[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2113":3,"related-2113":89},{"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,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":22,"doi":67,"paper":68,"created_at":88},2113,"Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112399","Orthogonal delineation of site-specific management zones using soil data: A multi-property optimization approach。Computers and Electronics in Agriculture","基于土壤数据的特定管理区正交划分：一种多属性优化方法。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-09-11T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,14,9,1,"核心期刊论文，提出基于多属性优化的管理分区正交划分方法，方法新颖且对精准农业变量管理有直接参考价值，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","变量施肥","精准农业","土壤数据","管理分区",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},15,true,[34],[50,34],"土壤",[30],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I1",2,[],[],[27,29],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"F3",[],[],[28],[64,65],"变量施肥 土壤数据 智慧农业 管理分区","变量施肥 土壤数据","变量施肥土壤数据智慧农业管理分区-2113","10.1016\u002Fj.compag.2026.112399",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":79,"oa_url":80,"card":81,"direction":85,"ingested_from":87},"W7157647968",[71,74,76],{"name":72,"orcid":73},"Salvador J. Vicencio-Medina","https:\u002F\u002Forcid.org\u002F0000-0002-3285-097X",{"name":75,"orcid":9},"Armin Lüer‐Villagra",{"name":77,"orcid":78},"Gonzalo Méndez-Vogel","https:\u002F\u002Forcid.org\u002F0000-0002-6455-7305",0,"https:\u002F\u002Ffigshare.com\u002Farticles\u002Fjournal_contribution\u002FOrthogonal_delineation_of_site-specific_management_zones_using_soil_data_a_multi-property_optimization_approach\u002F32095483",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出多属性优化方法，利用土壤数据正交划分特定地点管理分区。","多属性优化与正交划分，基于土壤属性数据。","多属性优化可有效生成管理分区，提升分区独立性。","农业遥感与作物表型","可结合遥感与实时传感器数据，发展动态管理分区与变量施肥决策。","openalex","2026-09-11T23:30:01.392377Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,172,267,328,398,445],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":9,"content":9,"source_name":97,"source_url":95,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"primary_topic":110,"secondary_topics":115,"classification":122,"search_phrases":150,"slug":153,"view_count":90,"doi":154,"paper":155,"created_at":171},3523,"Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10458-y","Soil-driven variability in crop response to variable-rate seeding and fertilization in an irrigated maize–sunflower system。Precision Agriculture","Precision Agriculture","2026-09-25T00:00:00Z",69,{"impact":17,"substance":101,"depth":19,"authority":20,"freshness":102,"relevant":22,"comment":103},18,8,"精准农业核心期刊论文，探讨土壤空间变异对变量播种与施肥效果的影响，方法新颖、结论可靠，对智慧农业田间管理有参考价值，但属细分领域研究，影响范围有限。",[105],{"name":97,"url":95},[27,28,107,29,108,109],"玉米","向日葵","变量播种",{"code":111,"slug":112,"name":107,"path":113,"parent_code":114},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[116,121],{"code":59,"slug":117,"name":118,"path":119,"parent_code":120},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":123},{"candidates":124,"agricultural_evidence":47},[125,129,134,139,145],{"code":111,"score":17,"supported":47,"title":126,"abstract":127,"tags":128},[112],[112],[107],{"code":59,"score":17,"supported":47,"title":130,"abstract":132,"tags":133},[131],"fertilization",[131],[28],{"code":33,"score":135,"supported":47,"title":136,"abstract":137,"tags":138},11,[34],[34],[],{"code":53,"score":140,"supported":47,"title":141,"abstract":142,"tags":144},5,[],[143],"precision agriculture",[27,29],{"code":146,"score":22,"supported":14,"title":147,"abstract":148,"tags":149},"A4",[],[],[108],[151,152],"Precision Agriculture 变量播种 变量施肥","灌溉玉米 向日葵 土壤变异","PrecisionAgriculture变量播种变量施肥-3523","10.1007\u002Fs11119-026-10458-y",{"doi":154,"openalex_id":156,"authors":157,"venue":97,"cited_by_count":79,"oa_url":9,"card":9,"direction":9,"ingested_from":87},"W7214357812",[158,161,163,165,167,169],{"name":159,"orcid":160},"María Videgain","https:\u002F\u002Forcid.org\u002F0000-0002-3630-7931",{"name":162,"orcid":9},"J. A. Martínez-Casasnovas",{"name":164,"orcid":9},"S. Artero",{"name":166,"orcid":9},"A. Vigo",{"name":168,"orcid":9},"M. Vidal",{"name":170,"orcid":9},"F. J. García-Ramos","2026-09-26T23:30:03.127069Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":190,"secondary_topics":191,"classification":198,"search_phrases":236,"slug":239,"view_count":240,"doi":241,"paper":242,"created_at":266},3516,"Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16160","Predictive modelling of soil nutrient dynamics is an essential tool for promoting sustainable agricultural practices and environmentally responsible farming methods. The statistical and machine learning techniques used to forecast the availability and dynamics of soil nutrients are summarised in this review. The core frameworks for measuring spatio-temporal nutritional variability are established by traditional statistical approaches such as time-series models autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), multivariate techniques (Principal component analysis (PCA) and factor analysis) and geostatistical tools (kriging). By capturing intricate nonlinear interactions within heterogeneous agroecosystems, machine learning techniques like random forest, support vector machines and ensemble approaches (XGBoost, LightGBM and AdaBoost) provide higher prediction accuracy. Forecasting capabilities are further enhanced by hybrid frameworks [Autoregressive integrated moving average with exogenous variables–artificial neural network. (ARIMAX-ANN)] and deep learning architectures (Convolutional neural network (CNN), long short-term memory (LSTM), ANN). With R2 values above 0.93 and notable decreases in prediction errors, ensemble approaches routinely perform better than traditional linear models. Nevertheless, persistent challenges include data quality limitations, spatial sampling constraints, insufficient environmental covariates and reduced model transferability across diverse pedoclimatic regions. Integrating high-resolution soil properties, climatic variables, terrain attributes and spectral information with advanced modelling architectures remains crucial for enhancing predictive reliability, ultimately supporting precision nutrient management, improved fertiliser efficiency and environmentally responsible agricultural systems.","土壤养分动态的预测建模是推动可持续农业实践和环境友好型耕作方法的重要工具。本综述总结了用于预测土壤养分有效性及其动态变化的统计与机器学习技术。传统统计方法，如时间序列模型自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型（SARIMA）、多变量技术（主成分分析（PCA）和因子分析）以及地统计工具（克里金法），为量化养分的时空变异性奠定了核心框架。通过捕捉异质性农业生态系统中复杂的非线性相互作用，随机森林、支持向量机和集成方法（XGBoost、LightGBM和AdaBoost）等机器学习技术可实现更高的预测精度。混合框架[含外生变量的自回归积分滑动平均模型–人工神经网络（ARIMAX-ANN）]和深度学习架构（卷积神经网络（CNN）、长短期记忆网络（LSTM）、人工神经网络（ANN））进一步增强了预测能力。集成方法的R²值超过0.93，且预测误差显著降低，其表现通常优于传统线性模型。然而，持续存在的挑战包括数据质量限制、空间采样约束、环境协变量不足以及模型在不同土壤气候区域间可迁移性降低等问题。将高分辨率土壤属性、气候变量、地形属性和光谱信息与先进建模架构相结合，对于提高预测可靠性仍然至关重要，最终可为精准养分管理、提高肥料利用效率以及环境友好型农业系统提供支撑。","Plant Science Today","2026-09-24T00:00:00Z",79,{"impact":101,"substance":182,"depth":101,"authority":183,"freshness":102,"relevant":22,"comment":184},22,13,"系统综述土壤养分动态预测的统计与机器学习方法，方法体系完整、结论有量化支撑，对精准施肥与农业信息化有参考价值，但属综述类论文，产业级影响有限。",[186],{"name":178,"url":175},[27,28,188,29,189],"机器学习","土壤养分",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[192],{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":199},{"candidates":200,"agricultural_evidence":47},[201,205,209,216,223,230],{"code":33,"score":46,"supported":47,"title":202,"abstract":203,"tags":204},[34],[50,34],[189],{"code":53,"score":13,"supported":47,"title":206,"abstract":207,"tags":208},[143],[],[27,29],{"code":193,"score":13,"supported":47,"title":210,"abstract":211,"tags":215},[],[212,188,213,214],"深度学习","machine learning","deep learning",[188],{"code":59,"score":217,"supported":47,"title":218,"abstract":219,"tags":222},7,[],[220,221],"肥料","养分管理",[28],{"code":224,"score":225,"supported":47,"title":226,"abstract":227,"tags":229},"H6",3,[],[228],"农业生态",[],{"code":231,"score":225,"supported":47,"title":232,"abstract":233,"tags":235},"H8",[],[234],"可持续农业",[],[237,238],"土壤养分 预测模型 精准农业","机器学习 施肥管理 可持续农业","土壤养分预测模型精准农业-3516",4,"10.14719\u002Fpst.16160",{"doi":241,"openalex_id":243,"authors":244,"venue":178,"cited_by_count":79,"oa_url":175,"card":260,"direction":264,"ingested_from":87},"W7214167059",[245,248,251,254,257],{"name":246,"orcid":247},"R Rathna","https:\u002F\u002Forcid.org\u002F0009-0004-7797-2673",{"name":249,"orcid":250},"B Sivasankari","https:\u002F\u002Forcid.org\u002F0000-0001-9921-8170",{"name":252,"orcid":253},"R. Gangai Selvi","https:\u002F\u002Forcid.org\u002F0000-0002-4475-2293",{"name":255,"orcid":256},"J Prabhakaran","https:\u002F\u002Forcid.org\u002F0000-0001-7339-175X",{"name":258,"orcid":259},"K. G. Sabarinathan","https:\u002F\u002Forcid.org\u002F0000-0002-8659-6479",{"tldr":261,"method":262,"finding":263,"direction":264,"opportunity":265},"综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。","综述ARIMA、地统计、随机森林、XGBoost、CNN\u002FLSTM及混合模型。","集成与深度学习模型精度更高（R²>0.93），但数据质量与跨区迁移性仍是瓶颈。","农业人工智能与决策模型","可研究多源遥感与气候数据融合的迁移学习模型，提升跨区域养分预测泛化能力。","2026-09-25T23:30:54.950445Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":9,"content":9,"source_name":272,"source_url":9,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":274,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":286,"classification":288,"search_phrases":315,"slug":318,"view_count":54,"doi":9,"paper":319,"created_at":327},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",{"impact":46,"substance":182,"depth":101,"authority":20,"freshness":13,"relevant":22,"comment":275},"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[277],{"name":272,"url":270},[27,29,279,280,31],"黑土区","遥感",{"code":282,"slug":283,"name":284,"path":285,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[287],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":289},{"candidates":290,"agricultural_evidence":47},[291,297,301,305,311],{"code":282,"score":20,"supported":47,"title":292,"abstract":294,"tags":296},[293],"气候适应",[293,295],"干旱",[],{"code":33,"score":102,"supported":47,"title":298,"abstract":299,"tags":300},[34],[],[],{"code":53,"score":140,"supported":47,"title":302,"abstract":303,"tags":304},[],[29],[27,29],{"code":306,"score":240,"supported":47,"title":307,"abstract":308,"tags":310},"I3",[],[309],"remote sensing",[280],{"code":224,"score":225,"supported":47,"title":312,"abstract":313,"tags":314},[],[228],[],[316,317],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",{"doi":9,"openalex_id":9,"authors":320,"venue":9,"cited_by_count":79,"oa_url":9,"card":321,"direction":85,"ingested_from":326},[],{"tldr":322,"method":323,"finding":324,"direction":85,"opportunity":325},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","agent","2026-09-22T00:05:38.189091Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":345,"secondary_topics":346,"classification":353,"search_phrases":378,"slug":381,"view_count":54,"doi":382,"paper":383,"created_at":397},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":17,"substance":101,"depth":338,"authority":90,"freshness":21,"relevant":22,"comment":339},16,"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[341],{"name":334,"url":331},[27,343,29,344,30],"农业人工智能","作物推荐",{"code":193,"slug":194,"name":195,"path":196,"parent_code":197},[347,348],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":349,"slug":350,"name":351,"path":352,"parent_code":197},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":354},{"candidates":355,"agricultural_evidence":47},[356,362,366,371],{"code":193,"score":357,"supported":47,"title":358,"abstract":360,"tags":361},34,[213,214,359],"artificial intelligence",[212,188,213,214,359],[343],{"code":33,"score":46,"supported":47,"title":363,"abstract":364,"tags":365},[34],[50,34],[30],{"code":53,"score":13,"supported":47,"title":367,"abstract":369,"tags":370},[368],"smart agriculture",[],[27,29],{"code":349,"score":21,"supported":47,"title":372,"abstract":373,"tags":377},[],[374,375,376],"物联网","internet of things","IoT",[],[379,380],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能 作物推荐","农业人工智能作物推荐土壤数据智慧农业-2509","10.64388\u002Firev10i3-1722922",{"doi":382,"openalex_id":384,"authors":385,"venue":334,"cited_by_count":79,"oa_url":390,"card":391,"direction":396,"ingested_from":87},"W7212594050",[386,388],{"name":387,"orcid":9},"Snehal Sanjay Raut",{"name":389,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":392,"method":393,"finding":394,"direction":264,"opportunity":395},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:08.397298Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":9,"content":9,"source_name":403,"source_url":9,"published_at":404,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":412,"secondary_topics":413,"classification":414,"search_phrases":433,"slug":436,"view_count":79,"doi":9,"paper":437,"created_at":444},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":17,"substance":18,"depth":19,"authority":183,"freshness":102,"relevant":22,"comment":407},"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[409],{"name":403,"url":401},[343,188,29,30,411],"作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":415},{"candidates":416,"agricultural_evidence":47},[417,421,425,429],{"code":33,"score":18,"supported":47,"title":418,"abstract":419,"tags":420},[50,34],[50],[30],{"code":193,"score":54,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[343,188],{"code":53,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[29],{"code":306,"score":22,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[411],[434,435],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":438,"venue":9,"cited_by_count":79,"oa_url":9,"card":439,"direction":264,"ingested_from":326},[],{"tldr":440,"method":441,"finding":442,"direction":264,"opportunity":443},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","2026-10-21T00:10:08.428584Z",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":451,"source_url":448,"published_at":452,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":456,"tags":458,"primary_topic":461,"secondary_topics":465,"classification":472,"search_phrases":516,"slug":519,"view_count":54,"doi":9,"paper":520,"created_at":530},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":338,"substance":18,"depth":19,"authority":183,"freshness":102,"relevant":22,"comment":455},"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[457],{"name":451,"url":448},[27,29,280,459,460],"盐碱地治理","土壤监测",{"code":306,"slug":462,"name":463,"path":464,"parent_code":197},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[466,471],{"code":467,"slug":468,"name":469,"path":470,"parent_code":197},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":473},{"candidates":474,"agricultural_evidence":47},[475,480,488,492,496,500,506,510],{"code":306,"score":101,"supported":47,"title":476,"abstract":477,"tags":479},[309],[280,309,478],"multispectral",[280],{"code":467,"score":19,"supported":47,"title":481,"abstract":483,"tags":487},[482],"GIS",[484,485,482,486],"地理信息","空间分析","geographic information",[],{"code":33,"score":46,"supported":47,"title":489,"abstract":490,"tags":491},[34],[50,34],[459],{"code":53,"score":13,"supported":47,"title":493,"abstract":494,"tags":495},[143],[],[27,29],{"code":193,"score":90,"supported":47,"title":497,"abstract":498,"tags":499},[],[188,213],[],{"code":501,"score":225,"supported":47,"title":502,"abstract":503,"tags":505},"H3",[],[504],"灌溉",[],{"code":282,"score":225,"supported":47,"title":507,"abstract":508,"tags":509},[],[295],[],{"code":511,"score":225,"supported":47,"title":512,"abstract":513,"tags":515},"I7",[],[514],"methodological framework",[],[517,518],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":521,"authors":522,"venue":451,"cited_by_count":79,"oa_url":448,"card":525,"direction":85,"ingested_from":87},"W7221843179",[523],{"name":524,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":526,"method":527,"finding":528,"direction":85,"opportunity":529},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z"]