[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3190":3,"related-3190":57},{"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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":56},3190,"Comparing Irrigation Identification Methods in Colorado: Limitations of Downscaled SMAP Soil Moisture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183223","With limited freshwater resources and growing water demands, it is imperative to identify and monitor water needs. Agricultural irrigation is the world’s largest water user, comprising 45–90% of freshwater withdrawals. Identifying and tracking changes in irrigated land is necessary for sustainable water management and forecasting agricultural water needs and patterns; however, the low resolution of available remote sensing observations hinders field-scale analysis. Downscaling soil moisture observations has been offered as a solution to this problem. Our study compares the performance of five irrigation identification methods using a newly developed downscaled deep soil moisture extrapolation method used to estimate Soil Moisture Active Passive (SMAP) soil moisture (SM) at a spatial resolution of 400 m for 5 cm, 20 cm, and 50 cm depths. Using this data along with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation and Landsat Normalized Difference Vegetation Index (NDVI), we evaluate these methods with respect to crop type, irrigation type, and observation depth on agricultural fields in Colorado using the irrigation maps provided by the Colorado Decision Support System from 2015 through 2024. With no crop\u002Firrigation type–observation depth combination exceeding an F1 score of 0.149 or MCC value of 0.163, we find that none of the methods can accurately identify irrigated land regardless of crop type, irrigation type, and observation depth. Because these methods succeeded in earlier small-area studies, and because the classified maps resolved into large, spatially uniform blocks, we interpret this as a limitation specific to field-scale detection over large, heterogeneous regions rather than a defect in the dataset. These findings highlight a limitation of this downscaled SM dataset and raise the question of whether other downscaled soil moisture products share this limitation.","在淡水资源有限且用水需求不断增长的背景下，识别和监测水资源需求势在必行。农业灌溉是全球最大的用水部门，占淡水取水量的45%–90%。识别和追踪灌溉土地的变化对于可持续水资源管理以及预测农业用水需求和模式至关重要；然而，现有遥感观测的低分辨率阻碍了田块尺度的分析。土壤湿度观测数据的降尺度被提出作为解决这一问题的方法。本研究比较了五种灌溉识别方法的性能，所用数据基于新开发的降尺度深层土壤湿度外推方法，用于估算土壤湿度主动被动（SMAP）卫星在400 m空间分辨率下5 cm、20 cm和50 cm深度的土壤湿度（SM）。利用该数据以及气候灾害组红外降水与站点数据（CHIRPS）降水和Landsat归一化植被指数（NDVI），我们结合科罗拉多决策支持系统提供的2015年至2024年灌溉地图，在科罗拉多州的农田上就作物类型、灌溉类型和观测深度对这些方法进行了评估。在没有任何作物\u002F灌溉类型–观测深度组合的F1分数超过0.149或MCC值超过0.163的情况下，我们发现无论作物类型、灌溉类型和观测深度如何，这些方法均无法准确识别灌溉土地。由于这些方法在早期小区域研究中取得了成功，且分类地图呈现为大的、空间均一的斑块，我们将此解释为大规模异质区域上田块尺度检测所特有的局限性，而非数据集本身的缺陷。这些发现凸显了该降尺度SM数据集的局限性，并提出其他降尺度土壤湿度产品是否也存在这一局限性的问题。",null,"Remote Sensing","2026-09-19T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"该研究通过大区域对比实验揭示降尺度SMAP土壤水分在田块尺度灌溉识别上的局限，方法严谨、结论明确，对农业遥感与水资源管理有参考价值，但属细分领域学术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","水资源管理","土壤水分","灌溉识别",[33,34],"SMAP 土壤水分 灌溉识别","科罗拉多 灌溉制图 遥感","SMAP土壤水分灌溉识别-3190",0,"10.3390\u002Frs18183223",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":49,"direction":53,"ingested_from":55},"W7213934204",[41,44,46],{"name":42,"orcid":43},"Annelise M. Turman","https:\u002F\u002Forcid.org\u002F0009-0000-2392-3631",{"name":45,"orcid":9},"Bin Fang",{"name":47,"orcid":48},"V. Vijaya Lakshmi","https:\u002F\u002Forcid.org\u002F0000-0001-9522-7897",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"比较五种灌溉识别方法，发现降尺度SMAP土壤湿度在科罗拉多田间尺度无法准确识别灌溉。","用400米降尺度SMAP土壤湿度、CHIRPS降水和Landsat NDVI，对","所有方法F1最高仅0.149，无法准确识别灌溉地，归因于大区域异质性而非数据缺陷。","农业遥感与作物表型","可检验其他降尺度土壤湿度产品是否同样受大区域异质性限制，并探索融合多源数据提升田间尺度灌溉识别。","openalex","2026-09-22T23:30:26.627420Z",{"total":58,"page":22,"page_size":58,"items":59},6,[60,95,126,152,194,240],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":9,"content":9,"source_name":65,"source_url":63,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":94},2309,"Modeling Water Quality Parameters in Laga Dadi Reservoir, Ethiopia: An Integrated Remote Sensing and Machine Learning Approach","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10838920\u002Fv1","Modeling Water Quality Parameters in Laga Dadi Reservoir, Ethiopia: An Integrated Remote Sensing and Machine Learning Approach。Research Square","Research Square","2026-09-10T00:00:00Z",55,{"impact":69,"substance":19,"depth":70,"authority":58,"freshness":69,"relevant":22,"comment":71},8,15,"埃塞俄比亚水库水质遥感与机器学习建模研究，方法有参考价值但属区域性案例，公共影响有限。",[73],{"name":65,"url":63},[27,75,28,29,76],"机器学习","水质监测",[78,79],"水资源管理 智慧农业 机器学习 水质监测","水资源管理 智慧农业","水资源管理智慧农业机器学习水质监测-2309","10.21203\u002Frs.3.rs-10838920\u002Fv1",{"doi":81,"openalex_id":83,"authors":84,"venue":65,"cited_by_count":36,"oa_url":92,"card":9,"direction":93,"ingested_from":55},"W7212151964",[85,87,90],{"name":86,"orcid":9},"Sadirak Tasissa",{"name":88,"orcid":89},"Kenatu Angassa","https:\u002F\u002Forcid.org\u002F0000-0002-1449-2451",{"name":91,"orcid":9},"Workineh Tesfaye","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10838920\u002Flatest.pdf","智慧农业 \u002F 农业物联网","2026-09-13T23:30:12.531084Z",{"id":96,"title":97,"url":98,"summary":99,"summary_zh":9,"content":9,"source_name":100,"source_url":9,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":107,"tags":109,"search_phrases":113,"slug":116,"view_count":36,"doi":9,"paper":117,"created_at":125},3250,"农机化研究2026(11)：基于多源感知与语义分割的果树行识别与路径跟踪控制——郑州工业应用技术学院许洋洋等 DeepLabv3+ F1达0.94","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166268.html","农机化研究2026年第11期发表郑州工业应用技术学院许洋洋、柴亚珂、王莹、牛瑞利团队论文：针对果园中裸露土壤、遮蔽物干扰和冠层阴影等因素导致导航路径难以稳定提取的问题，提出融合多源感知与语义分割的自走式植保机果树识别与路径控制方法。首先利用无人机获取多光谱影像并生成正射影像（DOM）与数字表面模型（DSM），计算归一化差异绿度指数（NDGI），增强果树冠层与背景的可分性；然后采用U-Net\u002FDeepLabv3+语义分割网络在保留空间上下文信息的同时实现果树行\u002F背景端到端分割。田间对比试验结果表明，DeepLabv3+整体分割F1可达0.94，每样区生成导航线断裂1.2次，且转向工况下横向误差RMSE为0.11 m，显著优于传统阈值分割和SVM方法。","农机化研究","2026-09-22T00:00:00Z",79,{"impact":104,"substance":18,"depth":19,"authority":105,"freshness":13,"relevant":22,"comment":106},16,13,"多源遥感与语义分割结合的果园导航研究，F1达0.94且横向误差0.11米，方法新颖、数据扎实，具备行业参考价值。",[108],{"name":100,"url":98},[27,110,111,28,112],"农业人工智能","农机导航","果园植保",[114,115],"郑州工业应用技术学院 果树行识别","DeepLabv3 果园导航 路径跟踪","郑州工业应用技术学院果树行识别-3250",{"doi":9,"openalex_id":9,"authors":118,"venue":9,"cited_by_count":36,"oa_url":9,"card":119,"direction":53,"ingested_from":124},[],{"tldr":120,"method":121,"finding":122,"direction":53,"opportunity":123},"提出融合无人机多光谱与语义分割的果树行识别与路径跟踪方法，实现植保机自主导航。","无人机多光谱DOM\u002FDSM与NDGI，U-Net\u002FDeepLabv3+语义分割，","DeepLabv3+分割F1达0.94，导航线断裂1.2次，转向横向误差RMSE 0.11 m，优于","可探索多光谱与DSM特征融合的轻量化分割模型，并迁移至多树种、多季节果园导航。","agent","2026-09-23T00:04:33.496233Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":9,"content":9,"source_name":131,"source_url":9,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":135,"tags":137,"search_phrases":140,"slug":143,"view_count":36,"doi":9,"paper":144,"created_at":151},3246,"Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）","https:\u002F\u002Fwww.mdpi.com\u002F3043-1204\u002F1\u002F1\u002F4","MDPI AI Precis. Agric. 1(1) 4 发表系统综述：综述2019-2026年Scopus、Web of Science及补充检索文献，考察精准农业中的应用、多模态数据集成与决策支持。综述表明研究范式从孤立的制图与预测转向集成工作流，整合卫星与无人机影像、田间传感器、气象、土壤与管理记录。报告效益包括产量预测改善、压力与病害更早检测、土壤制图更精细、灌溉与投入品更精准。然而，产量、环境和经济效益仍依赖当地条件和运行验证。核心发现是：地理参考数据本身不能确保真正的地理空间AI；可靠工作流必须解决空间依赖性、尺度不匹配、可迁移性和不确定性。可解释性和融入农场运营对可执行推荐同样重要。采用仍受数据互操作性、连接性、可负担性、隐私和技术能力限制。","MDPI AI in Precision Agriculture",82,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":134},"系统综述梳理2019-2026年GeoAI在精准农业的集成工作流与落地瓶颈，结论扎实、时效性强，对智慧农业技术路线有参考价值。",[136],{"name":131,"url":129},[27,110,28,138,139],"决策支持","多模态数据",[141,142],"GeoAI 精准农业 系统综述","地理空间人工智能 精准农业","GeoAI精准农业系统综述-3246",{"doi":9,"openalex_id":9,"authors":145,"venue":9,"cited_by_count":36,"oa_url":9,"card":146,"direction":53,"ingested_from":124},[],{"tldr":147,"method":148,"finding":149,"direction":53,"opportunity":150},"系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。","系统综述Scopus、Web of Science等文献，分析多模态数据集成与决","地理参考数据不等于地理空间AI，可靠工作流须解决空间依赖、尺度、可迁移性与不确定性。","可研究跨尺度空间依赖建模与可迁移性评估，提升模型在不同农场条件下的泛化能力。","2026-09-23T00:04:33.172821Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":9,"source_name":158,"source_url":155,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":164,"tags":166,"search_phrases":170,"slug":173,"view_count":36,"doi":174,"paper":175,"created_at":193},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research","2026-09-21T00:00:00Z",65,{"impact":69,"substance":162,"depth":104,"authority":105,"freshness":69,"relevant":22,"comment":163},20,"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[165],{"name":158,"url":155},[27,167,168,28,169],"产量预测","玉米","NDVI",[171,172],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":174,"openalex_id":176,"authors":177,"venue":158,"cited_by_count":36,"oa_url":155,"card":188,"direction":53,"ingested_from":55},"W7213918280",[178,180,182,184,186],{"name":179,"orcid":9},"N Mohammad",{"name":181,"orcid":9},"MA Islam",{"name":183,"orcid":9},"MG Mahboob",{"name":185,"orcid":9},"MM Rahman",{"name":187,"orcid":9},"I Ahmed",{"tldr":189,"method":190,"finding":191,"direction":53,"opportunity":192},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","2026-09-22T23:30:23.551878Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":205,"tags":207,"search_phrases":210,"slug":213,"view_count":36,"doi":214,"paper":215,"created_at":239},3176,"Relaxing the clear-sky assumption: Cloud-tolerant spatiotemporal fusion via mask-guided feature modulation and temporal-memory collaboration","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jag.2026.105571","The precise characterization of dynamic Earth surface processes relies on time-series remote sensing imagery with both high spatial fidelity and frequent temporal coverage. While spatiotemporal fusion (STF) bridges this resolution trade-off by integrating complementary observations, its practical application remains constrained by the idealized ”clear-sky assumption”. This requirement often favors the selection of temporally distant cloud-free acquisitions over temporally closer but cloud-contaminated observations, potentially reducing the relevance of reference information. To address this limitation, we propose CloudSTF, a robust framework designed to achieve high-fidelity reconstruction by effectively exploiting partially occluded observations. Our approach employs a Mask-guided Multi-scale Swin Transformer (M 2 ST) encoder to capture cloud spatial patterns and suppress noise propagation during feature extraction. This is coupled with a Cross-temporal Memory-guided Fusion (CTMF) module that adaptively integrates temporal trends with textural details retrieved from a spatio-temporal memory bank. To validate this paradigm, we introduce the Global Cloud-shrouded Regions (GCR-STF) benchmark, a geographically distributed dataset comprising 24 representative agricultural and urban sites across six continents. Extensive experiments demonstrate that CloudSTF consistently outperforms state-of-the-art methods. Additional assessments across varying cloud densities and diverse landscapes further demonstrate the robustness and applicability of CloudSTF under realistic cloud-contaminated conditions. These results suggest that CloudSTF provides a reliable solution for continuous Earth monitoring in realistic, cloud-prone environments. Source code and the GCR-STF benchmark are available at https:\u002F\u002Fgithub.com\u002FSichen-Lu\u002FCloudSTF .","精确刻画动态地球表面过程依赖于兼具高空间保真度与高频时间覆盖的时间序列遥感影像。尽管时空融合（STF）通过整合互补观测弥合了这一分辨率之间的权衡，但其实际应用仍受限于理想化的“晴空假设”。该要求往往倾向于选择时间上相距较远但无云的影像，而非时间上更近但受云污染的观测，这可能降低参考信息的相关性。为解决这一局限，我们提出了CloudSTF，一个旨在通过有效利用部分被遮挡的观测来实现高保真重建的稳健框架。我们的方法采用掩膜引导的多尺度Swin Transformer（M²ST）编码器来捕捉云的空间模式并抑制特征提取过程中的噪声传播。该编码器与跨时间记忆引导融合（CTMF）模块相结合，后者自适应地将时间趋势与从时空记忆库中检索到的纹理细节进行整合。为验证这一范式，我们引入了全球云覆盖区域（GCR-STF）基准数据集，这是一个地理分布广泛的数据集，涵盖六大洲24个代表性农业和城市站点。大量实验表明，CloudSTF始终优于现有最先进方法。在不同云密度和多样景观下的额外评估进一步证明了CloudSTF在真实云污染条件下的稳健性和适用性。这些结果表明，CloudSTF为真实且多云环境中的连续地球监测提供了可靠解决方案。源代码和GCR-STF基准数据集可在https:\u002F\u002Fgithub.com\u002FSichen-Lu\u002FCloudSTF获取。","International Journal of Applied Earth Observation and Geoinformation",80,{"impact":104,"substance":18,"depth":203,"authority":20,"freshness":21,"relevant":22,"comment":204},19,"提出抗云时空融合框架CloudSTF并发布全球云覆盖基准数据集，方法新颖、数据规模大，对多云地区农业遥感监测有实用价值。",[206],{"name":200,"url":197},[27,110,28,208,209],"时空融合","云污染",[211,212],"CloudSTF 时空融合","GCR-STF 云覆盖基准数据集","CloudSTF时空融合-3176","10.1016\u002Fj.jag.2026.105571",{"doi":214,"openalex_id":216,"authors":217,"venue":200,"cited_by_count":36,"oa_url":197,"card":234,"direction":53,"ingested_from":55},"W7213903535",[218,221,224,226,229,231],{"name":219,"orcid":220},"Sichen Lu","https:\u002F\u002Forcid.org\u002F0009-0009-3215-6521",{"name":222,"orcid":223},"Juanjuan Jing","https:\u002F\u002Forcid.org\u002F0009-0002-0371-7245",{"name":225,"orcid":9},"Junhua Yu",{"name":227,"orcid":228},"Lei Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8297-0868",{"name":230,"orcid":9},"Boyang Nie",{"name":232,"orcid":233},"Jinsong Zhou","https:\u002F\u002Forcid.org\u002F0009-0006-4704-0685",{"tldr":235,"method":236,"finding":237,"direction":53,"opportunity":238},"提出CloudSTF框架，利用含云影像实现高保真时空融合，突破晴空假设。","掩膜引导多尺度Swin Transformer编码器与跨时记忆融合模块，构建GC","在六个大洲24个站点上优于现有方法，不同云密度下均鲁棒。","可探索云污染下融合结果对作物长势监测与产量估测的精度影响。","2026-09-22T23:30:23.475701Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":9,"content":9,"source_name":245,"source_url":243,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":250,"tags":252,"search_phrases":256,"slug":259,"view_count":36,"doi":260,"paper":261,"created_at":272},3175,"Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40899-026-01401-0","Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt。Sustainable Water Resources Management","Sustainable Water Resources Management",71,{"impact":17,"substance":162,"depth":248,"authority":105,"freshness":21,"relevant":22,"comment":249},17,"埃及干旱区中心支轴灌溉下遥感蒸散发模型可靠性评估，方法扎实但属区域案例研究，对国内智慧灌溉有参考价值。",[251],{"name":245,"url":243},[28,29,253,254,255],"精准灌溉","蒸散发","干旱区农业",[257,258],"East Oweinat 中心支轴灌溉","遥感 蒸散发 模型 灌溉管理","EastOweinat中心支轴灌溉-3175","10.1007\u002Fs40899-026-01401-0",{"doi":260,"openalex_id":262,"authors":263,"venue":245,"cited_by_count":36,"oa_url":9,"card":9,"direction":53,"ingested_from":55},"W7213900869",[264,266,268,270],{"name":265,"orcid":9},"Hadeel A. Ibrahim",{"name":267,"orcid":9},"Mohamed H. Elgamal",{"name":269,"orcid":9},"Ashraf M. Ghanem",{"name":271,"orcid":9},"Mohamed H. Nour","2026-09-22T23:30:23.383931Z"]