprint scale is offset by a level jump (p = 0.014) — not a contradiction to the underlying story but simply because the territory on the two sides of the corridor is lopsided within the satellite scene. Both these findings together imply no abrupt change of institution at the old Berar\u002FCentral Provinces border, but a rather subtle phenomenon: a strengthening gradient of the institution with increasing distance from the border. To reinforce the overall NDVI signal, a field-ground-truth effort was carried out against separate field-monitoring for the same field boundaries at Karanja Ghadge and Sawardoh villages of Wardha district, covering 7 boundary tests.","150年历史的制度遗产，在21世纪是否还能以空间印记的形式，在植被物候模式（棉花特征）、农业劳动力结构、土地利用和地表温度中得以衡量？这是本文探讨的核心问题。研究设计并非“前后”对比，而是“空间不连续性”检验，旨在考察在历史变革之后，行政界线两侧是否仍存在可测度的差距。1853年，英国人攻占贝拉尔地区（含阿姆拉瓦蒂、阿科拉、布尔达纳、瓦希姆和亚瓦特马尔等县），并在该地区推行名为“莱特瓦尔制”（Ryotwari）的新土地保有制度。美国内战导致美国棉花对兰开夏郡的出口中断，兰开夏郡施压贝拉尔填补这一缺口，促使贝拉尔成为单一作物制的棉花产区。贝拉尔以东的六个县（那格浦尔、瓦尔达、钱德拉普尔、班达拉、贡迪亚和加德奇罗利）——中央省的核心地带——则实行一种略有不同的定居制度，即“马尔古扎里制”（Malguzari）。该行政边界于1956年正式更名并废止，但其走向与当今阿姆拉瓦蒂专区与那格浦尔专区之间的行政分界仍然对应。两个年度的“卡里夫—拉比”（kharif-rabi）归一化植被指数（NDVI）下降均显示出相似的特征。第一年，贝拉尔的表现负差为0.325，而中央省为0.255，在贝拉尔68个塔卢卡（taluka）之间显示出0.070个百分点的差异。到了第二年——一组全新的影像场景——情况几乎相同：贝拉尔为0.316，中央省为0.241，差异为0.075。令我信服的并非单一异常年份中的这种重复，而是两个独立周期中的重复。在构建边界之前，必须先将其建立起来——一条384.7公里的新界线，由120个塔卢卡按其历史上的两种归属划分连接而成，并通过提取两者之间的共同边界将其分隔——但前提是经过双重核查，确保并未因两者共享地理特征而假定该界线与旧边界重合，同时确认其与1903年至1956年间有文献记载的旧边界相符。最核心的零结果在于：在任何尺度上（紧邻边界的走廊地带），均未能检测到沿历史趋势线存在显著的坡度变化、断裂或位移。",null,"OpenAlex","2026-09-04T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,22,18,3,1,"论文利用空间断点回归检验150年前土地制度对当代农业与植被的持续影响，方法新颖，数据详实，但时效性较低。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","NDVI","历史制度","棉花种植","土地制度",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":51},15,true,[48],"cotton",[50,48],"棉花",[29],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I3",2,[],[],[27,26],[59,60],"农业遥感 历史制度 土地制度 棉花种植","农业遥感 历史制度","农业遥感历史制度土地制度棉花种植-1773","10.31223\u002Fx51b86",{"doi":62,"openalex_id":64,"authors":65,"venue":9,"cited_by_count":68,"oa_url":69,"card":70,"direction":74,"ingested_from":76},"W7208761436",[66],{"name":67,"orcid":9},"Sakshi Maske",0,"https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F14802\u002Fdownload\u002F25742\u002F",{"tldr":71,"method":72,"finding":73,"direction":74,"opportunity":75},"检验1853年英国殖民土地制度边界是否在21世纪仍留下空间印记。","空间断点回归，比较NDVI、土地利用、地表温度等指标。","历史边界两侧存在显著差异，且两年重复验证一致。","农业遥感与作物表型","可探索其他历史制度边界对农业生态的长期影响，结合遥感大数据。","openalex","2026-09-06T23:30:28.937643Z",{"total":79,"page":21,"page_size":79,"items":80},6,[81,124,184,226,315,452],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":9,"content":86,"source_name":87,"source_url":9,"published_at":88,"category":89,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":96,"tags":98,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":119,"slug":122,"view_count":68,"doi":9,"paper":9,"created_at":123},4468,"河北农科院棉花研究所冯国艺团队扎根新疆盐碱棉田 改良技术使耕层盐分降低11.5%","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F08\u002Fcontent_592804.html","国家棉麻产业技术体系土壤障碍消减与地力提升岗位团队（依托单位河北省农林科学院棉花研究所）在新疆库尔勒市举办南疆盐碱棉田改良及微生物菌剂应用技术观摩培训会；专家检测结果表明技术示范田耕层盐分降低11.5%、水稳性团聚体比例由16.2%增至16.9%、有机质含量由1.79g\u002Fkg增至1.86g\u002Fkg、土壤质量显著提升，收获株数提升14.2%、亩增产籽棉33.8公斤、增幅为8.2%。","![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   [深瞳](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fstbd\u002Fnode_434.html)\n*   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7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F08\u002F592804_9a72c3eb-7be7-4438-ae22-e891aacc073acopy.jpg)\n\n来自中国农业大学、华南农业大学、河北农业大学、巴音郭楞蒙古自治州农业科学研究院（以下简称巴州农科院）、库尔勒市农业技术推广中心、河北省农林科学院植物保护研究所等单位以及新疆国欣种业有限公司等巴州相关涉棉企业、种植大户50多人参加。\n\n观摩期间，专家现场检测技术示范效果。专家检测结果表明，技术示范田耕层盐分降低11.5%，水稳性团聚体比例由16.2%增至16.9%，有机质含量由1.79g\u002Fkg增至1.86g\u002Fkg，土壤质量显著提升；收获株数提升14.2%，亩增产籽棉33.8公斤，增幅为8.2%。\n\n国家棉麻产业技术体系土壤障碍消减与地力提升岗位科学家、河北省农林科学院棉花研究所冯国艺研究员介绍，团队自2021年开始扎根新疆棉区，聚焦内陆盐碱程度高、盐分表聚剧烈等问题，将滨海盐碱区成熟的盐碱地改良技术引进新疆盐碱棉区。结合当地灌溉制度和棉花生育期需水规律，团队仿真模拟优化灌排方案，重点开展水盐双控分级改良。同时，引入微生物菌剂等生物改良措施，促进土壤团聚体形成和有机质提升，逐步形成适配新疆环湖和沿河棉区的综合技术模式。在推广过程中，团队与巴州农科院、库尔勒市农业技术推广中心等单位协作，依托合作社、种植大户和涉棉企业开展示范展示，把技术落到具体田块，以看得见的效果带动周边棉农应用。\n\n（国家棉麻产业技术体系岗位团队供图）\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 8](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 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2026-10-08","2026-10-08T00:00:00Z","报道",79,{"impact":19,"substance":18,"depth":92,"authority":93,"freshness":94,"relevant":21,"comment":95},16,14,9,"国家棉麻体系岗位团队在新疆盐碱棉田的改良技术有具体检测数据支撑，信息增量足、时效性强，值得进入每日精选。",[97],{"name":87,"url":84},[99,100,29,101,102],"土壤健康","盐碱地改良","微生物菌剂","新疆棉区",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":106},{"candidates":107,"agricultural_evidence":46},[108,112],{"code":32,"score":17,"supported":46,"title":109,"abstract":110,"tags":111},[50],[50],[29],{"code":113,"score":114,"supported":46,"title":115,"abstract":116,"tags":118},"H2",5,[],[117],"土壤",[99,100],[120,121],"河北农科院 棉花所 盐碱棉田","冯国艺 微生物菌剂 新疆","河北农科院棉花所盐碱棉田-4468","2026-10-09T00:04:56.805549Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":137,"tags":139,"primary_topic":143,"secondary_topics":144,"classification":150,"search_phrases":168,"slug":171,"view_count":20,"doi":172,"paper":173,"created_at":183},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":134,"substance":135,"depth":92,"authority":13,"freshness":94,"relevant":21,"comment":136},8,20,"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[138],{"name":130,"url":127},[140,141,50,142,27],"智慧农业","产量预测","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[145],{"code":53,"slug":146,"name":147,"path":148,"parent_code":149},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":151},{"candidates":152,"agricultural_evidence":46},[153,157,163],{"code":32,"score":45,"supported":46,"title":154,"abstract":155,"tags":156},[48],[50,48],[50],{"code":53,"score":158,"supported":46,"title":159,"abstract":161,"tags":162},11,[160],"remote sensing",[],[27,141,142],{"code":164,"score":21,"supported":14,"title":165,"abstract":166,"tags":167},"I1",[],[],[140],[169,170],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":172,"openalex_id":174,"authors":175,"venue":130,"cited_by_count":68,"oa_url":127,"card":178,"direction":74,"ingested_from":76},"W7214748696",[176],{"name":177,"orcid":9},"B.H. Babashli",{"tldr":179,"method":180,"finding":181,"direction":74,"opportunity":182},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":9,"content":9,"source_name":189,"source_url":9,"published_at":190,"category":89,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":196,"tags":198,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":221,"slug":224,"view_count":208,"doi":9,"paper":9,"created_at":225},3695,"昌吉州：一部手机管万亩良田 数智兴农激活产业升级新动能","https:\u002F\u002Fopen.btzx.com.cn\u002Fopen\u002Farticle\u002Farticle.html?id=ARTI1790222529767861","昌吉回族自治州作为自治区智慧农业综合改革试点，依托2+N场景体系推进智慧农业。一部手机管万亩良田模式推广面积超112万亩，亩均节水30立方米、节肥15%以上、节本增收350元。玛纳斯县建成70万亩数字化农田，棉花数字种植水平领跑全疆。","新疆兵团广播电视台","2026-09-25T00:00:00Z",75,{"impact":18,"substance":193,"depth":92,"authority":94,"freshness":194,"relevant":21,"comment":195},21,7,"自治区智慧农业试点进展，含112万亩推广面积与节水节肥增收等硬数据，产业参考价值较高。",[197],{"name":189,"url":187},[140,199,29,200,201],"新疆昌吉","数字农田","节水节肥",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":205},{"candidates":206,"agricultural_evidence":46},[207,212,216],{"code":32,"score":208,"supported":46,"title":209,"abstract":210,"tags":211},4,[],[50],[29],{"code":164,"score":208,"supported":46,"title":213,"abstract":214,"tags":215},[],[140],[140],{"code":217,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},"H1",[],[],[200],[222,223],"昌吉州 智慧农业 万亩良田","玛纳斯县 数字农田 棉花","昌吉州智慧农业万亩良田-3695","2026-09-29T00:08:18.969197Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":247,"search_phrases":277,"slug":280,"view_count":208,"doi":281,"paper":282,"created_at":314},3014,"Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115674","Leaf inclination angle (LIA) dynamics act as a rapid response mechanism to abiotic stress, regulating canopy energy balance and water use efficiency. While the adaptive value of diurnal LIA plasticity (e.g., paraheliotropism) is well-recognized in ecology, most current remote sensing algorithms and ecosystem models still treat canopy architecture as static and neglect stress-induced geometric adjustments. Furthermore, the diurnal dynamics of LIA under combined abiotic stresses, such as concurrent water deficit and salinity, still remain poorly understood. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a promising approach for capturing LIA dynamics at high spatial and temporal resolution. However, accurately resolving fine scale, dynamic leaf movements in real environments using UAVs remains challenging. To address these gaps, we developed the Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), an integrated UAV analytical workflow that combines deep learning with Structure from Motion (SfM). CAPLA employs 2D semantic masks to strictly constrain 3D mesh reconstruction, effectively mitigating motion-induced artifacts. Independent validation against 19 plot level mean leaf angle (MLA) observations collected at 9:30 am and 12:00 pm yielded an R 2 of 0.89 and an RMSE of 0.9°, supporting plot level MLA estimation under the validated acquisition conditions. CAPLA was subsequently applied across five observation times to characterize diurnal canopy structural dynamics. Importantly, repeated measures analysis of the high frequency observations revealed significant effects of irrigation, salinity, and observation time on MLA, together with a significant irrigation × time interaction ( P = 0.0109), indicating that diurnal MLA trajectories differed among irrigation levels. In contrast, neither the irrigation × salinity interaction ( P = 0.8800) nor the irrigation × salinity × time interaction ( P = 0.9086) was significant. Descriptive differences in within-day variability were nevertheless observed among individual treatment combinations, highlighting the value of time-resolved structural monitoring for characterizing canopy responses to combined water and salinity stresses. These findings highlight the complex structural plasticity of canopies under interacting stresses, emphasizing the critical need to transition from static canopy assumptions to dynamic structural monitoring for improving ecosystem models and precision agriculture.","叶片倾角（LIA）动态变化是植物对非生物胁迫的快速响应机制，调控冠层能量平衡与水分利用效率。尽管昼夜LIA可塑性（如避日运动）的适应价值在生态学中已得到广泛认可，但当前大多数遥感算法和生态系统模型仍将冠层结构视为静态，忽略了胁迫诱导的几何调整。此外，在水分亏缺与盐分胁迫等复合非生物胁迫条件下，LIA的昼夜动态变化仍知之甚少。近年来无人机（UAV）摄影测量技术的进展为在高时空分辨率下捕捉LIA动态提供了有前景的方法。然而，利用无人机在真实环境中精确解析精细尺度的动态叶片运动仍具挑战性。为弥补上述不足，我们开发了约束辅助点云融合叶片尺度分析流程（CAPLA），这是一种集成了深度学习与运动恢复结构（SfM）的无人机综合分析工作流。CAPLA利用二维语义掩膜严格约束三维网格重建，有效减轻了运动诱导的伪影。基于上午9：30和中午12：00采集的19个样地水平平均叶倾角（MLA）观测值进行独立验证，结果R²为0.89，RMSE为0.9°，支持在验证采集条件下进行样地水平MLA估算。随后将CAPLA应用于五个观测时段以表征冠层结构的昼夜动态变化。重要的是，对高频观测的重复测量分析揭示了灌溉、盐分和观测时间对MLA的显著影响，以及显著的灌溉×时间交互效应（P = 0.0109），表明不同灌溉水平下MLA的昼夜变化轨迹存在差异。相比之下，灌溉×盐分交互效应（P = 0.8800）和灌溉×盐分×时间交互效应（P = 0.9086）均不显著。尽管如此，在各处理组合之间仍观察到日内变异性的描述性差异，凸显了时间分辨结构监测在表征冠层对水分与盐分复合胁迫响应方面的价值。这些发现揭示了冠层在交互胁迫下的复杂结构可塑性，强调亟需从静态冠层假设转向动态结构监测，以改进生态系统模型和精准农业。","Remote Sensing of Environment","2026-09-19T00:00:00Z",84,{"impact":19,"substance":236,"depth":237,"authority":45,"freshness":94,"relevant":21,"comment":238},23,19,"该研究提出CAPLA无人机点云融合方法，实现水盐胁迫下棉花叶倾角昼夜动态的高精度监测，方法新颖、数据可靠，对作物表型与精准农业有实质参考价值。",[240],{"name":232,"url":229},[140,26,50,242,243],"精准农业","无人机遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[246],{"code":53,"slug":146,"name":147,"path":148,"parent_code":149},{"status":39,"locked":14,"version":40,"method":41,"evidence":248},{"candidates":249,"agricultural_evidence":46},[250,254,259,263,270],{"code":32,"score":94,"supported":46,"title":251,"abstract":252,"tags":253},[48],[],[50],{"code":164,"score":134,"supported":46,"title":255,"abstract":256,"tags":258},[],[242,257],"precision agriculture",[140,242],{"code":53,"score":134,"supported":46,"title":260,"abstract":261,"tags":262},[],[142,160],[26,243],{"code":264,"score":79,"supported":46,"title":265,"abstract":266,"tags":269},"H3",[],[267,268],"灌溉","irrigation",[],{"code":271,"score":79,"supported":46,"title":272,"abstract":273,"tags":276},"I2",[],[274,275],"深度学习","deep learning",[],[278,279],"无人机 棉花 叶倾角 水盐胁迫","CAPLA 冠层结构 动态监测","无人机棉花叶倾角水盐胁迫-3014","10.1016\u002Fj.rse.2026.115674",{"doi":281,"openalex_id":283,"authors":284,"venue":232,"cited_by_count":68,"oa_url":229,"card":309,"direction":74,"ingested_from":76},"W7213661690",[285,288,290,292,294,296,299,302,304,307],{"name":286,"orcid":287},"Qing Li","https:\u002F\u002Forcid.org\u002F0009-0004-4580-7761",{"name":289,"orcid":9},"Dalei Hao",{"name":291,"orcid":9},"Jan Pisek",{"name":293,"orcid":9},"Zicheng Ji",{"name":295,"orcid":9},"Yanan Wei",{"name":297,"orcid":298},"Youngryel Ryu","https:\u002F\u002Forcid.org\u002F0000-0001-6238-2479",{"name":300,"orcid":301},"Jiarui Xu","https:\u002F\u002Forcid.org\u002F0000-0003-4925-2770",{"name":303,"orcid":9},"Yangmin Feng",{"name":305,"orcid":306},"Shaozhong Kang","https:\u002F\u002Forcid.org\u002F0000-0002-8019-2537",{"name":308,"orcid":9},"Yelu Zeng",{"tldr":310,"method":311,"finding":312,"direction":74,"opportunity":313},"提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。","结合深度学习与SfM，用2D语义掩膜约束3D网格重建，无人机高频观测。","灌溉、盐分和时间显著影响叶倾角，灌溉×时间交互显著，昼夜轨迹因灌溉而异。","可将动态叶倾角参数化嵌入作物模型，提升水盐胁迫下冠层结构与蒸散模拟精度。","2026-09-20T23:30:21.262698Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":9,"source_name":321,"source_url":318,"published_at":322,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":332,"secondary_topics":337,"classification":344,"search_phrases":404,"slug":407,"view_count":21,"doi":408,"paper":409,"created_at":451},1442,"Morphological, Taxonomic and Soil Suitability Evaluation for Sustainable Crop Production Using GIS and Remote Sensing in Gudvella Block, Balangir District, Odisha State, India","https:\u002F\u002Fdoi.org\u002F10.56557\u002Fjoban\u002F2026\u002Fv18i211054","A comprehensive soil-landscape assessment was undertaken to characterise the morphological, physicochemical and taxonomic properties of soils and to evaluate their suitability for major crops using a GIS-based approach. The study area exhibited considerable physiographic heterogeneity, comprising upper and lower pedi-plains, depressions, hillside slopes, foothill slopes, hillocks and stream-bank positions. Twenty-five representative pedons were examined for morphological and physicochemical characteristics and classified according to Soil Taxonomy. Soil depth varied from 14 to 125 cm, while textures ranged from loamy-skeletal to fine. Soil reaction ranged from strongly acidic to slightly alkaline (pH 4.97-7.85), whereas electrical conductivity (0.01-0.08 dS m-1) and exchangeable sodium percentage (0.56-8.35%) indicated negligible salinity and sodicity constraints. Organic carbon was generally low (0.05-0.50%), while CEC (3.85-37.69 cmol(p⁺) kg-1) and base saturation (21.04-72.90%) varied substantially among pedons. The soils were classified mainly as Entisols, Inceptisols and Alfisols, with Aquic, Lithic, Vertic and related intergrades reflecting contrasting soil-forming environments. Crop suitability assessment revealed marked differences among land-use types. Paddy had 68.3% of the area within the S1-S3 classes, whereas maize, potato and sugarcane each occupied approximately 86% within S2-S3. Cotton, mustard and sunflower were predominantly marginally suitable, with substantial areas classified as N1. Approximately 4% of the area was permanently unsuitable (N2). The results demonstrate that soil suitability is controlled by the interaction of soil properties and landscape processes. The integration of soil survey, taxonomy and GIS-based suitability evaluation provides a robust basis for crop-specific land-use planning, soil and water conservation and sustainable agricultural intensification.","为表征土壤的形态学、物理化学及分类学特性，并采用基于GIS的方法评估其对主要作物的适宜性，开展了一项综合的土壤-景观评估。研究区域表现出显著的地貌异质性，包括上、下麓原、洼地、山坡、山麓坡、丘陵及河岸带。对25个代表性土壤剖面进行了形态学和物理化学特性分析，并根据土壤系统分类法进行分类。土壤深度变化范围为14至125厘米，质地从壤质砾石到细质不等。土壤反应范围从强酸性到微碱性（pH 4.97-7.85），而电导率（0.01-0.08 dS m⁻¹）和交换性钠百分比（0.56-8.35%）表明盐分和钠质限制可忽略不计。有机碳普遍较低（0.05-0.50%），而阳离子交换量（3.85-37.69 cmol(p⁺) kg⁻¹）和盐基饱和度（21.04-72.90%）在各剖面间差异显著。土壤主要归类为新成土、始成土和淋溶土，其中包含潮湿润、石质、变性及相关的过渡类型，反映了不同的成土环境。作物适宜性评估揭示了不同土地利用类型间的显著差异。水稻有68.3%的面积属于S1-S3等级，而玉米、马铃薯和甘蔗在S2-S3等级中各占约86%。棉花、芥菜和向日葵主要为边缘适宜，有相当面积的区域被归类为N1。约4%的区域为永久不适宜（N2）。结果表明，土壤适宜性受土壤性质与景观过程相互作用的控制。土壤调查、分类学与基于GIS的适宜性评价的整合，为特定作物的土地利用规划、水土保持及可持续农业集约化提供了坚实基础。","Journal of biology and nature","2026-09-01T00:00:00Z",52,{"impact":134,"substance":19,"depth":92,"authority":134,"freshness":54,"relevant":21,"comment":325},"区域土壤适宜性评价研究，数据详实，但影响范围有限，时效性较低。",[327],{"name":321,"url":318},[26,329,330,331],"土壤调查","作物适宜性","GIS",{"code":113,"slug":333,"name":334,"path":335,"parent_code":336},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[338,343],{"code":339,"slug":340,"name":341,"path":342,"parent_code":149},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":345},{"candidates":346,"agricultural_evidence":46},[347,351,355,361,365,372,379,386,392,398],{"code":113,"score":93,"supported":46,"title":348,"abstract":349,"tags":350},[333],[117,333],[],{"code":339,"score":17,"supported":46,"title":352,"abstract":353,"tags":354},[331],[331],[331],{"code":32,"score":94,"supported":46,"title":356,"abstract":357,"tags":360},[],[50,358,48,359],"甘蔗","sugarcane",[],{"code":53,"score":94,"supported":46,"title":362,"abstract":363,"tags":364},[160],[],[26],{"code":366,"score":79,"supported":46,"title":367,"abstract":368,"tags":371},"A1",[],[369,370],"水稻","paddy",[],{"code":373,"score":79,"supported":46,"title":374,"abstract":375,"tags":378},"A3",[],[376,377],"玉米","maize",[],{"code":380,"score":79,"supported":46,"title":381,"abstract":382,"tags":385},"A5",[],[383,384],"马铃薯","potato",[],{"code":387,"score":20,"supported":46,"title":388,"abstract":389,"tags":391},"A4",[],[390],"向日葵",[],{"code":393,"score":20,"supported":46,"title":394,"abstract":395,"tags":397},"H6",[],[396],"水土保持",[],{"code":399,"score":20,"supported":46,"title":400,"abstract":401,"tags":403},"H8",[],[402],"可持续农业",[],[405,406],"作物适宜性 农业遥感 土壤调查 GIS","作物适宜性 农业遥感","作物适宜性农业遥感土壤调查GIS-1442","10.56557\u002Fjoban\u002F2026\u002Fv18i211054",{"doi":408,"openalex_id":410,"authors":411,"venue":321,"cited_by_count":68,"oa_url":318,"card":446,"direction":74,"ingested_from":76},"W7204891281",[412,414,416,419,421,424,426,428,430,432,434,436,438,440,442,444],{"name":413,"orcid":9},"D. N. Roy",{"name":415,"orcid":9},"Shaikh Mohitpasha",{"name":417,"orcid":418},"Saumen Saha","https:\u002F\u002Forcid.org\u002F0000-0003-2768-883X",{"name":420,"orcid":9},"D. C. Sarkar",{"name":422,"orcid":423},"M. K. Singh","https:\u002F\u002Forcid.org\u002F0000-0003-0118-3614",{"name":425,"orcid":9},"Sumit Raizada",{"name":427,"orcid":9},"Ravi Gautam",{"name":429,"orcid":9},"Monika",{"name":431,"orcid":9},"Indra Bahadur",{"name":433,"orcid":9},"R.T. Bankar",{"name":435,"orcid":9},"R. K. Setia",{"name":437,"orcid":9},"R. L. Meena",{"name":439,"orcid":9},"N. S. Gahlod",{"name":441,"orcid":9},"Y. Suresh Kumar",{"name":443,"orcid":9},"Kumar Rajeev",{"name":445,"orcid":9},"S. K. Tripathi",{"tldr":447,"method":448,"finding":449,"direction":74,"opportunity":450},"利用GIS和遥感评估印度某地块土壤特性及作物适宜性，为可持续生产提供规划依据。","土壤调查、形态理化分析、土壤分类及GIS适宜性评价。","水稻适宜性高，棉花等边际适宜，约4%永久不宜，需针对性规划。","可结合高分辨率遥感与机器学习，动态监测土壤属性并预测作物适宜性，提升区域精准农业决策。","2026-09-02T23:30:53.627384Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":9,"content":9,"source_name":457,"source_url":9,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":463,"tags":465,"primary_topic":469,"secondary_topics":470,"classification":475,"search_phrases":495,"slug":498,"view_count":68,"doi":9,"paper":499,"created_at":507},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",69,{"impact":92,"substance":193,"depth":461,"authority":94,"freshness":79,"relevant":21,"comment":462},17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[464],{"name":457,"url":455},[140,466,467,243,468],"农业人工智能","目标检测","棉花病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[471],{"code":271,"slug":472,"name":473,"path":474,"parent_code":149},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":476},{"candidates":477,"agricultural_evidence":46},[478,483,487,491],{"code":271,"score":19,"supported":46,"title":479,"abstract":481,"tags":482},[274,480],"YOLO",[],[466,467],{"code":32,"score":158,"supported":46,"title":484,"abstract":485,"tags":486},[50],[48],[],{"code":164,"score":21,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[140],{"code":53,"score":21,"supported":14,"title":492,"abstract":493,"tags":494},[],[],[243],[496,497],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":500,"venue":9,"cited_by_count":68,"oa_url":9,"card":501,"direction":74,"ingested_from":506},[],{"tldr":502,"method":503,"finding":504,"direction":74,"opportunity":505},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z"]