[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2437":3,"related-2437":106},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":82,"slug":85,"view_count":21,"doi":86,"paper":87,"created_at":105},2437,"Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52151\u002Fjae2026634.2043","Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-consuming, labour-intensive and limited in spatial coverage, restricting their applicability for regional-scale monitoring. Remote sensing integrated with machine learning provides a promising alternative for generating spatially continuous and timely soil moisture estimates. This study aimed to predict surface soil moisture in the Ranipool-Rumtek administrative region of Sikkim, India, using multimodal remote sensing data and machine-learning techniques. Soil moisture was measured at 85 locations using the gravimetric method and these same locations were used as ground truthing sites. Multi-temporal imagery from Landsat-8 and Sentinel-2 was processed to derive vegetation and moisture-related indices, i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Shortwave-infrared Difference Soil Moisture Index (NSDSI3), Land Surface Temperature (LST), Moisture Stress Index (MSI), and Vegetation Supply Water Index (VSWI). These indices were used as predictor variables to develop artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) models. The results revealed that the ANN model developed using Sentinel-2-derived indices exhibited the highest predictive accuracy, achieving values of coefficient of determination (R2) as 0.82, Root Mean Square Error (RMSE) as 6% and Mean Absolute Error (MAE) of 4.6%. The performance of the Sentinel-2-derived ANN model was found to be better than that of the Landsat-8-based ANN model as well as the SVM and MLR models developed using Sentinel-2 data. The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments. The proposed approach provides a robust framework to support precision agriculture, irrigation scheduling, hydrological modelling, and drought and flood monitoring.","土壤水分是影响农业生产力、水文过程和土地管理的重要变量，尤其是在印度东北丘陵邦（NEH）等高降雨地区。尽管传统土壤水分测量技术能够提供可靠的观测数据，但其耗时、费力且空间覆盖有限，限制了其在区域尺度监测中的适用性。遥感与机器学习相结合，为生成空间连续且及时的土壤水分估算提供了一种有前景的替代方案。本研究旨在利用多模态遥感数据和机器学习技术，预测印度锡金邦拉尼普尔-鲁姆特克行政区的表层土壤水分。采用重量法在85个位置测量了土壤水分，并将这些位置作为地面验证点。对Landsat-8和Sentinel-2的多时相影像进行处理，以提取植被和水分相关指数，即归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、归一化差异水分指数（NDMI）、归一化短波红外差异土壤水分指数（NSDSI3）、地表温度（LST）、水分胁迫指数（MSI）和植被供水指数（VSWI）。这些指数被用作预测变量，以构建人工神经网络（ANN）、支持向量机（SVM）和多元线性回归（MLR）模型。结果表明，使用Sentinel-2衍生指数构建的ANN模型预测精度最高，决定系数（R²）达到0.82，均方根误差（RMSE）为6%，平均绝对误差（MAE）为4.6%。Sentinel-2衍生的ANN模型性能优于基于Landsat-8的ANN模型以及使用Sentinel-2数据构建的SVM和MLR模型。较强的预测性能表明，将高分辨率Sentinel-2影像数据与ANN相结合，能够在地形起伏大、数据稀疏的环境中实现准确、可扩展且高效的土壤水分估算。所提出的方法为支持精准农业、灌溉调度、水文建模以及旱涝监测提供了一个稳健的框架。",null,"Journal of Agricultural Engineering (India)","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,12,6,1,"基于多模态遥感与人工神经网络的土壤墒情预测研究，方法新颖、精度可靠，对高降雨山区精准农业与灌溉调度有参考价值，但属区域性案例，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","机器学习","遥感","精准灌溉","土壤墒情",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":81},[39,47,53,59,67,74],{"code":40,"score":17,"supported":41,"title":42,"abstract":44,"tags":46},"H2",true,[43],"soil",[45,43],"土壤",[31],{"code":48,"score":17,"supported":41,"title":49,"abstract":51,"tags":52},"I2",[50],"machine learning",[28,50],[28],{"code":54,"score":17,"supported":41,"title":55,"abstract":57,"tags":58},"I3",[56],"remote sensing",[29,56],[29],{"code":60,"score":61,"supported":41,"title":62,"abstract":63,"tags":66},"H3",7,[],[64,65],"灌溉","irrigation",[30],{"code":68,"score":61,"supported":41,"title":69,"abstract":70,"tags":73},"I1",[],[71,72],"精准农业","precision agriculture",[27],{"code":75,"score":76,"supported":41,"title":77,"abstract":78,"tags":80},"H7",3,[],[79],"drought",[],"多个产业证据相当，需要确认主归属",[83,84],"土壤墒情 智慧农业 机器学习 精准灌溉","土壤墒情 智慧农业","土壤墒情智慧农业机器学习精准灌溉-2437","10.52151\u002Fjae2026634.2043",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":97,"oa_url":9,"card":98,"direction":102,"ingested_from":104},"W7212454193",[90,92,95],{"name":91,"orcid":9},"Pranjal Dubey",{"name":93,"orcid":94},"G. T. Patle","https:\u002F\u002Forcid.org\u002F0000-0002-9175-8567",{"name":96,"orcid":9},"Vinay Kumar Gautam",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。","Landsat-8与Sentinel-2植被\u002F水分指数，ANN、SVM、MLR建","Sentinel-2指数驱动的ANN精度最高，R²=0.82、RMSE=6%，优于Landsat-8","农业遥感与作物表型","可探索多源时序遥感与深度学习融合，提升高降雨山区土壤水分时空连续估算与业务化监测能力。","openalex","2026-09-14T23:30:27.771449Z",{"total":21,"page":22,"page_size":21,"items":107},[108,188,263,369,443,495],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"primary_topic":126,"secondary_topics":131,"classification":139,"search_phrases":163,"slug":166,"view_count":22,"doi":167,"paper":168,"created_at":187},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",72,{"impact":118,"substance":119,"depth":19,"authority":118,"freshness":120,"relevant":22,"comment":121},13,21,8,"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[123],{"name":114,"url":111},[27,125,28,30,31],"农业人工智能",{"code":48,"slug":127,"name":128,"path":129,"parent_code":130},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[132,136],{"code":40,"slug":43,"name":133,"path":134,"parent_code":135},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":60,"slug":65,"name":137,"path":138,"parent_code":135},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":140,"locked":14,"version":35,"method":36,"evidence":141},"classified",{"candidates":142,"agricultural_evidence":41},[143,148,152,158],{"code":48,"score":144,"supported":41,"title":145,"abstract":146,"tags":147},16,[50],[28,50],[125,28],{"code":40,"score":17,"supported":41,"title":149,"abstract":150,"tags":151},[43],[45,43],[31],{"code":60,"score":13,"supported":41,"title":153,"abstract":154,"tags":157},[],[64,155,65,156],"蒸散发","evapotranspiration",[30],{"code":68,"score":159,"supported":41,"title":160,"abstract":161,"tags":162},9,[72],[],[27],[164,165],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":167,"openalex_id":169,"authors":170,"venue":114,"cited_by_count":97,"oa_url":9,"card":181,"direction":185,"ingested_from":104},"W7213181547",[171,174,177,179],{"name":172,"orcid":173},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":175,"orcid":176},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":178,"orcid":9},"Kinnoree R. Pasha",{"name":180,"orcid":9},"Dilruba Yeasmin",{"tldr":182,"method":183,"finding":184,"direction":185,"opportunity":186},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","农业人工智能与决策模型","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":196,"sources":201,"tags":203,"primary_topic":205,"secondary_topics":211,"classification":213,"search_phrases":241,"slug":244,"view_count":76,"doi":245,"paper":246,"created_at":262},1905,"A novel surface reflectance calibration approach to improve the estimation of maize actual evapotranspiration in a semi-arid USA region","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727171","This study aimed to develop a novel surface reflectance (SR) data calibration approach to improve maize actual crop evapotranspiration (ETa, mm h−1) predictions in agricultural settings. The remote sensing (RS) platforms used in the study included Landsat-8 (30 m spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld radiometer (1 m), and a small uncrewed aerial system or sUAS (0.03 m). A one-source surface energy balance (OSEB) was applied to estimate ETa and assess any improvements when using original and calibrated SR data. Five machine-learning (ML) algorithms were evaluated as part of the calibration process. The ML approaches were linear regression, regression tree, random forest, support vector machine, and Gaussian process regression. The SR calibration approach incorporated a novel dual-source pixel decomposition model that accounts for the contributions of soil and vegetation light reflectance captured by a given SR band. Two maize research sites, one in Greeley and another in Fort Collins, Colorado, U.S.A. provided the grounds for data collection over a four-year period. Results indicated that the best ML model, for a given RS sensor, depended on the SR contributions from plants and bare soil. Among the evaluated algorithms, the Regression Tree and Gaussian Process Regression provided the most accurate pixel decomposition and reflectance adjustments, consistently demonstrating the highest statistical agreement (R2 = 0.70–0.94) and lowest residual errors (RMSE = 0.004–0.022). Improvements in maize hourly ETa had a 34% error reduction in maize ETa estimation for data from the handheld radiometer and sUAS, compared to a 12% error reduction for the spaceborne sensors, on average.","本研究旨在开发一种新型地表反射率（SR）数据校准方法，以提高农业环境中玉米实际作物蒸散量（ETa, mm h⁻¹）的预测精度。研究中使用的遥感（RS）平台包括Landsat-8（空间分辨率30米）、Sentinel-2（10米）、Planet CubeSat（3米）、手持式辐射计（1米）以及小型无人机系统（sUAS，0.03米）。采用单源地表能量平衡（OSEB）模型估算ETa，并评估使用原始与校准SR数据时的改进效果。校准过程中评估了五种机器学习（ML）算法，包括线性回归、回归树、随机森林、支持向量机和高斯过程回归。SR校准方法引入了一种新型双源像元分解模型，该模型考虑了特定SR波段所捕获的土壤与植被光反射的贡献。研究选取了美国科罗拉多州格里利和柯林斯堡的两个玉米研究站点，在四年期间收集数据。结果表明，对于给定的遥感传感器，最佳ML模型取决于植被和裸土的SR贡献。在所评估的算法中，回归树和高斯过程回归提供了最精确的像元分解和反射率调整，始终表现出最高的一致性统计指标（R² = 0.70–0.94）和最低的残差误差（RMSE = 0.004–0.022）。在玉米小时ETa估算改进方面，手持式辐射计和sUAS数据的ETa估算误差平均降低了34%，而星载传感器的误差平均降低了12%。","International Journal of Remote Sensing","2026-09-06T00:00:00Z",{"impact":17,"substance":197,"depth":198,"authority":199,"freshness":76,"relevant":22,"comment":200},22,18,14,"研究提出地表反射率校准新方法，结合多源遥感与机器学习提升玉米蒸散估算精度，对农业水资源管理有参考价值。",[202],{"name":194,"url":191},[27,28,29,204,30],"作物模型",{"code":206,"slug":207,"name":208,"path":209,"parent_code":210},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[212],{"code":60,"slug":65,"name":137,"path":138,"parent_code":135},{"status":140,"locked":14,"version":35,"method":36,"evidence":214},{"candidates":215,"agricultural_evidence":41},[216,220,224,228,232,237],{"code":206,"score":199,"supported":41,"title":217,"abstract":218,"tags":219},[207],[208,207],[],{"code":60,"score":20,"supported":41,"title":221,"abstract":222,"tags":223},[156],[156],[30],{"code":54,"score":61,"supported":41,"title":225,"abstract":226,"tags":227},[],[29,56],[29],{"code":40,"score":21,"supported":41,"title":229,"abstract":230,"tags":231},[],[45,43],[],{"code":48,"score":233,"supported":41,"title":234,"abstract":235,"tags":236},5,[],[28],[204,28],{"code":68,"score":22,"supported":14,"title":238,"abstract":239,"tags":240},[],[],[27],[242,243],"作物模型 智慧农业 机器学习 精准灌溉","作物模型 智慧农业","作物模型智慧农业机器学习精准灌溉-1905","10.1080\u002F01431161.2026.2727171",{"doi":245,"openalex_id":247,"authors":248,"venue":194,"cited_by_count":97,"oa_url":9,"card":257,"direction":102,"ingested_from":104},"W7210284621",[249,252,255],{"name":250,"orcid":251},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":253,"orcid":254},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":256,"orcid":9},"Huihui Zhang",{"tldr":258,"method":259,"finding":260,"direction":102,"opportunity":261},"提出地表反射率校准方法，结合机器学习与双源像素分解，提高半干旱区玉米蒸散发估算精度。","多源遥感数据，OSEB模型，五种机器学习算法，双源像素分解模型。","回归树和高斯过程回归校准效果最佳，误差降低12%-34%，高分辨率传感器提升更显著。","可探索将校准方法应用于其他作物或区域，或结合深度学习提升低分辨率卫星数据的校准精度。","2026-09-08T23:30:20.154043Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":274,"tags":276,"primary_topic":278,"secondary_topics":279,"classification":286,"search_phrases":339,"slug":342,"view_count":97,"doi":343,"paper":344,"created_at":368},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":198,"substance":197,"depth":198,"authority":20,"freshness":159,"relevant":22,"comment":273},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[275],{"name":269,"url":266},[27,125,29,277,30],"作物水分胁迫",{"code":60,"slug":65,"name":137,"path":138,"parent_code":135},[280,281],{"code":48,"slug":127,"name":128,"path":129,"parent_code":130},{"code":282,"slug":283,"name":284,"path":285,"parent_code":130},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":140,"locked":14,"version":35,"method":36,"evidence":287},{"candidates":288,"agricultural_evidence":41},[289,293,298,305,311,315,322,326,333],{"code":60,"score":17,"supported":41,"title":290,"abstract":291,"tags":292},[65],[64,65],[30],{"code":48,"score":13,"supported":41,"title":294,"abstract":295,"tags":297},[],[28,50,296],"artificial intelligence",[125],{"code":282,"score":159,"supported":41,"title":299,"abstract":300,"tags":304},[],[301,302,303],"物联网","internet of things","IoT",[],{"code":68,"score":61,"supported":41,"title":306,"abstract":307,"tags":310},[],[308,309],"数字孪生","digital twin",[27],{"code":54,"score":61,"supported":41,"title":312,"abstract":313,"tags":314},[],[29,56],[29],{"code":316,"score":21,"supported":41,"title":317,"abstract":318,"tags":321},"A2",[],[319,320],"小麦","wheat",[],{"code":206,"score":21,"supported":41,"title":323,"abstract":324,"tags":325},[],[208,207],[],{"code":327,"score":21,"supported":41,"title":328,"abstract":329,"tags":332},"A5",[],[330,331],"马铃薯","potato",[],{"code":334,"score":76,"supported":41,"title":335,"abstract":336,"tags":338},"B2",[],[337],"葡萄",[],[340,341],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":343,"openalex_id":345,"authors":346,"venue":269,"cited_by_count":97,"oa_url":266,"card":362,"direction":367,"ingested_from":104},"W7223088921",[347,350,353,356,359],{"name":348,"orcid":349},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":351,"orcid":352},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":354,"orcid":355},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":357,"orcid":358},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":360,"orcid":361},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":363,"method":364,"finding":365,"direction":102,"opportunity":366},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":375,"source_url":372,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":377,"sources":379,"tags":381,"primary_topic":384,"secondary_topics":386,"classification":389,"search_phrases":420,"slug":423,"view_count":97,"doi":424,"paper":425,"created_at":442},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":144,"substance":119,"depth":19,"authority":118,"freshness":13,"relevant":22,"comment":378},"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[380],{"name":375,"url":372},[27,28,382,31,383],"遥感监测","冬小麦",{"code":316,"slug":320,"name":319,"path":385,"parent_code":210},"\u002Ftopic\u002Ffield-crops\u002Fwheat",[387,388],{"code":40,"slug":43,"name":133,"path":134,"parent_code":135},{"code":48,"slug":127,"name":128,"path":129,"parent_code":130},{"status":140,"locked":14,"version":35,"method":36,"evidence":390},{"candidates":391,"agricultural_evidence":41},[392,398,402,406,411,415],{"code":316,"score":393,"supported":41,"title":394,"abstract":396,"tags":397},26,[395,320],"winter wheat",[319,395,320],[383],{"code":40,"score":17,"supported":41,"title":399,"abstract":400,"tags":401},[43],[45,43],[31],{"code":48,"score":17,"supported":41,"title":403,"abstract":404,"tags":405},[50],[28,50],[28],{"code":54,"score":13,"supported":41,"title":407,"abstract":408,"tags":410},[],[29,56,409],"multispectral",[382],{"code":60,"score":21,"supported":41,"title":412,"abstract":413,"tags":414},[],[64,65],[],{"code":68,"score":416,"supported":41,"title":417,"abstract":418,"tags":419},4,[],[27],[27],[421,422],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":424,"openalex_id":426,"authors":427,"venue":375,"cited_by_count":97,"oa_url":372,"card":437,"direction":367,"ingested_from":104},"W7223156204",[428,431,433,435],{"name":429,"orcid":430},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":432,"orcid":9},"Yi Zhang",{"name":434,"orcid":9},"WEI Lei",{"name":436,"orcid":9},"Chengcai Zhang",{"tldr":438,"method":439,"finding":440,"direction":102,"opportunity":441},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":9,"content":9,"source_name":448,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":456,"secondary_topics":457,"classification":458,"search_phrases":481,"slug":484,"view_count":485,"doi":9,"paper":486,"created_at":494},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":198,"substance":197,"depth":198,"authority":118,"freshness":76,"relevant":22,"comment":451},"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[453],{"name":448,"url":446},[27,455,28,208,71,29],"产量预测",{"code":206,"slug":207,"name":208,"path":209,"parent_code":210},[],{"status":140,"locked":14,"version":35,"method":36,"evidence":459},{"candidates":460,"agricultural_evidence":41},[461,465,469,473,477],{"code":68,"score":118,"supported":41,"title":462,"abstract":463,"tags":464},[71],[71],[27,71],{"code":206,"score":20,"supported":41,"title":466,"abstract":467,"tags":468},[208],[208],[208],{"code":54,"score":233,"supported":41,"title":470,"abstract":471,"tags":472},[],[29],[455,29],{"code":48,"score":416,"supported":41,"title":474,"abstract":475,"tags":476},[],[28],[28],{"code":40,"score":76,"supported":41,"title":478,"abstract":479,"tags":480},[],[45],[],[482,483],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",2,{"doi":9,"openalex_id":9,"authors":487,"venue":9,"cited_by_count":97,"oa_url":9,"card":488,"direction":367,"ingested_from":493},[],{"tldr":489,"method":490,"finding":491,"direction":367,"opportunity":492},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":9,"source_name":501,"source_url":498,"published_at":502,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":503,"sources":505,"tags":507,"primary_topic":9,"secondary_topics":510,"classification":511,"search_phrases":541,"slug":544,"view_count":97,"doi":545,"paper":546,"created_at":567},4311,"Cross-Regional Digital Soil Mapping of OpenLandMap-Derived USDA Soil Texture Classes Using Remote Sensing and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192147","Soil is a fundamental environmental resource that supports food production and sustainable agricultural systems. Soil’s texture is a key soil property that affects its capacity to hold water, infiltration rate, fertility, erosion risk, and crop productivity. Traditionally, it is determined using hydrometer or pipette analysis. These laboratory procedures are costly and time-consuming, typically requiring approximately two working days, which limits their practicality for large-scale assessments. This paper proposes a cross-regional digital soil texture classification system based on remote sensing data and machine learning in Malaysia and Pakistan. The two countries were chosen because they represent contrasting environmental conditions: Malaysia has humid tropical conditions, and Pakistan has arid and semi-arid conditions. The OpenLandMap United States Department of Agriculture (USDA) soil texture classes served as the reference labels, and the spectral bands from Landsat, together with spectral indices and Shuttle Radar Topography Mission (SRTM) elevation, were used as predictor variables. The three ensemble models, including Random Forest (RF), XGBoost, and CatBoost, were evaluated for multi-class classification of USDA soil texture. The main Landsat-based Malaysia–Pakistan dataset produced the best results, with RF achieving the highest accuracy of 71.45%, XGBoost 71.31%, and CatBoost 67.33%. Additional experiments using 2025 Landsat 8\u002F9 and Sentinel-2 imagery achieved lower accuracies of 63.00% and 56.10%, respectively, while five-fold spatial block cross-validation produced a mean accuracy of 59.27 ± 1.84%. To strengthen dataset reliability, representative OpenLandMap-derived soil texture classes were externally validated using PCRWR sand–silt–clay fractions plotted on the USDA soil texture triangle and independent Malaysian soil resource references. The results of the feature importance analysis showed that elevation, Bare Soil Index (BSI), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) were important predictors for classifying OpenLandMap-derived USDA soil texture classes. These show that the Landsat-based spectral and terrain features, when used in conjunction with ensemble learning, could assist cross-regional soil texture classification for the USDA. However, class imbalance, spectral overlap between soil texture classes, temporal variability, and resolution mismatch between satellite predictors and soil labels remain important challenges. The findings of this study provide a useful foundation for large-scale soil texture mapping and future spatial validation across different environmental regions.","土壤是支撑粮食生产和可持续农业系统的基础环境资源。土壤质地是影响土壤持水能力、入渗速率、肥力、侵蚀风险和作物生产力的关键土壤属性。传统上，土壤质地采用比重计法或吸管法测定。这些实验室方法成本高、耗时长，通常需要约两个工作日，限制了其在大规模评估中的实用性。本文提出了一种基于遥感数据和机器学习的跨区域数字土壤质地分类系统，研究区域为马来西亚和巴基斯坦。选择这两个国家是因为它们代表了截然不同的环境条件：马来西亚属于湿润热带条件，巴基斯坦属于干旱和半干旱条件。以OpenLandMap提供的美国农业部（USDA）土壤质地类别作为参考标签，以Landsat光谱波段、光谱指数和航天飞机雷达地形测绘任务（SRTM）高程数据作为预测变量。评估了三种集成模型，包括随机森林（RF）、XGBoost和CatBoost，用于USDA土壤质地的多类分类。基于Landsat的马来西亚—巴基斯坦主数据集取得了最佳结果，RF达到最高精度71.45%，XGBoost为71.31%，CatBoost为67.33%。使用2025年Landsat 8\u002F9和Sentinel-2影像的额外实验分别取得了较低的精度，为63.00%和56.10%，而五折空间块交叉验证产生的平均精度为59.27 ± 1.84%。为增强数据集可靠性，使用PCRWR砂—粉—黏粒比例绘制在USDA土壤质地三角图上，并结合独立的马来西亚土壤资源参考资料，对具有代表性的OpenLandMap衍生土壤质地类别进行了外部验证。特征重要性分析结果表明，高程、裸土指数（BSI）、归一化差异水体指数（NDWI）和归一化差异植被指数（NDVI）是分类OpenLandMap衍生USDA土壤质地类别的重要预测因子。这些结果表明，基于Landsat的光谱和地形特征与集成学习结合使用时，可辅助USDA的跨区域土壤质地分类。然而，类别不平衡、土壤质地类别之间的光谱重叠、时间变异性以及卫星预测因子与s之间的分辨率不匹配","Agriculture","2026-10-05T00:00:00Z",{"impact":144,"substance":119,"depth":198,"authority":118,"freshness":159,"relevant":22,"comment":504},"跨区域遥感与集成学习土壤质地分类研究，方法扎实、结论有参考价值，但精度有限，属细分领域进展。",[506],{"name":501,"url":498},[508,27,28,29,509],"数字农业","土壤制图",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":512},{"candidates":513,"agricultural_evidence":41,"reason":81},[514,518,522,526,531,537],{"code":40,"score":17,"supported":41,"title":515,"abstract":516,"tags":517},[43],[45,43],[509],{"code":48,"score":17,"supported":41,"title":519,"abstract":520,"tags":521},[50],[28,50],[28],{"code":54,"score":17,"supported":41,"title":523,"abstract":524,"tags":525},[56],[29,56],[29],{"code":75,"score":76,"supported":41,"title":527,"abstract":528,"tags":530},[],[529],"干旱",[],{"code":532,"score":76,"supported":41,"title":533,"abstract":534,"tags":536},"H8",[],[535],"可持续农业",[],{"code":68,"score":485,"supported":14,"title":538,"abstract":539,"tags":540},[],[],[508,27],[542,543],"OpenLandMap USDA 土壤质地","Landsat 土壤质地 机器学习","OpenLandMapUSDA土壤质地-4311","10.3390\u002Fagriculture16192147",{"doi":545,"openalex_id":547,"authors":548,"venue":501,"cited_by_count":97,"oa_url":498,"card":562,"direction":102,"ingested_from":104},"W7220411242",[549,551,553,556,559],{"name":550,"orcid":9},"Umair Maqsood",{"name":552,"orcid":9},"Fatima Iftikhar",{"name":554,"orcid":555},"Kashif Sattar","https:\u002F\u002Forcid.org\u002F0000-0001-8620-9419",{"name":557,"orcid":558},"Iftikhar A. Ahmad","https:\u002F\u002Forcid.org\u002F0000-0003-3719-2387",{"name":560,"orcid":561},"Madini Obad Alassafi","https:\u002F\u002Forcid.org\u002F0000-0001-9919-8368",{"tldr":563,"method":564,"finding":565,"direction":102,"opportunity":566},"用Landsat光谱与地形特征结合集成学习，实现马来西亚与巴基斯坦跨区域USDA土壤质地分类。","Landsat光谱波段、光谱指数与SRTM高程，RF\u002FXGBoost\u002FCatBo","RF精度最高71.45%，高程、BSI、NDWI、NDVI为关键预测因子，跨区域可行但精度受限。","可探索多源遥感融合与类别不平衡校正，提升跨气候区土壤质地制图精度与迁移能力。","2026-10-06T23:30:20.282911Z"]