Agriculture and Eutrophication: Where Do We Go from Here? Sustainability, vol.6, pp.5853-5875, 2014. ,
Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions, Science, vol.320, pp.889-892, 2008. ,
Cover Crop Impacts on Watershed Hydrology, J. Soil Water Conserv, vol.53, pp.207-213, 1998. ,
Hiérarchisation Des Facteurs Structurant Les Dynamiques Pluriannuelles Des Sols Nus Hivernaux. Application Au Bassin Versant Du Yar (Bretagne), Norois. Environ. Aménage. Soc, vol.193, pp.17-29, 2004. ,
Marais-Sicre, C. Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data-From Temporal Signatures to Crop Parameters Estimation, Adv. Remote Sens, 2013. ,
Monitoring Vegetation Phenology Using MODIS, Remote Sens. Environ, vol.84, pp.471-475, 2003. ,
A Scalable Approach to Mapping Annual Land Cover at 250 M Using MODIS Time Series Data: A Case Study in the Dry Chaco Ecoregion of South America, Remote Sens. Environ, vol.114, pp.2816-2832, 2010. ,
Estimating Biophysical Variables at 250 M with Reconstructed EOS/MODIS Time Series to Monitor Fragmented Landscapes, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp.7-11, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00443522
, , vol.2
, Remote Sens, vol.11, p.37, 2019.
Monitoring Winter Vegetation Cover Using Multitemporal Modis Data, Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS'05), vol.3, pp.2113-2116, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-00319624
Monitoring Land Use and Land Cover Changes in Oceanic and Fragmented Landscapes with Reconstructed MODIS Time Series, Proceedings of the International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, pp.195-199, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-00319626
Compare NDVI Extracted from Landsat 8 Imagery with That from Landsat 7 Imagery, Am. J. Remote Sens, vol.2, issue.10, 2014. ,
Mapping Crop Phenology Using NDVI Time-Series Derived from HJ-1 A/B Data, Int. J. Appl. Earth Observ. Geoinf, vol.34, pp.188-197, 2015. ,
Integrating SPOT-5 Time Series, Crop Growth Modeling and Expert Knowledge for Monitoring Agricultural practices-The Case of Sugarcane Harvest on Reunion Island, Remote Sens. Environ, vol.113, pp.2052-2061, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-02592040
Crop Discrimination with Multitemporal SPOT/HRV Data in the Saga Plains, Japan. Int. J. Remote Sens, vol.22, pp.1335-1348, 2001. ,
Estimating and Mapping Crop Residues Cover on Agricultural Lands Using Hyperspectral and IKONOS Data, Remote Sens. Environ, vol.104, pp.447-459, 2006. ,
Evaluating Multispectral Remote Sensing and Spectral Unmixing Analysis for Crop Residue Mapping, Remote Sens. Environ, vol.114, pp.2219-2228, 2010. ,
The Application of C-Band Polarimetric SAR for Agriculture: A Review, Can. J. Remote Sens, vol.30, pp.525-542, 2004. ,
Satellite Remote Sensing of River Inundation Area, Stage, and Discharge: A, Review. Hydrol. Process, vol.11, pp.1427-1439, 1997. ,
TerraSAR-X Dual-Pol Time-Series for Mapping of Wetland Vegetation, ISPRS J. Photogramm. Remote Sens, vol.107, pp.90-98, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01155318
Defining the Sensitivity of Multi-Frequency and Multi-Polarized Radar Backscatter to Post-Harvest Crop Residue, Can. J. Remote Sens, vol.27, pp.247-263, 2001. ,
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-00335373
The Sensitivity of Multi-Frequency (X, C and L-Band) Radar Backscatter Signatures to Bio-Physical Variables (LAI) over Corn and Soybean Fields, Int. Arch. Photogramm. Remote Sens, vol.38, pp.318-321, 2010. ,
Multitemporal Classification of TerraSAR-X Data for Wetland Vegetation Mapping, J. Appl ,
URL : https://hal.archives-ouvertes.fr/hal-01152075
Combined Use of Optical and Radar Satellite Data for the Detection of Tillage and Irrigation Operations: Case Study in Central Morocco, Agric. Water Manag, vol.96, pp.1120-1127, 2009. ,
URL : https://hal.archives-ouvertes.fr/ird-00389251
Optical and SAR Sensor Synergies for Forest and Land Cover Mapping in a Tropical Site in West Africa, Int. J. Appl. Earth Observ. Geoinf, vol.21, pp.7-16, 2013. ,
Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR And Optical Image Time Series ,
Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-like Data for Agricultural Applications. Remote Sens, vol.199, pp.415-426, 2017. ,
, Remote Sens, vol.11, p.37, 2019.
Sentinel-2 Cropland Mapping Using Pixel-Based and Object-Based Time-Weighted Dynamic Time Warping Analysis. Remote Sens, vol.204, pp.509-523, 2017. ,
How Much Does Multi-Temporal Sentinel-2 Data Improve Crop Type Classification?, Int. J. Appl. Earth Observ. Geoinf, vol.72, pp.122-130, 2018. ,
Sentinel-2's Potential for Sub-Pixel Landscape Feature Detection ,
Cover Mapping Using Sentinel-1 SAR Data, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.41, 2016. ,
Optical Time Series for Crop Classification, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.811-814, 2017. ,
Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal Sar Sentinel-1, IEEE Geosci. Remote Sens. Lett, vol.15, pp.464-468, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01931485
Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium, Remote Sens, vol.10, p.1642, 2018. ,
Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions, Int. J. Appl. Earth Observ. Geoinf, vol.73, pp.595-604, 2018. ,
Random Forests, Mach. Learn, vol.45, pp.5-32, 2001. ,
Support-Vector Networks, Mach. Learn, vol.20, p.9, 1995. ,
, Nitrates-Water Pollution Environment European Commission, 2018.
Global Scale Climate-crop Yield Relationships and the Impacts of Recent Warming, Environ. Res. Lett, 2007. ,
, Available online, PEPS-Plateforme D'exploitation des Produits Sentinel (CNES), p.9, 2018.
Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF, 2018. ,
Speckle Analysis and Smoothing of Synthetic Aperture Radar Images, Comput. Graph. Image Process, vol.17, pp.24-32, 1981. ,
Feature-Based Nonlocal Polarimetric SAR Filtering. Remote Sens, vol.9, 1043. ,
0: An ESA Educational Toolbox Used for Self-Education in the Field of POLSAR and POL-INSAR Data Analysis, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp.7377-7380, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00845798
Polarimetric Radar Imaging: From Basics to Applications, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00351911
Correction of Aerosol Effects on Multi-Temporal Images Acquired with Constant Viewing Angles: Application to Formosat-2 Images, Remote Sens. Environ, vol.112, pp.1689-1701, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00265400
A Multi-Temporal Method for Cloud Detection, Applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 Images, Remote Sens. Environ, vol.114, pp.1747-1755, 2010. ,
Monitoring Vegetation Systems in the Great Plains with ERTS, vol.NASA, 1974. ,
A Soil-Adjusted Vegetation Index (SAVI), vol.25, pp.295-309, 1988. ,
Use Change and Land Degradation in, Southeastern Mediterranean Spain. Environ. Manag, vol.40, pp.80-94, 2007. ,
The Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: A Review of the Current Status, Future Trends, and Practical Considerations ,
, and The Scientific and Technical Advisory Panel of the Global Environment Facility, vol.47, 2014.
NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ, vol.58, pp.257-266, 1996. ,
Characterizing Water and Nitrogen Stress in Corn Using Remote Sensing, Agron. J, vol.98, pp.579-587, 2006. ,
Review of Methods for in Situ Leaf Area Index (LAI) Determination: Part II. Estimation of LAI, Errors and Sampling, Agric. For. Meteorol, vol.121, pp.37-53, 2004. ,
PROSPECT+SAIL Models: A Review of Use for Vegetation Characterization, Remote Sens. Environ, vol.113, pp.56-66, 2009. ,
Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring. Remote Sens, vol.6, pp.6163-6182, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01102961
Noise Reduction in Chaotic Time-Series Data: A Survey of Common Methods, Phys. Rev. E, vol.48, p.1752, 1993. ,
Random Forest Classifier for Remote Sensing Classification, Int. J. Remote Sens, vol.26, pp.217-222, 2005. ,
Random Forest in Remote Sensing: A Review of Applications and Future Directions, ISPRS J. Photogramm. Remote Sens, vol.114, pp.24-31, 2016. ,
Support Vector Machines in Remote Sensing: A Review, ISPRS J. Photogramm. Remote Sens, vol.66, pp.247-259, 2011. ,
Classification and Regression by randomForest, R News, vol.2, pp.18-22, 2002. ,
Mapping Invasive Plants Using Hyperspectral Imagery and Breiman Cutler Classifications (RandomForest). Remote Sens. Environ, vol.100, pp.356-362, 2006. ,
Misc Functions of the Department of Statistics (e1071), vol.1, pp.5-24, 2008. ,
A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sens. Environ, vol.37, pp.35-46, 1991. ,
Review Article Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications, Int. J. Remote Sens, vol.19, pp.823-854, 1998. ,
RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens, vol.7, pp.4461-4471, 2014. ,
High-Resolution Measurements of Scattering in Wheat Canopies-Implications for Crop Parameter Retrieval, IEEE Trans. Geosci. Remote Sens, vol.41, pp.1602-1610, 2003. ,
A New Method for Crop Classification Combining Time Series of Radar Images and Crop Phenology Information. Remote Sens, vol.198, pp.369-383, 2017. ,
Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI, Remote Sens. Environ, vol.100, pp.321-334, 2006. ,
A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery, Remote Sens. Environ, vol.118, pp.259-272, 2012. ,
Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery, Remote Sens. Environ, vol.115, pp.1145-1161, 2011. ,
Comparison of Pixel-Based and Object-Oriented Image Classification Approaches-A Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China, Int. J. Remote Sens, vol.27, pp.4039-4055, 2006. ,