|
[1]
|
Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms
Remote Sensing,
2024
DOI:10.3390/rs16030549
|
|
|
|
|
[2]
|
Innovations in Machine and Deep Learning
Studies in Big Data,
2023
DOI:10.1007/978-3-031-40688-1_17
|
|
|
|
|
[3]
|
Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2023
DOI:10.1109/JSTARS.2023.3316304
|
|
|
|
|
[4]
|
A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level
Remote Sensing of Environment,
2023
DOI:10.1016/j.rse.2023.113800
|
|
|
|
|
[5]
|
Trends in Remote Sensing Technologies in Olive Cultivation
Smart Agricultural Technology,
2023
DOI:10.1016/j.atech.2022.100103
|
|
|
|
|
[6]
|
Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence
2023
DOI:10.1016/B978-0-323-99714-0.00012-1
|
|
|
|
|
[7]
|
Trends in Remote Sensing Technologies in Olive Cultivation
Smart Agricultural Technology,
2023
DOI:10.1016/j.atech.2022.100103
|
|
|
|
|
[8]
|
Estimation of Multi-Frequency, Multi-Incidence and Multi-Polarization Backscattering Coefficients over Bare Agricultural Soil Using Statistical Algorithms
Applied Sciences,
2023
DOI:10.3390/app13084893
|
|
|
|
|
[9]
|
Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions
International Journal of Applied Earth Observation and Geoinformation,
2022
DOI:10.1016/j.jag.2022.102818
|
|
|
|
|
[10]
|
Multifractal analysis for spatial characterization of high resolution Sentinel-2/MAJA products in Southwestern France
Remote Sensing of Environment,
2022
DOI:10.1016/j.rse.2021.112859
|
|
|
|
|
[11]
|
Forecasting seasonal plot-specific crop coefficient (Kc) protocol for processing tomato using remote sensing, meteorology, and artificial intelligence
Precision Agriculture,
2022
DOI:10.1007/s11119-022-09910-6
|
|
|
|
|
[12]
|
Landscape Agronomy
2022
DOI:10.1007/978-3-031-05263-7_3
|
|
|
|
|
[13]
|
Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model
Sensors,
2022
DOI:10.3390/s22020580
|
|
|
|
|
[14]
|
On the influence of acquisition geometry in backscatter time series over wheat
International Journal of Applied Earth Observation and Geoinformation,
2022
DOI:10.1016/j.jag.2021.102671
|
|
|
|
|
[15]
|
Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach
Remote Sensing,
2022
DOI:10.3390/rs14112600
|
|
|
|
|
[16]
|
Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data
Remote Sensing,
2022
DOI:10.3390/rs14143249
|
|
|
|
|
[17]
|
Wheat Water Deficit Monitoring Using Synthetic Aperture Radar Backscattering Coefficient and Interferometric Coherence
Agriculture,
2022
DOI:10.3390/agriculture12071032
|
|
|
|
|
[18]
|
Winter Wheat Acreage Extraction Based on Radarsat-2 Data and Polarization Characteristics
2022 10th International Conference on Agro-geoinformatics (Agro-Geoinformatics),
2022
DOI:10.1109/Agro-Geoinformatics55649.2022.9859186
|
|
|
|
|
[19]
|
Fusion of optical and SAR images based on deep learning to reconstruct vegetation NDVI time series in cloud-prone regions
International Journal of Applied Earth Observation and Geoinformation,
2022
DOI:10.1016/j.jag.2022.102818
|
|
|
|
|
[20]
|
The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment
Agriculture,
2022
DOI:10.3390/agriculture12030315
|
|
|
|
|
[21]
|
The noise-reduction potential of Radar Vegetation Index for crop management in the Czech Republic
Precision Agriculture,
2022
DOI:10.1007/s11119-021-09844-5
|
|
|
|
|
[22]
|
Multifractal analysis for spatial characterization of high resolution Sentinel-2/MAJA products in Southwestern France
Remote Sensing of Environment,
2022
DOI:10.1016/j.rse.2021.112859
|
|
|
|
|
[23]
|
Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data
Canadian Journal of Remote Sensing,
2022
DOI:10.1080/07038992.2021.2011180
|
|
|
|
|
[24]
|
Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat
Remote Sensing,
2021
DOI:10.3390/rs13040553
|
|
|
|
|
[25]
|
National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm
Remote Sensing,
2021
DOI:10.3390/rs13050846
|
|
|
|
|
[26]
|
Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors
Remote Sensing,
2021
DOI:10.3390/rs13071393
|
|
|
|
|
[27]
|
C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)
Earth System Science Data,
2021
DOI:10.5194/essd-13-3707-2021
|
|
|
|
|
[28]
|
Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI
Remote Sensing,
2021
DOI:10.3390/rs13152856
|
|
|
|
|
[29]
|
Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2021
DOI:10.1109/JSTARS.2021.3095537
|
|
|
|
|
[30]
|
Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2
Remote Sensing,
2021
DOI:10.3390/rs13245036
|
|
|
|
|
[31]
|
Potential and Complementarity of Dense SAR and Optical Data for Rapeseed Crops Monitoring
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS,
2021
DOI:10.1109/IGARSS47720.2021.9553318
|
|
|
|
|
[32]
|
Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces
International Journal of Applied Earth Observation and Geoinformation,
2020
DOI:10.1016/j.jag.2019.101972
|
|
|
|
|
[33]
|
Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring
Remote Sensing,
2020
DOI:10.3390/rs12182919
|
|
|
|
|
[34]
|
Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots
Remote Sensing,
2020
DOI:10.3390/rs12213478
|
|
|
|
|
[35]
|
Towards an Improved Inventory of N2O Emissions Using Land Cover Maps Derived from Optical Remote Sensing Images
Atmosphere,
2020
DOI:10.3390/atmos11111188
|
|
|
|
|
[36]
|
Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain
Remote Sensing,
2020
DOI:10.3390/rs12020278
|
|
|
|
|
[37]
|
Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series
Remote Sensing of Environment,
2020
DOI:10.1016/j.rse.2020.111660
|
|
|
|
|
[38]
|
Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale
Agronomy,
2020
DOI:10.3390/agronomy10030327
|
|
|
|
|
[39]
|
Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages
ISPRS Journal of Photogrammetry and Remote Sensing,
2020
DOI:10.1016/j.isprsjprs.2020.03.009
|
|
|
|
|
[40]
|
Microwave Remote Sensing Tools in Environmental Science
2020
DOI:10.1007/978-3-030-45767-9_1
|
|
|
|
|
[41]
|
Overview Of A Decade Of Yearly Land Cover Classifications Derived From Multi-Temporal Optical Satellite Images
2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS),
2020
DOI:10.1109/M2GARSS47143.2020.9105220
|
|
|
|
|
[42]
|
Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series
Remote Sensing,
2020
DOI:10.3390/rs12152385
|
|
|
|
|
[43]
|
Google Earth Engine: Application Of Algorithms For Remote Sensing Of Crops In Tuscany (Italy)
2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS),
2020
DOI:10.1109/LAGIRS48042.2020.9165561
|
|
|
|
|
[44]
|
The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images
International Journal of Parallel, Emergent and Distributed Systems,
2019
DOI:10.1080/17445760.2019.1597084
|
|
|
|
|
[45]
|
Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images
Remote Sensing,
2019
DOI:10.3390/rs11171978
|
|
|
|
|
[46]
|
Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping
Remote Sensing,
2019
DOI:10.3390/rs11192228
|
|
|
|
|
[47]
|
Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science,
2019
DOI:10.1007/s41064-019-00076-x
|
|
|
|
|
[48]
|
Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes
Remote Sensing,
2019
DOI:10.3390/rs11080979
|
|
|
|
|
[49]
|
Evaluation of Sentinel-1 and -2 time series to derive crop phenology and biomass of wheat and rapeseed: northen France and Brittany case studies
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI,
2019
DOI:10.1117/12.2533132
|
|
|
|
|
[50]
|
Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France
Remote Sensing,
2019
DOI:10.3390/rs11232842
|
|
|
|
|
[51]
|
Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data
Remote Sensing,
2019
DOI:10.3390/rs11131569
|
|
|
|
|
[52]
|
Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes
Remote Sensing of Environment,
2019
DOI:10.1016/j.rse.2019.111452
|
|
|
|
|
[53]
|
First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
Remote Sensing,
2019
DOI:10.3390/rs11232769
|
|
|
|
|
[54]
|
Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes
Remote Sensing,
2018
DOI:10.3390/rs11010037
|
|
|
|
|
[55]
|
Evaluation of the potentiality of polarimetric C- and L-SAR time-series images for the identification of winter land-use
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium,
2018
DOI:10.1109/IGARSS.2018.8517904
|
|
|
|
|
[56]
|
Identification of Winter Land Use in Temperate Agricultural Landscapes based on Sentinel-1 and 2 Times-Series
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium,
2018
DOI:10.1109/IGARSS.2018.8517673
|
|
|
|
|
[57]
|
Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata
European Journal of Remote Sensing,
2018
DOI:10.1080/22797254.2017.1401909
|
|
|
|
|
[58]
|
Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
Sensors,
2017
DOI:10.3390/s17112617
|
|
|
|
|
[59]
|
Estimation of Sunflower Yield Using a Simplified Agrometeorological Model Controlled by Optical and SAR Satellite Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2017
DOI:10.1109/JSTARS.2017.2737656
|
|
|
|
|
[60]
|
Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop
Hydrology and Earth System Sciences,
2017
DOI:10.5194/hess-21-4767-2017
|
|
|
|
|
[61]
|
Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX,
2017
DOI:10.1117/12.2280409
|
|
|
|
|
[62]
|
Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks
International Journal of Applied Earth Observation and Geoinformation,
2017
DOI:10.1016/j.jag.2016.12.011
|
|
|
|
|
[63]
|
Forecast of wheat yield throughout the agricultural season using optical and radar satellite images
International Journal of Applied Earth Observation and Geoinformation,
2017
DOI:10.1016/j.jag.2017.03.011
|
|
|
|
|
[64]
|
Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops
Journal of Applied Remote Sensing,
2016
DOI:10.1117/1.JRS.10.026020
|
|
|
|
|
[65]
|
Estimation of leaf area index and crop height of sunflowers using multi-temporal optical and SAR satellite data
International Journal of Remote Sensing,
2016
DOI:10.1080/01431161.2016.1176276
|
|
|
|
|
[66]
|
Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2016
DOI:10.1109/JSTARS.2015.2464698
|
|
|
|
|
[67]
|
Sensitivity of X-Band (σ0, γ) and Optical (NDVI) Satellite Data to Corn Biophysical Parameters
Advances in Remote Sensing,
2016
DOI:10.4236/ars.2016.52009
|
|
|
|
|
[68]
|
Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series
Remote Sensing,
2016
DOI:10.3390/rs8070591
|
|
|
|
|
[69]
|
Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2016
DOI:10.1109/JSTARS.2016.2541169
|
|
|
|
|
[70]
|
Yield estimation of the winter wheat using Radarsat 2 polarimetric SAR reponse
2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP),
2016
DOI:10.1109/ATSIP.2016.7523143
|
|
|
|
|
[71]
|
Land Surface Remote Sensing in Agriculture and Forest
2016
DOI:10.1016/B978-1-78548-103-1.50006-6
|
|
|
|
|
[72]
|
Estimation of crop parameters using multi-temporal optical and radar polarimetric satellite data
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII,
2015
DOI:10.1117/12.2194781
|
|
|
|
|
[73]
|
Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada
Remote Sensing of Environment,
2015
DOI:10.1016/j.rse.2015.06.024
|
|
|
|
|
[74]
|
Estimation of sunflower yield using multi-spectral satellite data (optical or radar) in a simplified agro-meteorological model
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
2015
DOI:10.1109/IGARSS.2015.7326702
|
|
|
|
|
[75]
|
Estimation of soybean yield from assimilated optical and radar data into a simplified agrometeorological model
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
2015
DOI:10.1109/IGARSS.2015.7326692
|
|
|
|
|
[76]
|
Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images
ISPRS Journal of Photogrammetry and Remote Sensing,
2014
DOI:10.1016/j.isprsjprs.2014.04.021
|
|
|
|
|
[77]
|
Optical and radar temporal signatures of sunflower using synchronous satellite images — Multi-frequencies and multi-polarizations analyses
2014 IEEE Geoscience and Remote Sensing Symposium,
2014
DOI:10.1109/IGARSS.2014.6946725
|
|
|
|
|
[78]
|
Use of satellite altimetry and imagery for monitoring the volume of small lakes
2014 IEEE Geoscience and Remote Sensing Symposium,
2014
DOI:10.1109/IGARSS.2014.6946380
|
|
|
|
|
[79]
|
Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data
Remote Sensing,
2014
DOI:10.3390/rs61010002
|
|
|
|
|
[80]
|
Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring
Remote Sensing,
2014
DOI:10.3390/rs61110375
|
|
|
|
|
[81]
|
Combining high-resolution satellite images and altimetry to estimate the volume of small lakes
Hydrology and Earth System Sciences,
2014
DOI:10.5194/hess-18-2007-2014
|
|
|
|
|
[82]
|
Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin
Remote Sensing Letters,
2013
DOI:10.1080/2150704X.2013.842285
|
|
|
|
|
[83]
|
Combining high-resolution satellite images and altimetry to estimate the volume of small lakes
Hydrology and Earth System Sciences Discussions,
2013
DOI:10.5194/hessd-10-15731-2013
|
|
|
|