[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3796":3,"related-3796":97},{"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":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":96},3796,"Development of drought index and quantitative assessment of spatiotemporal distribution characteristics of the Loess Plateau using multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-026-13145-3","Drought significantly affects water and carbon cycles of the terrestrial ecosystems, particularly in arid and semi-arid regions. However, drought monitoring using remote sensing approaches remains limited, because remote sensing-based drought indices lack explicit physical mechanisms. To address this gap, this study developed a comprehensive drought index (CDI) by integrating the normalized difference drought index (NDDI), soil moisture monitoring index (SMMI), and temperature vegetation drought index (TVDI), while explicitly accounting for the temporal evolution and physical drivers of drought. Using Theil-Sen Median trend analysis and the Mann-Kendall test, this study quantified the spatiotemporal dynamics of drought across the Loess Plateau during 2001–2022. Combined with Hurst index analysis, this study further assessed the long-term persistence and projected future drought trends. The results showed that: (1) CDI effectively reflected regional soil drought conditions, and exhibited a significant negative correlation with in situ soil moisture measurements ( R 2 = 0.71). (2) The Loess Plateau experienced moderate drought, with a slow declining trend in dryness. (3) Spatially, drought conditions migrated seasonally; severe drought occurred predominantly in spring and summer, whereas moderate drought prevailed in autumn and winter. Drying trends dominated the northwestern and southeastern Loess Plateau, while the central region along the northeast-southwest exhibited an obvious wetting trend. (4) Hurst index suggested an overall trend toward drier conditions in the future, with larger humidifying areas in autumn and winter than in spring and summer. This study advances drought monitoring methodology and provides data support for drought monitoring and early warning in the Loess Plateau.","干旱显著影响陆地生态系统的水循环和碳循环，在干旱和半干旱地区尤为突出。然而，基于遥感方法的干旱监测仍存在局限，因为基于遥感的干旱指数缺乏明确的物理机制。为弥补这一不足，本研究通过整合归一化差异干旱指数(NDDI)、土壤水分监测指数(SMMI)和温度植被干旱指数(TVDI)，并明确考虑干旱的时间演变和物理驱动因素，构建了综合干旱指数(CDI)。本研究采用Theil-Sen Median趋势分析和Mann-Kendall检验，量化了2001—2022年黄土高原干旱的时空动态。结合Hurst指数分析，本研究进一步评估了干旱的长期持续性并预测了未来干旱趋势。结果表明：(1) CDI能够有效反映区域土壤干旱状况，与原位土壤水分观测值呈显著负相关(R²=0.71)。(2) 黄土高原整体处于中度干旱状态，干旱程度呈缓慢下降趋势。(3) 空间上，干旱状况随季节迁移；重度干旱主要发生在春季和夏季，而中度干旱则多见于秋季和冬季。黄土高原西北部和东南部以干旱化趋势为主，而东北—西南走向的中部地区则表现出明显的湿润化趋势。(4) Hurst指数表明，未来整体趋向于更干旱的状况，且秋冬季湿润化面积大于春夏季。本研究推进了干旱监测方法学，并为黄土高原干旱监测与预警提供了数据支撑。",null,"Environmental Earth Sciences","2026-09-28T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,13,9,1,"多源遥感构建综合干旱指数并量化黄土高原2001—2022年干旱时空演变，方法有创新、数据扎实，对旱作区干旱预警有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业气象","遥感监测","土壤墒情","黄土高原","干旱指数",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":63},[39,47,55],{"code":40,"score":17,"supported":41,"title":42,"abstract":44,"tags":46},"H7",true,[43],"drought",[45,43],"干旱",[27,31],{"code":48,"score":49,"supported":41,"title":50,"abstract":52,"tags":54},"I3",15,[51],"remote sensing",[53,51],"遥感",[28],{"code":56,"score":57,"supported":41,"title":58,"abstract":59,"tags":62},"H2",7,[],[60,61],"土壤","soil",[29],"农业相关性需要复核",[65,66],"黄土高原 干旱指数 遥感","综合干旱指数 CDI 土壤湿度","黄土高原干旱指数遥感-3796",3,"10.1007\u002Fs12665-026-13145-3",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7214669937",[73,76,78,80,83,85],{"name":74,"orcid":75},"Jing Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-1762-6179",{"name":77,"orcid":9},"Wanyun Huang",{"name":79,"orcid":9},"Kelin Wang",{"name":81,"orcid":82},"Pingda Lu","https:\u002F\u002Forcid.org\u002F0009-0006-8079-6038",{"name":84,"orcid":9},"Mengyun Liu",{"name":86,"orcid":9},"Xiaoping Wang",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12665-026-13145-3.pdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"构建综合干旱指数CDI，评估2001-2022年黄土高原干旱时空动态与未来趋势。","融合NDDI、SMMI、TVDI构建CDI，用Theil-Sen、M-K检验和H","CDI与实测土壤水分显著相关，黄土高原整体中度干旱且缓慢变干，未来总体趋干。","农业遥感与作物表型","可引入物理机制与多源数据融合，提升干旱指数可解释性并拓展至其他半干旱区。","openalex","2026-09-29T23:30:30.259671Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,181,273,331,408,476],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":123,"classification":134,"search_phrases":161,"slug":164,"view_count":22,"doi":165,"paper":166,"created_at":180},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing","2026-10-08T00:00:00Z",75,{"impact":110,"substance":18,"depth":19,"authority":111,"freshness":21,"relevant":22,"comment":112},12,14,"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[114],{"name":106,"url":103},[116,27,28,117,31],"粮食安全","西非农业",{"code":40,"slug":119,"name":120,"path":121,"parent_code":122},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[124,129],{"code":48,"slug":125,"name":126,"path":127,"parent_code":128},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":130,"slug":131,"name":132,"path":133,"parent_code":122},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":135,"locked":14,"version":35,"method":36,"evidence":136},"classified",{"candidates":137,"agricultural_evidence":41},[138,142,146,152,156],{"code":40,"score":17,"supported":41,"title":139,"abstract":140,"tags":141},[43],[45,43],[27,31],{"code":48,"score":49,"supported":41,"title":143,"abstract":144,"tags":145},[51],[53,51],[28],{"code":130,"score":111,"supported":41,"title":147,"abstract":149,"tags":151},[148],"evapotranspiration",[150,148],"水资源",[],{"code":56,"score":98,"supported":41,"title":153,"abstract":154,"tags":155},[],[60,61],[],{"code":157,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},"A8",[],[],[116],[162,163],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":165,"openalex_id":167,"authors":168,"venue":106,"cited_by_count":87,"oa_url":103,"card":175,"direction":93,"ingested_from":95},"W7220963372",[169,172],{"name":170,"orcid":171},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":173,"orcid":174},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":176,"method":177,"finding":178,"direction":93,"opportunity":179},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":206,"classification":215,"search_phrases":249,"slug":252,"view_count":87,"doi":253,"paper":254,"created_at":272},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","2026-10-21T00:00:00Z",77,{"impact":17,"substance":191,"depth":192,"authority":20,"freshness":13,"relevant":22,"comment":193},21,17,"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。",[195],{"name":187,"url":184},[197,198,28,29,199],"智慧农业","机器学习","冬小麦",{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[207,210],{"code":56,"slug":61,"name":208,"path":209,"parent_code":122},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":211,"slug":212,"name":213,"path":214,"parent_code":128},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":135,"locked":14,"version":35,"method":36,"evidence":216},{"candidates":217,"agricultural_evidence":41},[218,224,228,233,238,243],{"code":201,"score":219,"supported":41,"title":220,"abstract":222,"tags":223},26,[221,202],"winter wheat",[203,221,202],[199],{"code":56,"score":49,"supported":41,"title":225,"abstract":226,"tags":227},[61],[60,61],[29],{"code":211,"score":49,"supported":41,"title":229,"abstract":231,"tags":232},[230],"machine learning",[198,230],[198],{"code":48,"score":13,"supported":41,"title":234,"abstract":235,"tags":237},[],[53,51,236],"multispectral",[28],{"code":130,"score":98,"supported":41,"title":239,"abstract":240,"tags":242},[],[241,131],"灌溉",[],{"code":244,"score":245,"supported":41,"title":246,"abstract":247,"tags":248},"I1",4,[],[197],[197],[250,251],"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":253,"openalex_id":255,"authors":256,"venue":187,"cited_by_count":87,"oa_url":184,"card":266,"direction":271,"ingested_from":95},"W7223156204",[257,260,262,264],{"name":258,"orcid":259},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":261,"orcid":9},"Yi Zhang",{"name":263,"orcid":9},"WEI Lei",{"name":265,"orcid":9},"Chengcai Zhang",{"tldr":267,"method":268,"finding":269,"direction":93,"opportunity":270},"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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":9,"source_name":279,"source_url":276,"published_at":280,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":281,"score_detail":282,"sources":286,"tags":288,"primary_topic":290,"secondary_topics":291,"classification":292,"search_phrases":312,"slug":315,"view_count":22,"doi":316,"paper":317,"created_at":330},5087,"Method for ten-day assessment of the contribution of agrometeorological conditions to expected winter grain crop productivity based on satellite and ground-based information","https:\u002F\u002Fdoi.org\u002F10.37162\u002F2618-9631-2026-3-167-183","The paper presents a developed and tested method for 10-day assessment of the contribution of agrometeorological factors to winter grain crop productivity. The method is based on an integrated analysis of satellite spectral indices and ground-based meteorological data for the North Caucasus, Volga and Central Black Earth regions. Satellite data from MODIS (Terra satellite) and VIIRS (SUOMI NPP satellite) radiometers were used to calculate the NDVI, VCI, and VCNI vegetation indices. Ground-based information includes meteorological and agrometeorological indicators reflecting temperature and humidity conditions. The algorithm and software modules developed within the study allow for automated tracking of 10-day changes in agrometeorological conditions and quantitative ranking of factors based on their contribution to the expected winter grain crop yield, providing an information basis for informed planning of agricultural measures.","本文提出并验证了一种用于每旬评估农业气象因素对冬粮作物生产力贡献的方法。该方法基于对北高加索、伏尔加河沿岸和中央黑土区卫星光谱指数与地面气象数据的综合分析。利用MODIS（Terra卫星）和VIIRS（SUOMI NPP卫星）辐射计的卫星数据计算NDVI、VCI和VCNI植被指数。地面信息包括反映温湿状况的气象和农业气象指标。研究开发的算法和软件模块能够自动跟踪农业气象条件的每旬变化，并根据各因素对预期冬粮作物产量的贡献进行定量排序，为农业措施的合理规划提供信息依据。","Hydrometeorological research and forecasting","2026-10-15T00:00:00Z",70,{"impact":110,"substance":283,"depth":192,"authority":20,"freshness":284,"relevant":22,"comment":285},20,8,"方法新颖、数据扎实的区域性遥感估产研究，对农业信息化有参考价值，但属细分领域学术成果，公共影响有限。",[287],{"name":279,"url":276},[197,27,28,199,289],"作物估产",{"code":40,"slug":119,"name":120,"path":121,"parent_code":122},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":293},{"candidates":294,"agricultural_evidence":41},[295,299,304,308],{"code":40,"score":245,"supported":41,"title":296,"abstract":297,"tags":298},[],[27],[27],{"code":48,"score":300,"supported":14,"title":301,"abstract":302,"tags":303},2,[],[],[289,28],{"code":201,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[199],{"code":244,"score":22,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[197],[313,314],"MODIS VIIRS 冬小麦 遥感估产","农业气象 冬谷类作物 产量评估","MODISVIIRS冬小麦遥感估产-5087","10.37162\u002F2618-9631-2026-3-167-183",{"doi":316,"openalex_id":318,"authors":319,"venue":279,"cited_by_count":87,"oa_url":276,"card":325,"direction":93,"ingested_from":95},"W7221991562",[320,323],{"name":321,"orcid":322},"A. D. Kleshchenko","https:\u002F\u002Forcid.org\u002F0000-0002-4200-6668",{"name":324,"orcid":9},"O. V. Savitskaya",{"tldr":326,"method":327,"finding":328,"direction":93,"opportunity":329},"提出一种基于卫星与地面数据每十天评估农业气象因子对冬粮作物产量贡献的方法。","用MODIS和VIIRS计算NDVI、VCI、VCNI，结合气象与农业气象指标做","可自动追踪十天农业气象变化并量化排序各因子对预期产量的贡献。","可扩展至多作物多区域，融合机器学习提升因子贡献动态预测精度。","2026-10-16T23:30:26.817010Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":339,"sources":341,"tags":343,"primary_topic":9,"secondary_topics":347,"classification":348,"search_phrases":364,"slug":367,"view_count":87,"doi":368,"paper":369,"created_at":407},4829,"Snowpack Temperature Profiler: A Novel Approach to Measuring Snow Temperature and Cold Content During Snow Surveys","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fauthorea.15010208\u002Fv1","Snowpack temperature and cold content control the timing of snowmelt, yet they remain among the least validated variables in operational snow water supply forecasting models. Unlike snow water equivalent, which is supported by extensive observational networks, snowpack temperature has been difficult to measure at scale. Traditional snow pits are labor-intensive and time-consuming, and continuous in-situ attempts have faced persistent challenges that have largely confined them to research settings. Here we introduce a novel approach. The Snowpack Temperature Profiler (STP) is a compact, low-power infrared temperature sensor designed to be lowered into the hole left by a Federal snow sampler, which enables quick snowpack temperature measurement without excavating a pit. We validated the STP against 62 co-located snow pit temperature profiles across maritime, continental, and intermountain snow climates over three winters (2023–2026). Average snowpack temperature measurements from the STP were highly correlated with snow pit observations (R2 of 0.91) and had low bias (-0.01 ý C), with similar results for cold content values derived from STP and snow pit observations (R2 = 0.92; -0.02 MJ m*2). These results show an upper bound on STP uncertainty rather than a direct measurement of it, as the reference sensor used for validation had an absolute accuracy of ,0.5 ý C. The STP offers a scalable method for collecting snowpack temperature that, if leveraged at snow courses across the United States and Canada, has the potential to collect 3,500 snow temperature profiles annually. Incorporating this observational data into existing operational models to constrain cold content values has the potential to improve snowmelt timing and runoff forecasts, and would additionally provide an important estimate of the potential for the presence of liquid water, which is critical for radar remote sensing retrievals.","雪包温度与冷含量控制着融雪发生的时间，但它们仍是业务化雪水供应预报模型中验证最不充分的变量之一。与拥有广泛观测网络支持的雪水当量不同，雪包温度一直难以在大尺度上测量。传统雪坑方法劳动强度大且耗时，而连续原位测量尝试则面临长期存在的挑战，使其大多局限于研究环境。在此，我们介绍一种新方法。雪包温度剖面仪（Snowpack Temperature Profiler, STP）是一种紧凑、低功耗的红外温度传感器，设计用于放入联邦雪采样器留下的孔洞中，从而无需挖掘雪坑即可快速测量雪包温度。我们在三个冬季（2023—2026年）中，针对海洋性、大陆性和山间雪气候区的62个同址雪坑温度剖面，对STP进行了验证。STP测得的平均雪包温度与雪坑观测高度相关（R²为0.91），偏差较低（-0.01 °C），由STP和雪坑观测得出的冷含量值结果相似（R² = 0.92；-0.02 MJ m⁻²）。这些结果反映的是STP不确定性的上界，而非对其不确定性的直接测量，因为用于验证的参考传感器绝对精度为±0.5 °C。STP提供了一种可扩展的雪包温度采集方法，若在美国和加拿大的各雪道加以利用，有潜力每年采集3500个雪温剖面。将该观测数据纳入现有业务化模型以约束冷含量值，有潜力改进融雪时间和径流预报，并且还将提供对液态水存在可能性的重要估计，这对雷达遥感反演至关重要。","OpenAlex","2026-10-11T00:00:00Z",{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":57,"relevant":22,"comment":340},"提出可随雪样器快速测量雪温剖面的红外传感器方案，验证数据扎实，对融雪径流预报与雷达遥感有实用价值。",[342],{"name":337,"url":334},[27,344,28,345,346],"传感器","水文预报","积雪温度",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":349},{"candidates":350,"agricultural_evidence":14,"reason":63},[351,355,359],{"code":48,"score":57,"supported":41,"title":352,"abstract":353,"tags":354},[],[53,51],[28],{"code":40,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[27],{"code":360,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},"I5",[],[],[344],[365,366],"积雪温度 冷含量 测量","农业气象 水文预报 积雪温度 遥感监测","积雪温度冷含量测量-4829","10.22541\u002Fauthorea.15010208\u002Fv1",{"doi":368,"openalex_id":370,"authors":371,"venue":9,"cited_by_count":87,"oa_url":401,"card":402,"direction":93,"ingested_from":95},"W7221461714",[372,375,377,379,381,383,385,388,390,392,395,397,399],{"name":373,"orcid":374},"Anne Heggli","https:\u002F\u002Forcid.org\u002F0000-0002-3207-6953",{"name":376,"orcid":9},"Bjoern Bingham",{"name":378,"orcid":9},"Daniel McEvoy",{"name":380,"orcid":9},"Rich Moran",{"name":382,"orcid":9},"Lucas Zukiewicz",{"name":384,"orcid":9},"Daniel McGrath",{"name":386,"orcid":387},"Wyatt Reis","https:\u002F\u002Forcid.org\u002F0000-0002-2801-5357",{"name":389,"orcid":9},"Rosemary Carroll",{"name":391,"orcid":9},"H.P. Marshall",{"name":393,"orcid":394},"Christine M. Albano","https:\u002F\u002Forcid.org\u002F0000-0003-1610-6961",{"name":396,"orcid":9},"Joseph Ammatelli",{"name":398,"orcid":9},"Andrew Schwartz",{"name":400,"orcid":9},"D. Gochis","https:\u002F\u002Fwww.authorea.com\u002Fdoi\u002Fpdf\u002F10.22541\u002Fauthorea.15010208\u002Fv1",{"tldr":403,"method":404,"finding":405,"direction":93,"opportunity":406},"提出雪包温度剖面仪，用红外传感器快速测量雪温与冷含量，验证精度高。","红外温度传感器降入雪采样孔，对比62个雪坑剖面，三个冬季多气候验证。","STP与雪坑温度高度相关（R²=0.91），冷含量R²=0.92，可规模化采集。","将雪温观测同化进水文模型以改进融雪径流预报，并支撑雷达遥感液态水反演。","2026-10-13T23:30:25.274222Z",{"id":409,"title":410,"url":411,"summary":412,"summary_zh":413,"content":9,"source_name":414,"source_url":411,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":424,"secondary_topics":425,"classification":426,"search_phrases":457,"slug":460,"view_count":22,"doi":461,"paper":462,"created_at":475},4506,"Spatial assessment of soil degradation under intensive agricultural pressure in the central loess plateau","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1940953","Soil degradation (SD) threatens the sustainability of semi-arid agricultural systems in the Central Loess Plateau, where intensive cultivation and repeated land-use cycles accelerate soil deterioration. This study developed a spatially explicit and uncertainty-aware framework for assessing and mapping SD by integrating soil quality indicators (SQIs), environmental data, remote sensing, and Random Forest (RF) modelling. A total of 350 surface-soil samples were collected and analyzed. Principal component analysis and the minimum data set (MDS) approach were used to identify representative soil-quality indicators. The final MDS included clay, soil bulk density, pH, soil organic carbon, calcium carbonate equivalent, cation exchange capacity, potassium, and sodium adsorption ratio. Among these indicators, pH, SAR, CEC, SOC, K, and clay received the greatest weights, highlighting the importance of soil chemical conditions, sodicity, nutrient retention, organic matter, and texture in soil quality assessment. Soil degradation was quantified by comparing SQI values of agricultural soils with reference values from natural ecosystems. Environmental predictors were then incorporated into an RF model to estimate the spatial distribution of SD and assess predictor importance. The model showed good predictive performance, with R 2 values of 0.83 for training and 0.76 for validation, and RMSE values ranging from 0.031 to 0.034. Severe degradation was mainly concentrated in the central and southwestern regions, reflecting the cumulative effects of intensive cultivation, nutrient depletion, and SOC loss. Prediction-interval analysis identified areas with greater uncertainty and priority for additional sampling. The proposed framework provides a practical basis for spatially targeted soil conservation, sustainable agricultural management, and long-term ecosystem resilience. Future research should incorporate biological indicators and temporal monitoring to improve SD prediction under changing climatic and land-use conditions.","土壤退化（SD）威胁着黄土高原中部半干旱农业系统的可持续性，该地区集约化耕作和反复的土地利用周期加速了土壤恶化。本研究通过整合土壤质量指标（SQIs）、环境数据、遥感技术和随机森林（RF）建模，开发了一个空间明确且具有不确定性感知的土壤退化评估与制图框架。共采集并分析了350个表层土壤样品。采用主成分分析和最小数据集（MDS）方法识别代表性土壤质量指标。最终的最小数据集包括黏粒、土壤容重、pH、土壤有机碳、碳酸钙当量、阳离子交换量、钾和钠吸附比。在这些指标中，pH、SAR、CEC、SOC、K和黏粒获得了最大的权重，凸显了土壤化学条件、钠质化、养分保持、有机质和质地在土壤质量评估中的重要性。通过比较农业土壤的SQI值与自然生态系统的参考值，对土壤退化进行了量化。随后将环境预测因子纳入随机森林模型，以估算土壤退化的空间分布并评估预测因子的重要性。该模型表现出良好的预测性能，训练集R²值为0.83，验证集R²值为0.76，RMSE值在0.031至0.034之间。严重退化主要集中在中部和西南部地区，反映了集约化耕作、养分耗竭和有机碳损失的累积效应。预测区间分析识别了不确定性较大且需要优先补充采样的区域。所提出的框架为空间针对性的土壤保护、可持续农业管理和长期生态系统恢复力提供了实践基础。未来研究应纳入生物指标和时间监测，以改进气候变化和土地利用变化条件下的土壤退化预测。","Frontiers in Environmental Science",76,{"impact":49,"substance":18,"depth":19,"authority":20,"freshness":284,"relevant":22,"comment":417},"基于350个样点与随机森林模型的空间化土壤退化评估，方法扎实、结论可靠，对黄土高原耕地保护有参考价值，但属区域学术研究，公共影响有限。",[419],{"name":414,"url":411},[421,422,28,423,30],"随机森林","耕地保护","土壤退化",{"code":56,"slug":61,"name":208,"path":209,"parent_code":122},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":427},{"candidates":428,"agricultural_evidence":41},[429,433,437,442,448,453],{"code":56,"score":111,"supported":41,"title":430,"abstract":431,"tags":432},[61],[60,61],[],{"code":48,"score":57,"supported":41,"title":434,"abstract":435,"tags":436},[],[53,51],[28],{"code":40,"score":98,"supported":41,"title":438,"abstract":439,"tags":441},[],[440,45],"气候变化",[],{"code":443,"score":68,"supported":41,"title":444,"abstract":445,"tags":447},"H8",[],[446],"可持续农业",[],{"code":449,"score":22,"supported":14,"title":450,"abstract":451,"tags":452},"H1",[],[],[422],{"code":211,"score":22,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[421],[458,459],"黄土高原 土壤退化 遥感","土壤质量 随机森林 评估","黄土高原土壤退化遥感-4506","10.3389\u002Ffenvs.2026.1940953",{"doi":461,"openalex_id":463,"authors":464,"venue":414,"cited_by_count":87,"oa_url":411,"card":470,"direction":93,"ingested_from":95},"W7221006056",[465,467],{"name":466,"orcid":9},"Xiandong Hou",{"name":468,"orcid":469},"Tao Bai","https:\u002F\u002Forcid.org\u002F0000-0002-2034-8026",{"tldr":471,"method":472,"finding":473,"direction":93,"opportunity":474},"构建融合土壤指标、遥感与随机森林的空间显式框架，评估黄土高原中部高强度农业下的土壤退化。","350个表层土样，PCA与最小数据集筛选指标，随机森林建模并做预测区间分析。","pH、SAR、CEC、SOC、钾和黏粒权重最高；严重退化集中于中部和西南部。","可引入生物指标与多时相遥感监测，提升气候变化和土地利用变化下土壤退化预测的时空精度。","2026-10-09T23:30:25.996370Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":9,"source_name":482,"source_url":479,"published_at":483,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":484,"score_detail":485,"sources":487,"tags":489,"primary_topic":491,"secondary_topics":492,"classification":493,"search_phrases":512,"slug":515,"view_count":245,"doi":516,"paper":517,"created_at":539},4360,"Assessment of bias correction techniques for satellite soil moisture data assimilation in the dry season of Southeastern South America","https:\u002F\u002Fdoi.org\u002F10.1175\u002Fjhm-d-26-0062.1","Abstract Estimation of surface soil moisture (SSM) is fundamental for hydrological monitoring and agricultural management, particularly in drought-prone regions such as central–northeastern Argentina. Data assimilation provides a robust approach for estimating SSM by integrating land surface models with remote sensing data. Satellite missions such as SMAP and SMOS deliver global SSM products; however, these observations often exhibit systematic biases arising from instrument noise, indirect measurements, and temporal and spatial heterogeneities. Since standard data assimilation techniques assume unbiased observations, bias correction is required to ensure optimal assimilation. This work evaluates off-line observation bias-correction methods within a land data assimilation framework based on the Noah-MP land surface model and an ensemble Kalman filter, focusing on the 2022 dry season in the endorheic Pampas region. During dry conditions, SSM probability density functions differ significantly among soil textures within the bin. To address sampling errors in cumulative distribution function (CDF) matching, we propose an approach in which climatological statistics are computed using homogeneous soil texture pixels, and a 45-day moving temporal window is applied to smooth CDF evolution. The proposed method is compared with standard CDF matching and normal deviate scaling. Results indicate that improved statistical sampling in CDF matching produces a measurable impact on SSM estimates, particularly during dry periods. It shows better agreement with independent in-situ soil measurements than standard CDF matching, provided that the soil texture classifications at the in-situ site and in the model grid cell are consistent.","摘要 地表土壤水分（SSM）估算对于水文监测和农业管理至关重要，尤其是在阿根廷中东北部等干旱易发地区。数据同化通过将陆面模型与遥感数据相结合，为估算SSM提供了一种稳健的方法。SMAP和SMOS等卫星任务可提供全球SSM产品；然而，这些观测往往因仪器噪声、间接测量以及时空异质性而存在系统性偏差。由于标准数据同化技术假设观测无偏，因此需要进行偏差校正以确保最优同化。本研究在基于Noah-MP陆面模型和集合卡尔曼滤波的陆面数据同化框架内，评估了离线观测偏差校正方法，重点关注内流潘帕斯地区2022年旱季。在干旱条件下，同一分箱内不同土壤质地的SSM概率密度函数存在显著差异。为解决累积分布函数（CDF）匹配中的采样误差问题，我们提出了一种方法：利用均质土壤质地像元计算气候统计量，并应用45天滑动时间窗口以平滑CDF演变。将所提方法与标准CDF匹配和正态偏差缩放进行了比较。结果表明，CDF匹配中改进的统计采样对SSM估算产生了可测量的影响，尤其是在干旱时期。与原位站点和模型网格单元土壤质地分类一致的前提下，该方法与原位土壤实测数据的吻合程度优于标准CDF匹配。","Journal of Hydrometeorology","2026-10-06T00:00:00Z",69,{"impact":284,"substance":191,"depth":192,"authority":111,"freshness":21,"relevant":22,"comment":486},"面向干旱区卫星土壤水分同化的偏差校正方法研究，方法有新意但属区域尺度学术成果，对国内农业遥感墒情监测有参考价值。",[488],{"name":482,"url":479},[53,27,29,490],"数据同化",{"code":56,"slug":61,"name":208,"path":209,"parent_code":122},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":494},{"candidates":495,"agricultural_evidence":41},[496,500,504,508],{"code":56,"score":49,"supported":41,"title":497,"abstract":498,"tags":499},[61],[60,61],[29],{"code":40,"score":57,"supported":41,"title":501,"abstract":502,"tags":503},[],[45,43],[27],{"code":48,"score":57,"supported":41,"title":505,"abstract":506,"tags":507},[],[53,51],[53],{"code":360,"score":22,"supported":14,"title":509,"abstract":510,"tags":511},[],[],[490],[513,514],"SMAP SMOS 土壤水分 同化","南美 潘帕斯 土壤湿度 偏差校正","SMAPSMOS土壤水分同化-4360","10.1175\u002Fjhm-d-26-0062.1",{"doi":516,"openalex_id":518,"authors":519,"venue":482,"cited_by_count":87,"oa_url":9,"card":534,"direction":93,"ingested_from":95},"W7220742830",[520,523,525,528,531],{"name":521,"orcid":522},"Fabricio Matías Obregón","https:\u002F\u002Forcid.org\u002F0009-0005-9861-1356",{"name":524,"orcid":9},"Lucini Magdalena",{"name":526,"orcid":527},"Omar Vicente Müller","https:\u002F\u002Forcid.org\u002F0000-0001-9943-8368",{"name":529,"orcid":530},"Romina Carla Ruscica","https:\u002F\u002Forcid.org\u002F0000-0003-0127-9579",{"name":532,"orcid":533},"Manuel Pulido","https:\u002F\u002Forcid.org\u002F0000-0003-3992-8044",{"tldr":535,"method":536,"finding":537,"direction":93,"opportunity":538},"评估南美干旱季卫星土壤湿度同化中的偏差校正方法，提出基于均质土壤纹理和滑动窗口的改进CDF匹配。","Noah-MP模型与集合卡尔曼滤波同化框架，对比标准CDF匹配和正态偏差缩放。","改进CDF匹配在干旱期对土壤湿度估计更准确，与实测数据吻合更好，但需土壤纹理分类一致。","可探索不同气候区与土壤类型下偏差校正的普适性，并耦合作物模型提升干旱监测能力。","2026-10-07T23:30:26.212164Z"]