Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data, Int. J. Appl. Earth Obs. Geoinf, vol.38, pp.321-334, 2015. ,
Development and validation of the global map of irrigation areas, Hydrol. Earth Syst. Sci, vol.9, pp.535-547, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-00304866
A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US, Remote Sens. Environ, vol.112, pp.3520-3537, 2008. ,
Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data ,
Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute, Int. J. Remote Sens, vol.25, pp.2277-2286, 2004. ,
Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data, Remote Sens. Environ, vol.204, pp.197-211, 2018. ,
Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data, Int. J. Remote Sens, vol.26, pp.2527-2547, 2005. ,
Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data, Remote Sens, vol.10, 1953. ,
URL : https://hal.archives-ouvertes.fr/hal-01952417
Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops, Hydrol. Earth Syst. Sci, vol.15, 1117. ,
In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series ,
, Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens, vol.10, 1495.
URL : https://hal.archives-ouvertes.fr/hal-01900567
Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia ,
URL : https://hal.archives-ouvertes.fr/hal-02609618
Estimating surface soil moisture from TerraSAR-X data over two small catchments in the Sahelian Part of Western Niger ,
A new empirical model for radar scattering from bare soil surfaces ,
URL : https://hal.archives-ouvertes.fr/hal-01529350
Semiempirical Calibration of the Integral Equation Model for SAR Data in C-Band and Cross Polarization Using Radar Images and Field Measurements, IEEE Geosci. Remote Sens. Lett, vol.8, pp.14-18, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00550013
, Remote Sens, vol.12, p.1456, 2020.
Toward an operational bare soil moisture mapping using TerraSAR-X data acquired over agricultural areas, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol.6, pp.900-916, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00911016
Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol.9, pp.1229-1243, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01249995
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution, vol.17, 1966. ,
URL : https://hal.archives-ouvertes.fr/hal-01735576
Comparative analysis of the accuracy of surface soil moisture estimation from the C-and L-bands, Int. J. Appl. Earth Obs. Geoinf, vol.82, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02609461
Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy, vol.12, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02613661
Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data. Remote Sens, vol.6, pp.10002-10032, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01098272
Using SAR Data to Detect Wheat Irrigation Supply in an Irrigated Semi-arid Area, J. Agric. Sci, vol.11, 2018. ,
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series ,
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, IEEE Geosci. Remote Sens. Mag, vol.5, pp.8-36, 2017. ,
Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France, Remote Sens, vol.10, 1217. ,
URL : https://hal.archives-ouvertes.fr/hal-01900540
Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta ,
Mapping Paddy Rice Using Sentinel-1 SAR, Time Series, vol.11, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02137627
Distilling Before Refine: Spatio-Temporal Transfer Learning for Mapping Irrigated Areas Using Sentinel-1 Time Series, IEEE Geosci. Remote Sens. Lett, vol.2020, pp.1-5 ,
URL : https://hal.archives-ouvertes.fr/hal-02610244
Exploring the potential contribution of irrigation to global agricultural primary productivity: IRRIGATION AND PRIMARY PRODUCTIVITY, Glob. Biogeochem. Cycles, vol.25, 2011. ,
Effects of deficit irrigation on yield, water productivity, and economic returns of wheat. Agric. Water Manag, vol.92, pp.151-161, 2007. ,
Improving Irrigation Water Use Efficiency: A Review of Advances, Challenges and Opportunities in the Australian Context. Water, vol.10, 1771. ,
Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas ,
URL : https://hal.archives-ouvertes.fr/hal-01737156
Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France, Sensors, vol.19, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02608831
Rodes, I. Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series ,
Vegetation modeled as a water cloud, Radio Sci, vol.13, pp.357-364, 1978. ,
Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping, p.2228 ,
URL : https://hal.archives-ouvertes.fr/hal-02609689
, Remote Sens, vol.12, p.1456, 2020.
Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France, Remote Sens, vol.10, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01900522
A Comparison of Two Soil Moisture Products S2MP and Copernicus-SSM Over Southern France, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol.2019, pp.1-10 ,
URL : https://hal.archives-ouvertes.fr/hal-02609692
Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands, Remote Sens, vol.11, p.31, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-02608303
Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust, Remote Sens. Environ, vol.115, pp.1801-1810, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00602355
Potential of Sentinel-1 Images for Estimating the Soil Roughness over, Bare Agricultural Soils, vol.10, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01702526
Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: Application to hydrological and erosion modelling, Hydrol. Process. Int. J, vol.22, pp.9-20, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00149444
Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields, Remote Sens. Environ, vol.112, pp.4370-4379, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00473243
Impact of Diurnal Variation in Vegetation Water Content on Radar Backscatter from Maize During Water Stress, IEEE Trans. Geosci. Remote Sens, vol.53, pp.3855-3869, 2015. ,
The diurnal pattern of microwave backscattering by wheat, Remote Sens. Environ, vol.34, pp.37-47, 1990. ,
Diurnal Differences in Global ERS Scatterometer Backscatter Observations of the Land Surface, IEEE Trans. Geosci. Remote Sens, vol.50, pp.2595-2602, 2012. ,
Introduction to Data Mining, 2006. ,
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