Advances in Remote Sensing

Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.net/journal/ars
E-mail: [email protected]
"Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation"
written by Rémy Fieuzal, Frédéric Baup, Claire Marais-Sicre,
published by Advances in Remote Sensing, Vol.2 No.2, 2013
has been cited by the following article(s):
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[1] Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning
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[2] Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms
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[3] A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat
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[4] Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning
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[5] A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery
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[6] Convolutional Neural Networks for Planting System Detection of Olive Groves
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[7] Reviews of Methods for Vegetation Phenology Monitoring from Remote Sensing Data
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[8] A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level
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[9] 植被物候遥感监测研究进展
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[10] Machine learning approach for climate change impact assessment in agricultural production
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[11] Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints
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[12] Estimation of Multi-Frequency, Multi-Incidence and Multi-Polarization Backscattering Coefficients over Bare Agricultural Soil Using Statistical Algorithms
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[13] Winter Wheat Acreage Extraction Based on Radarsat-2 Data and Polarization Characteristics
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[14] Integrating earth observation data into area frame sampling approach to improve crop production estimates
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[15] Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data
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[16] Multifractal analysis for spatial characterization of high resolution Sentinel-2/MAJA products in Southwestern France
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[17] Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach
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[18] Availability and integration of agro-environmental data: the French case
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[19] On the influence of acquisition geometry in backscatter time series over wheat
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[20] The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment
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[21] Wheat Water Deficit Monitoring Using Synthetic Aperture Radar Backscattering Coefficient and Interferometric Coherence
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[22] Forecasting seasonal plot-specific crop coefficient (Kc) protocol for processing tomato using remote sensing, meteorology, and artificial intelligence
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[23] The noise-reduction potential of Radar Vegetation Index for crop management in the Czech Republic
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[24] Trends in Remote Sensing Technologies in Olive Cultivation
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[25] Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions
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[26] Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model
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[27] Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data
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[28] Evaluation of multiorbital SAR and multisensor optical data for empirical estimation of rapeseed biophysical parameters
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[29] C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)
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[30] Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI
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[31] Detecting phenological development of winter wheat and winter barley using time series of Sentinel-1 and Sentinel-2
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[32] National crop mapping using sentinel-1 time series: A knowledge-based descriptive algorithm
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[33] Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat. Remote Sens. 2021, 13, 553
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[34] Cereal crops soil parameters retrieval using L-Band ALOS-2 and C-Band Sentinel-1 sensors
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[35] Estimating Kc on a plot level using remote sensing and artificial intelligence
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[36] Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat
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[37] Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes
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[38] Ensemble Learning for Crop Monitoring from Multitemporal Optical and Synthetic Aperture Radar Earth Observations
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[39] Potential and Complementarity of Dense SAR and Optical Data for Rapeseed Crops Monitoring
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[40] 115. Estimating Kc on a plot level using remote sensing and artificial intelligence
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[41] Deriving crop productivity indicators from satellite synthetic aperture radar to assess wheat production at field-scale.
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[42] Evaluation des séries temporelles Sentinel-1 & 2 pour l'identification et la caractérisation des continuités écologiques, des paysages boisés aux paysages agricoles
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[43] Informing on Crop Water-Use, Stress, and Growth with Integrated Satellite Remote Sensing and Modeling
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[44] Towards an Improved Inventory of N2O Emissions Using Land Cover Maps Derived from Optical Remote Sensing Images
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[45] MONITORING THE CROPS PHENOLOGY USING TIME SERIES SENTINEL 2 IMAGES
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[46] Sentinel-1 And Sentinel-2 Data For The Characterisation Of The States Of Continental Surface Over A Semi-Arid Region En Tunisia
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[47] Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces
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[48] Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages
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[49] Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and-2 time series
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[50] Combined use of multi-temporal Landsat-8 and sentinel-2 images for wheat yield estimates at the intra-plot spatial scale
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[51] Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain
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[52] Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy)
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[53] Comparing Sentinel-1 and-2 Data and Indices for Agricultural Land Use Monitoring
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[54] Overview Of A Decade Of Yearly Land Cover Classifications Derived From Multi-Temporal Optical Satellite Images
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[55] Basic Concepts of Microwave Radiometry
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[56] Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series
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[57] Evaluation of Sentinel-1 and-2 time series for estimating LAI and biomass of wheat and rapeseed crop types
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[58] Etude des lois d'échelle multifractales caractérisant les observations satellitaires des surfaces continentales
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[59] Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots
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[60] Evaluation of time-series SAR and optical images for the study of winter land-use
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[61] Using SAR Data to Detect Wheat Irrigation Supply in an Irrigated Semi-arid Area
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[62] Evaluation of using sentinel-1 and-2 time-series to identify winter land use in agricultural landscapes
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[63] Crop mapping and yield estimation of wheat in the Bekaa plain of Lebanon
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[64] Potenzialità del radar nella stima dell'umidità del suolo in un bacino sperimentale in Sardegna
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[65] Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France
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[66] Fusing optical and SAR time series for LAI gap fillingwith multioutput Gaussian processes
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[67] Evaluation of Sentinel-1 and-2 time series to derive crop phenology and biomass of wheat and rapeseed: northen France and Brittany case studies
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[68] First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
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[69] Apport des données multispectrales (hyperfréquences, thermique, optique) pour le suivi hydrique des cultures: application aux couverts de blé et de tournesol
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[70] Potenzialità del radar nella stima dell'umidità del suolo in un bacino sperimentale in Sardegna.
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[71] Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes
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[72] Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping
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[73] Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
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[74] Estimation of Wheat Yields at the Intra-Plot Scale by Combining Multi-Temporal Landsat-8 and Sentinel-2 Images
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[75] The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images
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[76] Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data
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[77] Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images
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[78] Evapotranspiration monitoring based on thermal infrared data over agricultural landscapes: comparison of a simple energy budget model and a SVAT model
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[79] Identification and characterization of agricultural winter land-use based on Alos-2 and Radarsat-2 polarimetric SAR time-series images
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[80] Evaluation of the potentiality of polarimetric C-and L-SAR time-series images for the identification of winter land-use
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[81] Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series
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[82] Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata
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[83] Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks
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[84] Forecast of wheat yield throughout the agricultural season using optical and radar satellite images
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[85] Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications
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[86] Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data
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[87] Apport des images satellites multi-spectrales (optique et radar) pour la classification des surfaces en herbe.
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[88] Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties
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[89] Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop
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[90] Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
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[91] Monitoring of Agricultural Landscapes Using Remote Sensing Data
Land Surface Remote Sensing in Agriculture and Forest, 2017
[92] Caractérisation de la viticulture irriguée par télédétection en contexte de changement climatique: application aux vignobles de la province de Mendoza en Argentine
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[93] Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield
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[94] Sensitivity of X-Band (σ0, γ) and Optical (NDVI) Satellite Data to Corn Biophysical Parameters
Advances in Remote Sensing, 2016
[95] Estimation of leaf area index and crop height of sunflowers using multi-temporal optical and SAR satellite data
International Journal of Remote Sensing, 2016
[96] Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops
Journal of Applied Remote Sensing, 2016
[97] Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series
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[98] Yield estimation of the winter wheat using Radarsat 2 polarimetric SAR reponse
2016
[99] Using remote sensing (optical and radar) and modeling to support the irrigation management of cereals in a semi-arid region: a case study of the Tadla irrigated …
2015
[100] Using remote sensing (optical and radar) and modeling to support the irrigation management of cereals in a semi-arid region: a case study of the Tadla irrigated perimeter in Morocco
Doctoral dissertation, Université de Liège,​ Liège,​​ Belgique, 2015
[101] Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of (Volume:PP , Issue: 99 ) , 2015
[102] Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada
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[103] Estimation of crop parameters using multi-temporal optical and radar polarimetric satellite data
SPIE Remote Sensing, 2015
[104] Estimation of sunflower yield using multi-spectral satellite data (optical or radar) in a simplified agro-meteorological model
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 2015
[105] Estimation of soybean yield from assimilated optical and radar data into a simplified agrometeorological model
Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, 2015
[106] 基于多源遥感信息融合的广东省土地利用分类方法——以雷州半岛为例
生态学报, 2014
[107] Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images
ISPRS Journal of Photogrammetry and Remote Sensing, 2014
[108] Combining high-resolution satellite images and altimetry to estimate the volume of small lakes
Hydrology and Earth System Sciences, 2014
[109] Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring
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[110] Optical and radar temporal signatures of sunflower using synchronous satellite images—Multi-frequencies and multi-polarizations analyses
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[111] Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data
Remote Sensing, 2014
[112] Use of satellite altimetry and imagery for monitoring the volume of small lakes
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 2014
[113] Estimation spatialisée de l'évapotranspiration à l'aide de données infra-rouge thermique multi-résolutions
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[114] 基于多源遥感信息融合的广东省土地利用分类方法--以雷州半岛为例
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[115] Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin
Remote Sensing Letters, 2013
[116] Apports de données radar pour l'estimation des paramètres biophysiques des surfaces agricoles
2013
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