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Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Mapping rice areas with Sentinel-1 time series and superpixel segmentation
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Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data
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Filtering of multichannel SAR images
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First Results of Rice Monitoring Practices in Spain by Means of Time Series of TerraSAR-X Dual-Pol Images
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Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas
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Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands
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inferredcites →https://doi.org/10.1109/rtsi.2018.8548410openalex-snapshot
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The Measurement of Observer Agreement for Categorical Data
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Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images
inferredcites →https://doi.org/10.3390/rs10081217openalex-snapshot
Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform
inferred← citeshttps://doi.org/10.3390/ijgi15060275openalex-snapshot
Multisource Satellite Data-Driven Machine Learning Approach for Rice Yield Prediction
inferred← citeshttps://doi.org/10.3390/ijgi9040191openalex-snapshot
Abandoned Farmland Location in Areas Affected by Rapid Urbanization Using Textural Characterization of High Resolution Aerial Imagery
inferred← citeshttps://doi.org/10.3390/ijgi9110663openalex-snapshot
Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia
inferred← citeshttps://doi.org/10.3390/land13010091openalex-snapshot
Development of a Pre-Automatized Processing Chain for Agricultural Monitoring Using a Multi-Sensor and Multi-Temporal Approach
inferred← citeshttps://doi.org/10.3390/rs11121441openalex-snapshot
Crop NDVI Monitoring Based on Sentinel 1
inferred← citeshttps://doi.org/10.3390/rs11151836openalex-snapshot
Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
inferred← citeshttps://doi.org/10.3390/rs11161887openalex-snapshot
Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands
inferred← citeshttps://doi.org/10.3390/rs11171985openalex-snapshot
Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review
inferred← citeshttps://doi.org/10.3390/rs12091456openalex-snapshot
Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
inferred← citeshttps://doi.org/10.3390/rs12101622openalex-snapshot
Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine
inferred← citeshttps://doi.org/10.3390/rs12121976openalex-snapshot
Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
inferred← citeshttps://doi.org/10.3390/rs12132159openalex-snapshot
Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data
inferred← citeshttps://doi.org/10.3390/rs12203459openalex-snapshot
Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion
inferred← citeshttps://doi.org/10.3390/rs12213613openalex-snapshot
Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1
inferred← citeshttps://doi.org/10.3390/rs12223733openalex-snapshot
Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images
inferred← citeshttps://doi.org/10.3390/rs12233959openalex-snapshot
An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand
inferred← citeshttps://doi.org/10.3390/rs12244058openalex-snapshot
Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data
inferred← citeshttps://doi.org/10.3390/rs13010103openalex-snapshot
Rice-Field Mapping with Sentinel-1A SAR Time-Series Data
inferred← citeshttps://doi.org/10.3390/rs13040732openalex-snapshot
Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data
inferred← citeshttps://doi.org/10.3390/rs13050846openalex-snapshot
National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm
inferred← citeshttps://doi.org/10.3390/rs13081498openalex-snapshot
Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics
inferred← citeshttps://doi.org/10.3390/rs13132584openalex-snapshot
An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data
inferred← citeshttps://doi.org/10.3390/rs13193994openalex-snapshot
Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model
inferred← citeshttps://doi.org/10.3390/rs13214400openalex-snapshot
Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm
inferred← citeshttps://doi.org/10.3390/rs13224514openalex-snapshot
Using Time Series Optical and SAR Data to Assess the Impact of Historical Wetland Change on Current Wetland in Zhenlai County, Jilin Province, China
inferred← citeshttps://doi.org/10.3390/rs13224609openalex-snapshot
High Resolution Distribution Dataset of Double-Season Paddy Rice in China
inferred← citeshttps://doi.org/10.3390/rs13224666openalex-snapshot
An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China
inferred← citeshttps://doi.org/10.3390/rs14020328openalex-snapshot
Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels
inferred← citeshttps://doi.org/10.3390/rs14030759openalex-snapshot
Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine
inferred← citeshttps://doi.org/10.3390/rs14081929openalex-snapshot
Spatial-Temporal Neural Network for Rice Field Classification from SAR Images
inferred← citeshttps://doi.org/10.3390/rs14092157openalex-snapshot
Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images
inferred← citeshttps://doi.org/10.3390/rs14133213openalex-snapshot
Rice Mapping in a Subtropical Hilly Region Based on Sentinel-1 Time Series Feature Analysis and the Dual Branch BiLSTM Model
inferred← citeshttps://doi.org/10.3390/rs14143254openalex-snapshot
Rice Crop Monitoring Using Sentinel-1 SAR Data: A Case Study in Saku, Japan
inferred← citeshttps://doi.org/10.3390/rs14153721openalex-snapshot
Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method
inferred← citeshttps://doi.org/10.3390/rs14184573openalex-snapshot
Rice Planting Area Identification Based on Multi-Temporal Sentinel-1 SAR Images and an Attention U-Net Model
inferred← citeshttps://doi.org/10.3390/rs15112785openalex-snapshot
An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images
inferred← citeshttps://doi.org/10.3390/rs15164055openalex-snapshot
High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data
inferred← citeshttps://doi.org/10.3390/rs15164071openalex-snapshot
Mapping Irrigated Croplands from Sentinel-2 Images Using Deep Convolutional Neural Networks
inferred← citeshttps://doi.org/10.3390/rs16152785openalex-snapshot
Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data
inferred← citeshttps://doi.org/10.3390/rs16173180openalex-snapshot
A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data
inferred← citeshttps://doi.org/10.3390/rs17010057openalex-snapshot
Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery
inferred← citeshttps://doi.org/10.3390/rs17040692openalex-snapshot
PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency
inferred← citeshttps://doi.org/10.3390/rs17061033openalex-snapshot
Paddy Rice Mapping in Hainan Island Using Time-Series Sentinel-1 SAR Data and Deep Learning
inferred← citeshttps://doi.org/10.3390/rs17081479openalex-snapshot
Rapeseed Area Extraction Based on Time-Series Dual-Polarization Radar Vegetation Indices
inferred← citeshttps://doi.org/10.3390/rs18060951openalex-snapshot
Terrain-Aware Self-Supervised Representation Learning for Tree Species Mapping in Mountainous Regions Under Limited Field Samples
inferred← citeshttps://doi.org/10.3390/rs18081248openalex-snapshot
Prediction of Rice and Wheat Cultivation Regions of Chongming Island Using Time-Series Sentinel-1A SAR Images
inferred← citeshttps://doi.org/10.3390/s20102969openalex-snapshot
Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net
inferred← citeshttps://doi.org/10.3390/su13020503openalex-snapshot
Mapping Paddy Rice with Satellite Remote Sensing: A Review
inferred← citeshttps://doi.org/10.3390/w14050804openalex-snapshot
Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data
inferred← citeshttps://doi.org/10.3390/w14091341openalex-snapshot
Comparative Analysis between Two Operational Irrigation Mapping Models over Study Sites in Mediterranean and Semi-Oceanic Regions
inferred← citeshttps://doi.org/10.34133/remotesensing.0152openalex-snapshot
Paddy Rice Mapping Based on Phenology Matching and Cultivation Pattern Analysis Combining Multi-Source Data in Guangdong, China
inferred← citeshttps://doi.org/10.4236/jgis.2025.176017openalex-snapshot
Temporal Profile Analysis of Sentinel-1 Image to Identify T. Aman Rice Varieties in the Coastal Region of Bangladesh
inferred← citeshttps://doi.org/10.4287/jsprs.60.144openalex-snapshot
Estimation of North Korea's paddy harvest area in 2020 by Sentinel1
inferred← citeshttps://doi.org/10.46932/sfjdv5n11-021openalex-snapshot
Machine learning-based crop type detection and vegetation mon-itoring using Sentinel-1 SAR and Landsat 8
inferred← citeshttps://doi.org/10.4995/raet.2022.17426openalex-snapshot
High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series
inferred← citeshttps://doi.org/10.5194/essd-13-5969-2021openalex-snapshot
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
inferred← citeshttps://doi.org/10.5194/essd-17-95-2025openalex-snapshot
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
inferred← citeshttps://doi.org/10.5194/essd-2024-90openalex-snapshot
SEA-Rice-Ci10: High-resolution Mapping of Rice Cropping Intensity and Harvested Area Across Southeast Asia using the Integration of Sentinel-1 and Sentinel-2 Data
inferred← citeshttps://doi.org/10.5194/isprs-annals-v-3-2020-641-2020openalex-snapshot
DATA PROCESSING ARCHITECTURES FOR MONITORING FLOODS USING SENTINEL-1
inferred← citeshttps://doi.org/10.5194/isprs-archives-xlii-4-w18-897-2019openalex-snapshot
RICE CROP MAPPING USING SENTINEL-1 TIME SERIES IMAGES (CASE STUDY: MAZANDARAN, IRAN)
inferred← citeshttps://doi.org/10.5194/isprs-archives-xliii-b3-2020-259-2020openalex-snapshot
EXPLOITING SENTINEL-1 SAR TIME SERIES TO DETECT GRASSLANDS IN THE NORTHERN BRAZILIAN AMAZON
inferred← citeshttps://doi.org/10.52045/jca.v3i1.298openalex-snapshot
Vegetation-Water-Built Up Index Combined: Algorithm Indices Combination for Characterization and distribution of Mangrove Forest through Google Earth Engine
inferred← citeshttps://doi.org/10.53555/kuey.v30i6.5160openalex-snapshot
Mapping Sowing Dates of Paddy in Different Agro-Climatic Regions of Punjab using SAR Satellite Data
inferred← citeshttps://doi.org/10.61726/1785.2024.36.72.001openalex-snapshot
Geoinformation Support of Agricultural Land Accounting to Improve the Economic Efficiency of Municipal District
inferred← citeshttps://doi.org/10.62880/rars26001openalex-snapshot
Use of Sentinel-1 to monitor the evolution of rice production in the Vietnam Mekong Delta over the last decade
inferred← citeshttps://doi.org/10.70675/f36bf8dbzfb3dz4b7az8dfez7343c0b5b9baopenalex-snapshot
Mapping Irrigated Areas using Radar and Optical Remote Sensing Data
inferred← citeshttps://doi.org/10.7160/aol.2021.130401openalex-snapshot
Geoinformation Methodology of Agricultural Land Evaluation for Agricultural Economic Growth
inferred← citeshttps://doi.org/10.7745/kjssf.2020.53.3.345openalex-snapshot
Estimation of Rice Cultivation Area by Threshold Method using Sentinel-1 Imagery in South Korea
inferred← citeshttps://doi.org/10.7780/kjrs.2025.41.3.5openalex-snapshot
Rice Cultivation Area Estimation Using Sentinel-1/2 Imagery: Focus on Classification Methods and Farm Map Integration Strategies
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