Works matching DE "SENTINEL-1 (Artificial satellite)"
Results: 2330
A study of subsidence hotspots by mapping vulnerability indices through innovatory 'ALPRIFT' using artificial intelligence at two levels.
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- Bulletin of Engineering Geology & the Environment, 2020, v. 79, n. 8, p. 3989, doi. 10.1007/s10064-020-01781-3
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Comparing Forest Understory Fuel Classification in Portugal Using Discrete Airborne Laser Scanning Data and Satellite Multi-Source Remote Sensing Data.
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- Fire (2571-6255), 2023, v. 6, n. 9, p. 327, doi. 10.3390/fire6090327
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Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data.
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- Fire (2571-6255), 2023, v. 6, n. 2, p. 72, doi. 10.3390/fire6020072
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Wetland Fire Assessment and Monitoring in the Paraná River Delta, Using Radar and Optical Data for Burnt Area Mapping.
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- Fire (2571-6255), 2022, v. 5, n. 6, p. 190, doi. 10.3390/fire5060190
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Burn Severity Assessment Using Sentinel-1 SAR in the Southeast Peruvian Amazon, a Case Study of Madre de Dios.
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- Fire (2571-6255), 2022, v. 5, n. 4, p. 94, doi. 10.3390/fire5040094
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Mapping Paddy Rice Fields Using Landsat and Sentinel Radar Images in Urban Areas for Agriculture Planning.
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- Journal of The Planner & Development, 2020, n. 41, p. 1
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Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia.
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- Land Degradation & Development, 2024, v. 35, n. 9, p. 2982, doi. 10.1002/ldr.5109
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Monitoring soil organic carbon under coastal restoration using time series Sentinel-1.
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- Land Degradation & Development, 2024, v. 35, n. 8, p. 2913, doi. 10.1002/ldr.5105
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Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models.
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- Land Degradation & Development, 2023, v. 34, n. 3, p. 911, doi. 10.1002/ldr.4505
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Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique.
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- Land Degradation & Development, 2021, v. 32, n. 16, p. 4779, doi. 10.1002/ldr.4061
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A simplified subsurface soil salinity estimation using synergy of SENTINEL‐1 SAR and SENTINEL‐2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India.
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- Land Degradation & Development, 2021, v. 32, n. 14, p. 3905, doi. 10.1002/ldr.4009
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Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems.
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- Agroforestry Systems, 2023, v. 97, n. 1, p. 109, doi. 10.1007/s10457-022-00791-2
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Dynamics of a giant slow landslide complex along the coast of the Aral Sea, Central Asia.
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- Turkish Journal of Earth Sciences, 2023, v. 32, n. 6, p. 819, doi. 10.55730/1300-0985.1876
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Coseismic surface deformation and fault model of the 27 May 2017 Mw 5.2 Saruhanlı- Manisa (western Turkey) earthquake from InSAR.
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- Turkish Journal of Earth Sciences, 2020, v. 29, n. 3, p. 538, doi. 10.3906/yer-1912-7
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Combining multisensor images and social network data to assess the area flooded by a hurricane event.
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- PeerJ, 2024, p. 1, doi. 10.7717/peerj.17319
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Radar remote sensing-based inversion model of soil salt content at different depths under vegetation.
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- PeerJ, 2022, p. 1, doi. 10.7717/peerj.13306
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Investigating the potential use of Sentinel-1 data for monitoring wetland water level changes in China's Momoge National Nature Reserve.
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- PeerJ, 2020, p. 1, doi. 10.7717/peerj.8616
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Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data.
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- PeerJ, 2018, p. 1, doi. 10.7717/peerj.5431
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Anthropogenic problems threatening major cities: Largest surface deformations observed in Hatay, Türkiye based on SBAS-InSAR.
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- Bulletin of the Mineral Research & Exploration, 2024, v. 173, n. 173, p. 235, doi. 10.19111/bulletinofmre.1298494
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National-scale 10-m maps of cropland use intensity in China during 2018–2023.
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- Scientific Data, 2024, v. 11, n. 1, p. 1, doi. 10.1038/s41597-024-03456-0
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基于Sentinel-1 SAR影像的南黄海浒苔提取与动态监测.
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- Journal of Remote Sensing, 2024, v. 28, n. 8, p. 2030, doi. 10.11834/jrs.20242200
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基于升降轨Sentinel-1影像的格陵兰后向散射系数入射角归一化方法.
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- Journal of Remote Sensing, 2023, v. 27, n. 9, p. 2072, doi. 10.11834/jrs.20211243
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基于随机森林算法和知识规则的国际湿地城市精细湿地分类——以常德市为例
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- Journal of Remote Sensing, 2023, v. 27, n. 6, p. 1426, doi. 10.11834/jrs.20232293
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多源多特征集成的南美洲典型地区湿地制图.
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- Journal of Remote Sensing, 2023, v. 27, n. 6, p. 1300, doi. 10.11834/jrs.20232265
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哨兵1号和高分三号SAR数据海浪谱反演精度评估.
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- Journal of Remote Sensing, 2023, v. 27, n. 4, p. 891, doi. 10.11834/jrs.20221503
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北极海域SAR海浪方向谱反演及其与中法海洋卫星 CFOSAT/SWIM数据的比较.
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- Journal of Remote Sensing, 2023, v. 27, n. 4, p. 881, doi. 10.11834/jrs.20211210
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联合GEE 与多源遥感数据的黑龙江流域沼泽湿地 信息提取.
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- Journal of Remote Sensing, 2022, v. 26, n. 2, p. 386, doi. 10.11834/jrs.20200033
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时间序列SAR图像不可移动文物水域淹没监测.
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- Journal of Remote Sensing, 2021, v. 25, n. 12, p. 2431, doi. 10.11834/jrs.20211146
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Sentinel-1和SBAS-InSAR分析钻井水溶岩盐矿山时序沉降.
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- Journal of Remote Sensing, 2019, v. 23, n. 3, p. 501, doi. 10.11834/jrs.20198292
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SAR 图像舰船目标检测数据集构建研究综述.
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- Telecommunication Engineering, 2021, v. 61, n. 11, p. 1451, doi. 10.3969/j.issn.1001-893x.2021.11.019
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Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia.
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- Journal of Engineering & Technological Sciences, 2023, v. 55, n. 1, p. 98, doi. 10.5614/j.eng.technol.sci.2023.55.1.10
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Effective Detection of Potential Oil Pollution in the Egyptian Maritime Routes of the Mediterranean Sea Using SAR Data.
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- Egyptian Journal of Aquatic Biology & Fisheries, 2023, v. 27, n. 4, p. 729, doi. 10.21608/ejabf.2023.312184
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Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy.
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- Journal of Agricultural Engineering (1974-7071), 2021, v. 52, n. 4, p. 1, doi. 10.4081/jae.2021.1265
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Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data.
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- TEM Journal, 2023, v. 12, n. 2, p. 1030, doi. 10.18421/TEM122-49
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Pamuk Bitkisi Üretim Alanı Ortam Nem ve Sıcaklık Değerlerinin, SAR ve Optik Uydu Görüntüleri ile Tahmin Edebilirliğinin Araştırılması.
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- Journal of the Institute of Science & Technology / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2023, v. 13, n. 3, p. 2217, doi. 10.21597/jist.1265099
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Delimitation of low topsoil moisture content areas in a vineyard using remote sensing imagery (Sentinel-1 and Sentinel-2) in a Mediterranean-climate region.
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- Soil & Water Research, 2021, v. 16, n. 2, p. 85, doi. 10.17221/101/2019-SWR
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Oil leaking and seeping site in the Persian Gulf detected and studied by satellite observations.
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- Marine Georesources & Geotechnology, 2021, v. 39, n. 12, p. 1481, doi. 10.1080/1064119X.2020.1853286
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Seasonal and transient surface displacements in the Kumamoto area, Japan, associated with the 2016 Kumamoto earthquake: implications for seismic-induced groundwater level change.
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- Earth, Planets & Space, 2020, v. 72, n. 1, p. N.PAG, doi. 10.1186/s40623-020-01275-2
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Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches.
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- Forests (19994907), 2024, v. 15, n. 6, p. 1055, doi. 10.3390/f15061055
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Monitoring Forest Dynamics and Conducting Restoration Assessment Using Multi-Source Earth Observation Data in Northern Andes, Colombia.
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- Forests (19994907), 2024, v. 15, n. 5, p. 754, doi. 10.3390/f15050754
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Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China.
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- Forests (19994907), 2024, v. 15, n. 2, p. 288, doi. 10.3390/f15020288
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Collaborative Utilization of Sentinel-1/2 and DEM Data for Mapping the Soil Organic Carbon in Forested Areas Based on the Random Forest.
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- Forests (19994907), 2024, v. 15, n. 1, p. 218, doi. 10.3390/f15010218
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Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI.
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- Forests (19994907), 2024, v. 15, n. 1, p. 215, doi. 10.3390/f15010215
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Coniferous Forests Aboveground Biomass Inversion in Typical Regions of China with Joint Sentinel-1 and Sentinel-2 Remote Sensing Data Supported by Different Feature Optimizing Algorithms.
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- Forests (19994907), 2024, v. 15, n. 1, p. 56, doi. 10.3390/f15010056
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Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data.
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- Forests (19994907), 2023, v. 14, n. 11, p. 2224, doi. 10.3390/f14112224
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Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data.
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- Forests (19994907), 2023, v. 14, n. 9, p. 1864, doi. 10.3390/f14091864
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Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network.
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- Forests (19994907), 2023, v. 14, n. 9, p. 1823, doi. 10.3390/f14091823
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Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects.
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- Forests (19994907), 2023, v. 14, n. 9, p. 1747, doi. 10.3390/f14091747
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Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season.
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- Forests (19994907), 2023, v. 14, n. 9, p. 1700, doi. 10.3390/f14091700
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Synergistic Use of Sentinel-1 and Sentinel-2 Based on Different Preprocessing for Predicting Forest Aboveground Biomass.
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- Forests (19994907), 2023, v. 14, n. 8, p. 1615, doi. 10.3390/f14081615
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