[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-201":3,"related-201":69},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},201,"STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series(基于卫星时序影像的自监督作物胁迫时空网络)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.18791v1","及时准确地检测作物胁迫对提升农业生产力和保障全球粮食安全至关重要,但标注数据成本高。文章提出新型时空应力网络STS-NET,基于自监督3D卷积自编码器构建,利用PlanetScope高分辨率影像时序数据,提取NDVI、GNDVI、RECI、NDRE四种植被指数捕捉时空胁迫模式。在印度北方邦Lakhimpur-Kheri地区2.5英亩甘蔗试验田上,STS-NET对水分胁迫达到97.98%精度、对氮素胁迫达85.08%精度、对复合胁迫达83.47%精度,证明该自监督方法在标注数据稀缺条件下有效检测甘蔗胁迫的潜力,并可作为简单模型的稳健特征提取器。[预印本]",null,"[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.18791v1)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.18791v1)\n\n> Abstract:Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98\\% for water stress, 85.08\\% for nitrogen stress, and 83.47\\% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.\n\nComments:5 pages\nSubjects:Computer Vision and Pattern Recognition (cs.CV)\nCite as:[arXiv:2607.18791](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.18791) [cs.CV]\n(or [arXiv:2607.18791v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.18791v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.18791](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.18791)\n\narXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\nFrom: Rajiv Ranjan [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Fd2f5f5ac\u002F2607.18791)] \n\n**[v1]** Tue, 21 Jul 2026 07:11:03 UTC (2,480 KB)","arXiv:2607.18791v1 [cs.CV] 2026-07-21","2026-07-21T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,2,1,"基于卫星时序影像的自监督作物胁迫检测方法，具有创新性和实用价值，但发表于两周前，时效性较低。",[24],{"name":10,"url":6},[26,27,28,29,30],"作物胁迫","卫星时序影像","自监督学习","3D卷积自编码器","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"partial","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46,"reason":62},[43,51,57],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},"A6",3,true,[],[49],"甘蔗",[],{"code":52,"score":45,"supported":46,"title":53,"abstract":54,"tags":56},"A8",[],[55],"粮食安全",[],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[30],"一级领域明确，二级方向需要细分",[64,65],"3D卷积自编码器 卫星时序影像 自监督学习 作物胁迫","3D卷积自编码器 卫星时序影像","3D卷积自编码器卫星时序影像自监督学习作物胁迫-201",6,"2026-08-04T01:15:47.504564Z",{"total":67,"page":21,"page_size":67,"items":70},[71,161,223,277,383,431],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":84,"tags":86,"primary_topic":91,"secondary_topics":92,"classification":93,"search_phrases":133,"slug":136,"view_count":137,"doi":138,"paper":139,"created_at":160},4234,"Opportunity Cost of Precision Conservation","https:\u002F\u002Fdoi.org\u002F10.3368\u002Fle.103.2.110525-0095r1","Abstract Conservation practices provide public environmental benefits, but the associated private costs are chiefly borne by farmers. Precision agriculture technologies can help reduce farmer private costs by identifying low-yielding areas, thereby minimizing foregone revenue. This study estimates the opportunity cost of such precision conservation on 29 commercial fields in Michigan, USA, using fine-scale yield maps collected between 2020 and 2024. We employ spatial regression models and Monte Carlo simulations to account for spatial heterogeneity and price variability. Results show that under a corn-soybean crop rotation, precision conservation improves profitability in 22 of 29 fields, indicating potential for adoption without government subsidies.","摘要 保护性耕作实践能带来公共环境效益，但相关的私人成本主要由农民承担。精准农业技术有助于通过识别低产区域来降低农民的私人成本，从而最大限度地减少放弃的收入。本研究利用2020年至2024年间收集的精细尺度产量图，估算了美国密歇根州29块商业农田上此类精准保护性耕作的机会成本。我们采用空间回归模型和蒙特卡洛模拟来处理空间异质性和价格波动。结果表明，在玉米-大豆轮作下，精准保护性耕作提高了29块农田中22块的盈利能力，表明无需政府补贴也具备采用潜力。","Land Economics","2026-10-02T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":81,"freshness":82,"relevant":21,"comment":83},14,8,"基于2020-2024年29块商业田块精细产量图，量化精准保护性耕作的机会成本，结论显示多数田块无需补贴即可盈利，对智慧农业落地与农业绿色政策有参考价值。",[85],{"name":77,"url":74},[87,88,30,89,90],"智慧农业","农业遥感","保护性耕作","产量地图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":94},{"candidates":95,"agricultural_evidence":46,"reason":62},[96,101,108,115,122,128],{"code":58,"score":82,"supported":46,"title":97,"abstract":98,"tags":100},[],[30,99],"precision agriculture",[87,30],{"code":102,"score":67,"supported":46,"title":103,"abstract":104,"tags":107},"A3",[],[105,106],"玉米","corn",[],{"code":109,"score":67,"supported":46,"title":110,"abstract":111,"tags":114},"A4",[],[112,113],"大豆","soybean",[],{"code":116,"score":67,"supported":46,"title":117,"abstract":118,"tags":121},"A7",[],[119,120],"轮作","crop rotation",[],{"code":123,"score":124,"supported":46,"title":125,"abstract":126,"tags":127},"H8",4,[],[89],[89],{"code":129,"score":21,"supported":14,"title":130,"abstract":131,"tags":132},"I3",[],[],[88],[134,135],"密歇根 精准保护性耕作 机会成本","精准农业 产量地图 玉米大豆轮作","密歇根精准保护性耕作机会成本-4234",0,"10.3368\u002Fle.103.2.110525-0095r1",{"doi":138,"openalex_id":140,"authors":141,"venue":77,"cited_by_count":137,"oa_url":151,"card":152,"direction":158,"ingested_from":159},"W7215722779",[142,145,148],{"name":143,"orcid":144},"Seo Woo Lee","https:\u002F\u002Forcid.org\u002F0009-0000-4165-9520",{"name":146,"orcid":147},"Scott M. Swinton","https:\u002F\u002Forcid.org\u002F0000-0002-3751-1811",{"name":149,"orcid":150},"Bruno Basso","https:\u002F\u002Forcid.org\u002F0000-0003-2090-4616","https:\u002F\u002Fle.uwpress.org\u002Fcontent\u002Fwple\u002Fearly\u002F2026\u002F09\u002F24\u002Fle.103.2.110525-0095R1.full.pdf",{"tldr":153,"method":154,"finding":155,"direction":156,"opportunity":157},"基于密歇根29块田的精细产量图，估算精准保护的机会成本并评估其经济可行性。","2020-2024年精细产量图，空间回归与蒙特卡洛模拟。","玉米-大豆轮作下，29块田中22块精准保护可提高利润，无需政府补贴。","农业绿色发展与碳","可拓展至其他作物与区域，探究精准保护对农户收益与环境效益的协同机制。","农业遥感与作物表型","openalex","2026-10-05T23:30:26.394346Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":8,"content":166,"source_name":167,"source_url":8,"published_at":168,"category":169,"cover_url":8,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":217,"slug":220,"view_count":221,"doi":8,"paper":8,"created_at":222},1916,"辽宁省答复省政协\"北斗+智慧农业\"提案 沈阳盘锦入选首批北斗规模应用试点城市","https:\u002F\u002Fnync.ln.gov.cn\u002Fnync\u002Fzfxxgk\u002Ffdzdgknr\u002Fjyta\u002Fszxta\u002Fszxssjschy2026n\u002F2026090310284688624\u002Findex.shtml","辽宁省农业农村厅9月3日答复省政协王春雨委员关于加快推广应用\"北斗+智慧农业\"的提案（辽农提〔2026〕110号）。省厅将联合工信厅持续强化政策引导，依托《辽宁省\"人工智能+农业\"三年行动方案（2025-2027年）》《辽宁省\"数据要素×现代农业\"实施方案（2024—2026年）》，沈阳、盘锦两市已入选工业和信息化领域首批北斗规模应用试点城市，两市共完成1万余台套北斗独立定位应用。2024年率先在铁岭昌图县启动玉米试点4.3万亩，2025年拓展至盘锦大洼区水稻示范，形成玉米、水稻双作物技术储备。","辽 农提〔202 6〕110 号\n\n王春雨 委员：\n\n您提出的关于 加快推广应用“北斗+智慧农业”的建议 的 提案 收悉，现答复如下：\n\n一、关于 立足政策赋能牵引，强化规范标准设计\n\n**一是强化政策引领。**省农业农村厅先后印发了《辽宁省“人工智能+农业”三年行动方案（2025-2027 年）》和《辽宁省“数据要素×现代农业”实施方案（2024—2026 年）》提出强化农业智能装备创新应用，鼓励生产主体采用北斗辅助驾驶系统、田间作业监测终端、农业设施物联网监测与控制系统等数字化工具，提升精准作业水平。同时推进大田种植精准化，推广“北斗+智慧农业”模式，开展无人农场试点。\n\n**二是完善补贴支持。**2024 年，省农业农村厅印发《关于印发辽宁省加力支持农业机械报废更新补贴工作实施细则的通知》（辽农机〔2024〕203 号），将农用北斗辅助驾驶系统纳入农机报废更新补贴范围。为进一步加大支持力度，又印发了《关于实施好 2025 年农业机械报废更新补贴政策的通知》（辽农机〔2025〕80 号），明确报废并新购置农用北斗辅助驾驶系统，在《实施细则》确定的补贴额基础上，按 50%提高报废补贴标准。有效降低了经营主体应用北斗智能装备的成本。\n\n下一步，省农业农村厅将联合有关部门持续强化政策引导，推广北斗终端农机广泛应用，鼓励服务主体采用北斗导航等数字化工具。并持续优化补贴政策，积极研究北斗智能装备和应用补贴，推动政策红利精准直达生产一线。\n\n二、关于锚定核心技术创新，加强示范案例推广\n\n**一是强化北斗产业培育。**省工信厅组织沈阳、盘锦两市成功入选工业和信息化领域首批北斗规模应用试点城市名单，指导盘锦市召开北斗规模应用试点工作部署会，推动北斗创新应用，拓展北斗应用广度和深度。截至目前，沈阳、盘锦两市在各领域共完成 1 万余台\u002F套北斗独立定位应用。\n\n**二是坚持试点先行，梯次推进示范推广。**2024 年，省农业农村厅在农业农村部指导下，率先在铁岭市昌图县启动“北斗+智慧农业”玉米试点，依托两家规模化农机合作社完成 4.3 万亩示范任务，验证了北斗技术在东北黑土区玉米种植中的增产可行性，形成了核心技术框架。2025 年，在昌图玉米试点基础上，拓展至盘锦市大洼区布局水稻试验示范，探索“北斗+智慧农业”在我省水稻主产区的技术适配模式，积累稻区精准作业经验，形成了玉米、水稻双作物技术储备。\n\n下一步，省农业农村厅将联合有关部门推动“北斗+智慧农业”玉米水稻产量品质协同提升技术集成示范，以昌图、大洼为核心引领，有力带动全省粮食产能稳中有升。\n\n三、关于构建立体发展模式，丰富应用场景设计\n\n**一是**我省高度重视智慧农业立体发展。“十四五”期间，全省建设 1 个国家数字农业区域创新分中心（东北）和 5 个国家数字农业创新应用基地，累计培育省级智慧农业应用基地 105 个，改造提升 500 家智能化标准化畜牧养殖场，创建 19 个国家级海洋牧场，助推智慧农业场景落地。\n\n**二是**省科技厅会同省农业农村厅等单位加快农业领域智能装备应用，推动农用无人机技术在大田作物生产高质量发展中发挥重要作用。围绕大田作物农用无人机精准作业，开展“天-空-地”一体化作物养分遥感诊断、植保无人机作业精准调控、植保无人机作业方法与标准等关键技术攻关，探索可复制易推广的农用无人机精准作业新模式。\n\n**三是**省数据局会同省农业农村厅，在全省范围内广泛征集现代农业等领域“数据要素×”典型应用场景。连续两年举办“数据要素×现代农业”赛事，赛题由 3 个扩大到 8 个，参赛团队由 66 支扩大到 84 支，推动以赛促学、以赛促用、以赛促建，积极营造数字化转型的良好氛围。\n\n下一步，省农业农村厅将联合有关部门，聚焦智慧农业领域，加强北斗导航技术创新和产业链协同，推进智能农机、农业机器人、无人机等高端装备研发应用，为农业产业发展提供支撑。\n\n感谢您对全省农业农村发展的关心，希望继续对“三农”工作给予支持。\n\n联 系 人：马旭\n\n联系电话：23448801\n\n辽宁省农业农村厅\n\n202 6 年 5 月 2 8 日","辽宁省农业农村厅 辽农提〔2026〕110号 2026-09-03","2026-09-03T00:00:00Z","政策",73,{"impact":172,"substance":18,"depth":173,"authority":19,"freshness":20,"relevant":21,"comment":174},20,17,"辽宁省对北斗+智慧农业提案的详细答复，涵盖政策、技术试点与推广，具有省级指导意义。",[176],{"name":167,"url":164},[87,30,178,179,180],"辽宁","政策支持","北斗应用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":184},{"candidates":185,"agricultural_evidence":46,"reason":62},[186,191,197,201,207,212],{"code":58,"score":187,"supported":46,"title":188,"abstract":189,"tags":190},13,[87],[87],[87,30],{"code":192,"score":45,"supported":46,"title":193,"abstract":194,"tags":196},"A1",[],[195],"水稻",[],{"code":102,"score":45,"supported":46,"title":198,"abstract":199,"tags":200},[],[105],[],{"code":202,"score":45,"supported":46,"title":203,"abstract":204,"tags":206},"I6",[],[205],"数据要素",[],{"code":208,"score":21,"supported":14,"title":209,"abstract":210,"tags":211},"F5",[],[],[180],{"code":213,"score":21,"supported":14,"title":214,"abstract":215,"tags":216},"J1",[],[],[179],[218,219],"北斗应用 政策支持 智慧农业 精准农业","北斗应用 政策支持","北斗应用政策支持智慧农业精准农业-1916",5,"2026-09-09T00:03:51.996622Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":8,"content":228,"source_name":229,"source_url":8,"published_at":230,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":8,"score_detail":8,"sources":231,"tags":233,"primary_topic":238,"secondary_topics":239,"classification":240,"search_phrases":272,"slug":275,"view_count":124,"doi":8,"paper":8,"created_at":276},711,"基于无人机多光谱的玉米与大豆盐胁迫响应指数及盐分管理干预比较评估","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769","北京市农林科学院联合中国农业科学院烟草研究所在山东滨海盐渍区开展 14 块玉米与 13 块大豆试验,基于 UAV 多光谱与偏最小二乘路径建模(PLS-PM)融合基线土壤盐分与叶面积指数、株高、SPAD、覆盖度,构建盐胁迫响应指数 SSRI;SSRI 与产量呈显著负相关(r=−0.670 至 −0.764),EN 模型在玉米拔节期 R²=0.774、PLSR 在开花期 R²=0.819,生成的空间 SSRI 地图可捕捉田间异质性,为盐渍农业 UAV 作物胁迫监测与精准管理干预评估提供概念验证框架。","## 1. Introduction\n\nSoil salinization is one of the major abiotic stressors constraining sustainable agricultural development worldwide [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B1-agriculture-16-01769)]. Excessive soil salinity not only impairs water and mineral nutrient uptake and transport but also directly suppresses photosynthetic activity, thereby inducing coordinated changes in a range of morphological and physiological traits, including plant height, leaf area, chlorophyll content, and dry matter accumulation. These changes ultimately manifest as distinct phenotypic characteristics, such as inhibited plant growth and altered canopy spectral properties [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B2-agriculture-16-01769),[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B3-agriculture-16-01769)]. Traditionally, salt stress has been diagnosed through manual field observations based on phenotypic traits, whereby crop growth status is visually assessed using geometric characteristics, morphology, and color. However, this approach is labor-intensive, time-consuming, and often yields results with limited spatial representativeness. More importantly, the pronounced spatial heterogeneity of soil salinity leads to highly discontinuous and patchy crop growth patterns that cannot be adequately captured by conventional field observations [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B4-agriculture-16-01769)]. In China, where saline cropland is widely distributed, considerable efforts have been devoted to developing chemical, biological, and agronomic amelioration strategies. The effectiveness of these approaches has typically been evaluated using field-based observations of crop growth, yield, and soil properties. Nevertheless, their practical performance under conditions of pronounced spatial variability remains insufficiently understood [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B5-agriculture-16-01769)]. Therefore, accurate and efficient monitoring of salt stress through the response of crops is essential for providing a scientific basis for optimizing soil amelioration and crop management strategies, improving the utilization efficiency of saline cropland, and identifying effective amelioration practices.\n\nCurrent remote sensing approaches for salinity monitoring have been developed across multiple platforms. Satellite sensors provide large-scale and long-term observations but are often limited by spatial resolution when characterizing field-scale salinity heterogeneity. UAV platforms provide centimeter-level spatial information and flexible data acquisition, making them suitable for crop-scale stress monitoring; however, most UAV-based studies still rely on individual spectral indices or limited indicators. Although hyperspectral and ground-based measurements can provide detailed physiological information, their operational efficiency and scalability remain limited. Therefore, integrating multi-dimensional crop responses with UAV observations remains necessary for more comprehensive salt stress characterization. At the crop scale, UAV multispectral observations can capture stress-related changes in canopy structure and pigment status through variations in spectral reflectance, providing a basis for quantitative monitoring of crop salt stress [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B6-agriculture-16-01769)]. Stress-induced response in crop internal structure and chlorophyll content can lead to regular variations in reflectance at specific spectral bands, which lays the theoretical foundation for using UAV-based multispectral data to monitor salt stress and provides a technical means for objectively evaluating the effectiveness of soil amelioration. To date, various abiotic and biotic stresses, such as drought, nitrogen deficiency, and disease, have been extensively explored in major crops, including maize, soybean, and wheat [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B7-agriculture-16-01769),[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B8-agriculture-16-01769),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B9-agriculture-16-01769),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B10-agriculture-16-01769),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B11-agriculture-16-01769),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B12-agriculture-16-01769),[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B13-agriculture-16-01769)]. For salt stress monitoring, Zhao et al. used biomass as an indicator and constructed an alfalfa salt stress index with UAV multispectral data, realizing quantitative diagnosis of alfalfa salt stress [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B14-agriculture-16-01769)]. Shi et al. established an evaluation system for saline soil improvement effectiveness using UAV-derived leaf area index at the jointing stage of winter wheat [[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B15-agriculture-16-01769)]. Although UAV-based stress monitoring has evolved from single spectral indicators to multi-trait crop responses, existing composite indices often determine trait contributions based on statistical variance, empirical assumptions, or yield-related relationships, rather than the response relationships between crops and environmental stressors. This limitation is particularly relevant because crop growth indicators exhibit distinct sensitivities and response dynamics under salt stress. Previous studies have shown that salt stress initially affects leaf area index, followed by fractional vegetation cover and photosynthetically active radiation, whereas physiological indicators such as leaf water content and chlorophyll-related traits generally respond at later stages [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B16-agriculture-16-01769)]. These asynchronous responses highlight the difficulty of accurately characterizing salt stress based on individual traits, which may fail to capture the integrated effects of salinity on crop morphological structure and physiological function [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B17-agriculture-16-01769)]. Therefore, an index that integrates multiple crop indicators according to their responses to soil salinity is needed to better characterize crop salt stress and facilitate comparative assessment of management practices.\n\nThis study develops a salt stress response index (SSRI) by integrating multiple crop growth indicators according to their responses to soil salinity. We hypothesized that integrating multiple growth indicators could provide a more comprehensive characterization of crop salt stress than individual traits and that UAV-based SSRI retrieval performance would vary across crop growth stages due to differences in canopy development and stress expression. The specific objectives of this study are to (i) quantify the responses of key crop growth traits to soil salinity; (ii) identify salt stress-sensitive spectral features and develop models for SSRI retrieval; and (iii) compare crop salt stress responses under different management practices using the proposed SSRI.\n\n## 2. Materials and Methods\n\n### 2.1. Study Area\n\nThe study area is located at the experimental base of the Shandong Yellow River Delta Agricultural High-tech Industry Demonstration Zone, China, spanning 118°50′–119°03′ E and 37°36′–38°17′ N ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f001)). This region is representative of a coastal saline soil transition zone, where chloride-dominated saline soils prevail and are characterized by high salinity and pronounced spatial heterogeneity. The area experiences a warm-temperate, semi-humid continental monsoon climate, with a mean annual temperature of 13.3 °C, an accumulated temperature above 10 °C of approximately 4300 °C, and an average annual precipitation of 537 mm. The local cropping system follows a two-year, three-crop rotation, primarily consisting of maize, soybean, and winter wheat. Baseline soil physicochemical properties were characterized to describe the initial soil conditions. The 0–20 cm soil layer had a pH of 8.93, electrical conductivity of 1716 μS cm−1, bulk density of 1.55 g cm−3, organic carbon content of 7.81 g kg−1, available phosphorus of 7.3 mg kg−1, and available potassium of 204.4 mg kg−1. The soil texture consisted of 16.97% sand, 61.30% silt, and 21.73% clay. As a key research base for saline land improvement and sustainable agricultural production, this region provides a critical setting for remote sensing-based monitoring of salt stress. As shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#table_body_display_agriculture-16-01769-t001), 14 experimental plots were established for maize and another 14 for soybean, each measuring 15 m × 50 m. Each management practice was assigned to one experimental plot.\n\n**Figure 1.** Geographic location of the study area and distribution of sampling sites. (**a**) Location of the Shandong Yellow River Delta Agricultural High-tech Industry Demonstration Zone. (**b**) Location of the experimental sites. (**c**) Distribution of the experimental treatments and sampling sites.\n\n**Table 1.** Experimental treatments for maize and soybean.\n\nThe experimental management practices consisted of soil amendment practices, cultivar selection, and agronomic management practices. For maize, the evaluated practices included shared practices (Treatments 1–10) and maize-specific practices (Treatments M1–M4). Treatment 1 represented conventional management and served as the reference practice, whereas Treatment 2 received 75% of the conventional fertilizer rate. Treatments 3–10 involved soil amendment practices, including a waste-derived soil conditioner, phosphorus-rich mineral amendment, and other additive measures. The maize-specific practices included cultivar selection (M1 and M2, Zhongdan 1158 and Zhongdan 1191), ridge-furrow planting (M3), and rhizosphere application of a composite microbial agent (M4). Except for M1 and M2, all maize practices used Zhengdan 958 as the cultivar. For soybean, Treatments 1–10 followed the same shared management practices as those applied to maize, whereas Treatments S1–S4 represented cultivar selections involving four soybean varieties. These management practices represented different approaches for regulating crop growth conditions under saline environments. Notably, soybean Treatment 9 was excluded from subsequent analyses because this plot underwent separate replanting during the growing season due to field management issues. The replanting resulted in inconsistent crop establishment and growth timing compared with the other plots, making it unsuitable for direct comparison. The evaluated practices originated from different research teams and were compared exploratorily under the field conditions of this study.\n\nMaize was sown on 22 May 2024 and harvested on 2 October 2024 at a density of 82,500 plants ha−1; soybean was sown on 30 May 2024 and harvested on 15 October 2024 at a density of 210,000 plants ha−1. All plots received the same irrigation management, whereas Treatment 2 differed from the other practices in fertilizer application rate. The conventional fertilizer regime was 140 kg N ha−1, 60 kg P 2 O 5 ha−1, and 60 kg K 2 O ha−1 for maize, and 60 kg N ha−1, 75 kg P 2 O 5 ha−1, and 60 kg K 2 O ha−1 for soybean. All fertilizers were applied as basal fertilizer by broadcasting followed by rotary tillage before sowing, and Treatment 2 received 75% of the conventional fertilizer rate. After sowing, one irrigation event was conducted using drip irrigation belts with an irrigation amount of 60 mm. No further irrigation was applied during the growing season, and the irrigation water salinity was 484 mg L−1.\n\n### 2.2. Data Acquisition and Processing\n\n#### 2.2.1. Soil Sampling Data\n\nThree sampling sites were established within each plot to capture within-plot spatial variability. Soil samples were collected in May 2024, prior to crop sowing and following the seasonal peak of soil salt accumulation, and were used to characterize baseline soil salinity. Three sampling sites were evenly distributed within each plot ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f001)). At each sampling site, three surface soil samples (0–20 cm) were collected and thoroughly mixed to form one composite sample. Therefore, each plot contained three independent soil sampling locations, and the resulting composite samples were used for soil salinity (SC) determination. Accordingly, 42 SC observations for maize and 39 SC observations for soybean were obtained and used in subsequent analyses. The samples were air-dried, passed through a 2 mm sieve, and prepared as soil–water extracts at a soil-to-water ratio of 1:5. After shaking for 5 min and allowing the extracts to stand for 8 h, electrical conductivity (EC) was measured using a FE38 conductivity meter (Mettler Toledo, Zurich, Switzerland). The measured EC values (EC 1:5, mS\u002Fcm) were converted to soil salt content (SC, g\u002Fkg) using the following conversion equation established for the same saline soil region and soil salinity type [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B18-agriculture-16-01769)]: SC = 2.9863·EC 1:5 − 0.1019. The equation was established based on saline soils with SC ranging from 0.78 to 39.54 g\u002Fkg.\n\nThe soil salt content across the experimental plots ranged from 0.79 to 2.72 g\u002Fkg. According to the Chinese soil salinity classification standard [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B19-agriculture-16-01769)], the soils were classified as non-saline (\u003C1 g\u002Fkg), slightly saline (1–2 g\u002Fkg), and moderately saline (2–4 g\u002Fkg). No artificial salt gradient was imposed in this study. Instead, the baseline salinity of the experimental site encompassed the typical range of soil salinity found in regional maize and soybean production areas [[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B20-agriculture-16-01769)]. For each plot, the reported SC value was calculated as the mean of three sampling sites within the plot.\n\n#### 2.2.2. Crop Sampling Data\n\nCrop growth measurements were collected at each sampling site on 18 July and 23 August 2024. These dates corresponded to the maize jointing stage and soybean branching stage and to the flowering stage of both crops, respectively. A total of 42 sets of growth measurements were collected for maize and 39 for soybean at each sampling campaign.\n\nGiven the relatively small size of each experimental plot, all measurements were obtained using non-destructive in situ methods to avoid disturbing crop growth and maintain the integrity of the field experiment. Four key growth indicators were measured directly in the field: leaf area index (LAI), plant height (PH), relative chlorophyll content (SPAD), and fractional vegetation cover (FVC). These indicators were selected because of their sensitivity to salt stress and their suitability for retrieval from UAV-based remote sensing data. LAI characterizes canopy density and is closely associated with photosynthetic productivity [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B21-agriculture-16-01769)]. FVC reflects horizontal canopy coverage and effectively captures reductions in canopy cover caused by salt stress [[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B22-agriculture-16-01769)]. SPAD provides an indirect measure of leaf chlorophyll content and serves as an indicator of salt-induced changes in photosynthetic physiology [[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B23-agriculture-16-01769),[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B24-agriculture-16-01769)]. PH, a fundamental morphological trait, is highly responsive to salt stress and complements the assessment of crop growth status [[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B25-agriculture-16-01769),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B26-agriculture-16-01769)]. Collectively, these four indicators represent complementary aspects of crop structure, function, and physiology, while all can be retrieved from remote sensing data, making them well suited for field-scale monitoring.\n\nSpecifically, LAI was measured using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA). Three measurements were taken at each sampling site and averaged to obtain the site-level LAI value. SPAD was measured using a SPAD-502Plus Chlorophyll Meter (Konica Minolta, Tokyo, Japan). Readings were collected from the midpoint of the fully expanded functional leaf of 10 randomly selected plants at each sampling site. PH was determined by measuring the vertical distance from the soil surface to the apex of 10 randomly selected plants at each site using a ruler.\n\nFVC was estimated using the digital photography method described by [[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#B27-agriculture-16-01769)]. Three RGB images were captured along the diagonal of each sampling site using a Huawei P20 smartphone camera (1632 × 1224 pixels) with the camera positioned approximately 2 m above the canopy under nadir viewing geometry. Images were processed in MATLAB R2016b (MathWorks Inc., Natick, MA, USA) by converting RGB images into the CIE L*a*b* color space. The a* channel was used to separate vegetation from background, and vegetation pixels were extracted using a fixed threshold (a* = −8.0). FVC was calculated as the proportion of vegetation pixels within each image, and the mean value of the three images was used as the site-level FVC.\n\nAt crop maturity, grain yield was determined at both sampling-site and plot scales through manual harvesting. For site-level yield measurement, 10 representative plants were harvested at each sampling site, whereas the entire experimental plot was harvested for plot-level yield determination. After threshing and cleaning, the grains were air-dried to constant weight, and yield was expressed as kg\u002Fha based on dry grain weight.\n\n#### 2.2.3. UAV Multispectral Data\n\nUAV multispectral imagery was acquired on the same dates as the field measurements using a DJI Phantom 4 Multispectral (P4M) UAV (SZ DJI Technology Co., Shenzhen, China). The basic parameters of the P4M camera are summarized in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#table_body_display_agriculture-16-01769-t002). Flights were conducted between 10:00 and 14:00 under stable illumination conditions with a flight speed of 3 m s−1. Before each flight, radiometric calibration was performed using the DJI P4M reflectance calibration panel. The UAV was flown at an altitude of 40 m, resulting in a ground sampling distance (GSD) of approximately 2.1 cm, with 75% forward and side overlaps. The RTK positioning module of the UAV was enabled during data acquisition for image georeferencing. Images were acquired with the multispectral camera oriented nadir to the ground. Orthomosaic generation, radiometric processing, and inter-band alignment were performed using DJI Terra to generate reflectance mosaics for the blue, green, red, red-edge, and near-infrared bands, with a spatial resolution of approximately 2.1 cm. Regions of interest (ROIs) corresponding to each sampling site were delineated as 3 m × 3 m square areas around the sampling locations in ENVI 5.3. The mean reflectance values of each spectral band within each ROI were extracted for subsequent analysis.\n\n**Table 2.** Basic parameters of the P4M camera.\n\n### 2.3. Method\n\nThis study developed a framework for estimating crop salt stress and exploring differences among salinity-management practices by integrating UAV remote sensing, crop growth measurements, and soil data, as illustrated in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1769#fig_body_display_agriculture-16-01769-f002). First, partial least squares path modeling (PLS-PM) was used to derive relative weights for LAI, PH, SPAD, and FVC based on their relationships with baseline soil salinity, and these weighted indicators were integrated to con","Agriculture 2026, 16(16), 1769 (MDPI) 2026-08-18","2026-08-18T00:00:00Z",[232],{"name":229,"url":226},[234,235,30,236,237],"数字农业","农业无人机","多光谱","盐胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":241},{"candidates":242,"agricultural_evidence":46,"reason":62},[243,248,252,257,262,268],{"code":102,"score":244,"supported":46,"title":245,"abstract":246,"tags":247},11,[105],[105],[],{"code":109,"score":244,"supported":46,"title":249,"abstract":250,"tags":251},[112],[112],[],{"code":129,"score":82,"supported":46,"title":253,"abstract":255,"tags":256},[254],"无人机多光谱",[],[],{"code":44,"score":45,"supported":46,"title":258,"abstract":259,"tags":261},[],[260],"烟草",[],{"code":263,"score":45,"supported":46,"title":264,"abstract":265,"tags":267},"H2",[],[266],"土壤",[],{"code":58,"score":20,"supported":14,"title":269,"abstract":270,"tags":271},[],[],[234,30],[273,274],"农业无人机 数字农业 精准农业 多光谱","农业无人机 数字农业","农业无人机数字农业精准农业多光谱-711","2026-08-18T23:59:19.258819Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":8,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":285,"score_detail":286,"sources":291,"tags":293,"primary_topic":299,"secondary_topics":300,"classification":301,"search_phrases":336,"slug":339,"view_count":137,"doi":340,"paper":341,"created_at":382},5550,"Integrated effects of fermented organic manure on soil fertility, productivity, and economic performance of the rice–wheat cropping system in Uttar Pradesh","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1968370","The rice–wheat cropping system (RWCS) is a major cereal-based production system in Uttar Pradesh, India, where sustaining crop productivity and soil fertility under intensive cultivation is a major challenge. This study evaluated the field-level performance of fermented organic manure (FOM) integrated with inorganic fertilizers in terms of crop productivity, selected soil fertility indicators, and economic returns during 2025–2026. A total of 224 frontline demonstrations (FLDs) were conducted through 25 Krishi Vigyan Kendras (KVKs) across Uttar Pradesh. The analysis included demonstrations with sufficiently complete and consistent field-level records, comprising 40 rice and 110 wheat demonstrations under farmers’ field conditions and 6 rice and 11 wheat demonstrations at KVK instructional farms. Seven nutrient management treatments were evaluated at KVK farms, compared with three treatments under farmers’ field conditions. Crop performance, post-harvest soil fertility indicators, and economic returns were assessed. The treatment combining 100% recommended inorganic fertilizers with 125% FOM (T₃) recorded the highest numerical means for most growth and yield attributes and grain yield in both crops. Compared with 100% recommended inorganic fertilizers alone, T₃ increased grain yield by 6.4–7.3% in rice and 10.8–12.7% in wheat. FOM-integrated treatments also showed higher numerical values of soil organic carbon (SOC) and available nitrogen, phosphorus, and potassium after the rice–wheat cropping cycle. Under T₃, SOC increased from 0.271% to 0.304% at KVK instructional farms and from 0.447% to 0.515% under farmers’ field conditions. Although T₃ generally recorded the highest numerical gross returns, the additional costs of FOM reduced net returns and benefit–cost (B) ratios in several cases. Under farmers’ field conditions, inorganic fertilizers alone recorded higher numerical net returns and B ratios for both crops. The findings indicate that integrating FOM with recommended inorganic fertilizers was associated with improved crop productivity and selected soil fertility indicators under the demonstrated rice–wheat systems. However, the economic advantage depended on the costs of FOM production, transportation, and application. As the demonstrations were participatory and lacked formal plot-level randomization and inferential statistical testing, the observed differences represent descriptive numerical responses rather than statistically established treatment effects. Further controlled trials and cost optimization are needed to establish the agronomic and economic viability of FOM-integrated nutrient management.","稻麦种植制度（RWCS）是印度北方邦以谷类作物为主的重要生产体系，在集约化种植下维持作物生产力与土壤肥力是该体系面临的主要挑战。本研究评估了发酵有机肥（FOM）与无机肥料配合施用在2025—2026年度对作物生产力、选定土壤肥力指标及经济效益的田间表现。研究通过北方邦25个农业科技推广中心（KVKs）开展了224项前线示范（FLDs）。分析纳入了田间记录足够完整且一致的示范，包括农民田块条件下的40项水稻和110项小麦示范，以及KVK教学农场的6项水稻和11项小麦示范。在KVK农场评估了7个养分管理处理，并与农民田块条件下的3个处理进行比较。评估了作物表现、收获后土壤肥力指标及经济效益。100%推荐无机肥料配合125% FOM的处理（T₃）在两种作物的多数生长和产量性状及籽粒产量上记录了最高的数值均值。与单施100%推荐无机肥料相比，T₃使水稻籽粒产量提高6.4%—7.3%，小麦提高10.8%—12.7%。整合FOM的处理在稻麦轮作周期后也表现出较高的土壤有机碳（SOC）及有效氮、磷、钾数值。在T₃处理下，KVK教学农场的SOC从0.271%提高到0.304%，农民田块条件下从0.447%提高到0.515%。尽管T₃总体上记录了最高的数值总收益，但FOM的额外成本在多种情况下降低了净收益和效益成本比（B）。在农民田块条件下，单施无机肥料在两种作物上均记录了较高的数值净收益和B比。研究结果表明，在示范的稻麦系统中，FOM与推荐无机肥料配合施用与作物生产力及选定土壤肥力指标的改善相关。然而，经济优势取决于FOM的生产、运输和施用成本。由于示范为参与式，缺乏正式的田块水平随机化和推断性统计检验，观察到的差异代表描述性数值响应，而非统计学上确立的处理","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z",71,{"impact":287,"substance":17,"depth":288,"authority":187,"freshness":289,"relevant":21,"comment":290},16,15,9,"基于224个前线示范的实地数据，结论有参考价值，但缺乏随机化与统计检验，且为印度区域研究，属细分领域进展。",[292],{"name":283,"url":280},[294,295,296,297,298],"有机肥","土壤肥力","养分管理","印度农业","水稻小麦轮作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":302},{"candidates":303,"agricultural_evidence":46,"reason":62},[304,309,314,321,327,331],{"code":263,"score":288,"supported":46,"title":305,"abstract":307,"tags":308},[306],"soil",[266,306],[295],{"code":192,"score":81,"supported":46,"title":310,"abstract":312,"tags":313},[311],"rice",[195,311],[],{"code":315,"score":81,"supported":46,"title":316,"abstract":318,"tags":320},"A2",[317],"wheat",[319,317],"小麦",[],{"code":322,"score":82,"supported":46,"title":323,"abstract":324,"tags":326},"F3",[],[325,296],"肥料",[296,294],{"code":116,"score":45,"supported":46,"title":328,"abstract":329,"tags":330},[],[119],[],{"code":332,"score":21,"supported":14,"title":333,"abstract":334,"tags":335},"J3",[],[],[297],[337,338],"发酵有机肥 稻麦轮作","Uttar Pradesh 稻麦系统","发酵有机肥稻麦轮作-5550","10.3389\u002Ffsufs.2026.1968370",{"doi":340,"openalex_id":342,"authors":343,"venue":283,"cited_by_count":137,"oa_url":376,"card":377,"direction":156,"ingested_from":159},"W7223144837",[344,346,349,350,353,356,358,359,361,364,366,369,371,373],{"name":345,"orcid":8},"Raghwendra Singh",{"name":347,"orcid":348},"Ajay Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-8743-6477",{"name":347,"orcid":348},{"name":351,"orcid":352},"Preeti P. Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7843-9783",{"name":354,"orcid":355},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0002-9773-6641",{"name":357,"orcid":8},"Abhishek Singh",{"name":357,"orcid":8},{"name":360,"orcid":8},"Saurabh Raj Pandey",{"name":362,"orcid":363},"Vinod Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0004-2583-6565",{"name":365,"orcid":8},"Narendra Raghubanshi",{"name":367,"orcid":368},"Yash Pal Singh","https:\u002F\u002Forcid.org\u002F0000-0001-8345-9084",{"name":370,"orcid":8},"Mahesh Kumar",{"name":372,"orcid":8},"Shivangi Singh",{"name":374,"orcid":375},"Reena Kumari","https:\u002F\u002Forcid.org\u002F0000-0002-1101-5473","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1968370\u002Fpdf",{"tldr":378,"method":379,"finding":380,"direction":156,"opportunity":381},"评估发酵有机肥配施化肥对稻麦轮作产量、土壤肥力与经济收益的综合影响。","印度北方邦224个农户与KVK示范基地田间示范，比较7种养分管理处理。","全量化肥配125%发酵有机肥增产稻6.4-7.3%、麦10.8-12.7%并提升有机碳，但净收益常因","可研究有机肥成本分摊与碳交易补偿机制，量化其经济可行性与土壤固碳协同效益。","2026-10-22T11:20:16.098808Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":8,"content":388,"source_name":389,"source_url":8,"published_at":390,"category":391,"cover_url":8,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":402,"secondary_topics":403,"classification":404,"search_phrases":426,"slug":429,"view_count":137,"doi":8,"paper":8,"created_at":430},5437,"上海农科院揭榜天长市粮食单产提升项目 攻关水稻秸秆田边发酵技术","https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html","天长市农业农村局公示 2026 年粮食单产提升「揭榜挂帅」项目实施单位，上海市农业科院与南京农业大学工学院分别揭榜水稻秸秆田边发酵技术研究与示范、秸秆田间就地焚燎消杀还田技术模式示范推广两个项目，公示期为 2026 年 10 月 20 日至 26 日。该举措旨在破解秸秆直接还田弊端、缓解秸秆禁烧压力，同时提高粮食单产，是科研院所参与县域粮食单产提升「揭榜挂帅」的具体落地案例。","403\n\nForbidden\n\nError Times: Wed, 21 Oct 2026 00:10:36 GMT   \nIP: 2600:1900:0:2105::1c01 Node information: PS-ATL-017OX49   \nURL: https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html   \nRequest-Id: 6ad802fc_PS-ATL-017OX49_7380-31487   \nCheck: Details\n\nERROR\n\nThe Requested URL could not be retrieved\n\nWhile trying to retrieve the URL:\n\nhttps:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html\n\nThe following error was encountered:\n\n*   Invalid Request\n\nThe access control configuration prevents your request at this time.\n\nPlease contact your service provider if you feel this is incorrect.\n\n[return](https:\u002F\u002Fwww.tianchang.gov.cn\u002Fzwdt\u002Ftzgg\u002F392406342.html#)","天长市人民政府 2026-10-20","2026-10-20T00:00:00Z","报道",41,{"impact":19,"substance":82,"depth":67,"authority":67,"freshness":289,"relevant":21,"comment":394},"地市级院地合作动态，主题相关但正文仅403字且原文不可访问，信息增量与专业深度有限，暂不宜进入每日精选。",[396],{"name":389,"url":386},[195,398,399,400,401],"院地合作","秸秆利用","粮食单产提升","天长市",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":405},{"candidates":406,"agricultural_evidence":46,"reason":62},[407,411,416,421],{"code":192,"score":19,"supported":46,"title":408,"abstract":409,"tags":410},[195],[195],[195],{"code":52,"score":19,"supported":46,"title":412,"abstract":414,"tags":415},[413],"单产提升",[413],[400],{"code":417,"score":21,"supported":14,"title":418,"abstract":419,"tags":420},"G1",[],[],[398],{"code":422,"score":21,"supported":14,"title":423,"abstract":424,"tags":425},"H5",[],[],[399],[427,428],"上海农科院 天长市 水稻秸秆","天长市 粮食单产提升 秸秆发酵","上海农科院天长市水稻秸秆-5437","2026-10-21T00:10:01.423686Z",{"id":432,"title":433,"url":434,"summary":435,"summary_zh":436,"content":8,"source_name":437,"source_url":434,"published_at":438,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":443,"tags":445,"primary_topic":450,"secondary_topics":451,"classification":452,"search_phrases":471,"slug":474,"view_count":137,"doi":475,"paper":476,"created_at":488},5404,"Validation of a Day-of-Year Standardized Canopy Indicator for Regional Crop Monitoring: Suitability and Decoupling Boundaries","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.1707.v1","Translating satellite-derived crop canopy anomalies into regional production signals is critical for agricultural monitoring. However, standard anomaly indicators assume a stable, direct coupling between greenness and production, ignoring phenological shifts, sub-seasonal cropping dynamics, and harvested acreage fluctuations. I evaluate the validation boundaries and expected conditions of a pixel-level, day-of-year (DOY)-first Leaf Area Index (LAI) Z-score anomaly indicator with a leave- year-out baseline (2003–2022) across five contrasting global breadbaskets: Australia, Indo-Gangetic Plains (IGP), North China Plain (NCP), US Corn Belt, and Ukraine. The analysis shows that DOY- first standardization reduces temporal composite timing mismatches, though coupling with regional statistics varies systematically. Retrospective validation reveals that direct canopy-yield coupling remains robust exclusively in rainfed Australia (Pearson’s r = 0.90), where yield variability dominates production variance (explaining 90% of variance) and moisture stress limits canopy development. In contrast, three interpretation boundaries are identified: (1) greening trend-sensitivity in the IGP, where detrending reduces the association to r = 0.53; (2) temporal representativeness bias in the double-cropped NCP, requiring crop-calendar windowing to recover the wheat signal (r = 0.51); and (3) area-shock-sensitivity in Ukraine (where harvested area contractions explain 78% of production variance), decoupling canopy vigor from production (r = 0.40). Furthermore, the LAI Z-score provides a more interpretable canopy-response measure than the Vegetation Condition Index (VCI) in dense canopies where NDVI saturates. These validation levels are synthesized into a prototype diagnostic decision framework to support indicator configuration in operational monitoring systems.","将卫星反演的作物冠层异常转化为区域产量信号对农业监测至关重要。然而，标准异常指标假定绿度与产量之间存在稳定、直接的耦合关系，忽略了物候偏移、次季节作物动态以及收获面积波动。我评估了一种像元级、以年积日（DOY）为首要标准化维度的叶面积指数（LAI）Z分数异常指标在五个对比鲜明的全球粮仓——澳大利亚、印度河-恒河平原（IGP）、华北平原（NCP）、美国玉米带和乌克兰——的验证边界与预期适用条件，该指标采用留一年基线（2003—2022年）。分析表明，以DOY为首要标准化维度可减少时间合成时相不匹配，但其与区域统计数据的耦合关系存在系统性差异。回顾性验证显示，冠层与产量的直接耦合仅在雨养条件下的澳大利亚保持稳健（Pearson's r = 0.90），该地区产量变异性主导产量方差（解释90%的方差），且水分胁迫限制冠层发育。相比之下，识别出三个解释边界：（1）IGP的绿度趋势敏感性，去趋势后关联性降至r = 0.53；（2）双季种植的NCP的时间代表性偏差，需按作物日历开窗以恢复小麦信号（r = 0.51）；（3）乌克兰的面积冲击敏感性（收获面积缩减解释78%的产量方差），导致冠层活力与产量解耦（r = 0.40）。此外，在NDVI饱和的密集冠层中，LAI Z分数提供了比植被条件指数（VCI）更具可解释性的冠层响应度量。这些验证层级被综合为一个原型诊断决策框架，以支持业务化监测系统中的指标配置。","Preprints.org","2026-10-19T00:00:00Z",65,{"impact":19,"substance":441,"depth":17,"authority":67,"freshness":82,"relevant":21,"comment":442},21,"预印本提出按年积日标准化的LAI异常指标，并在五大农业区验证其适用边界，方法有新意但尚未经同行评审，属细分领域进展。",[444],{"name":437,"url":434},[87,446,447,448,449],"遥感","作物监测","产量估测","叶面积指数",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":453},{"candidates":454,"agricultural_evidence":46,"reason":62},[455,459,463,467],{"code":315,"score":67,"supported":46,"title":456,"abstract":457,"tags":458},[],[319,317],[],{"code":102,"score":67,"supported":46,"title":460,"abstract":461,"tags":462},[],[105,106],[],{"code":129,"score":45,"supported":14,"title":464,"abstract":465,"tags":466},[],[],[448,447,449,446],{"code":58,"score":21,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[87],[472,473],"LAI Z-score 作物监测","遥感 作物产量 估测","LAIZ-score作物监测-5404","10.20944\u002Fpreprints202610.1707.v1",{"doi":475,"openalex_id":477,"authors":478,"venue":437,"cited_by_count":137,"oa_url":434,"card":482,"direction":487,"ingested_from":159},"W7222967882",[479],{"name":480,"orcid":481},"Xiaolong Song","https:\u002F\u002Forcid.org\u002F0000-0002-2106-6629",{"tldr":483,"method":484,"finding":485,"direction":158,"opportunity":486},"验证了按年积日标准化的LAI Z-score冠层指标在五大农业区的适用边界。","用2003–2022年留一年基线，在五个全球粮仓区做LAI DOY标准化与产量相","仅雨养澳大利亚冠层-产量强耦合(r=0.90)，其余区域受趋势、物候和面积冲击影响而解耦。","可发展融合物候窗口、去趋势与收获面积因子的分区自适应冠层异常指标诊断框架。","农业人工智能与决策模型","2026-10-20T23:30:40.675672Z"]