A5000022398- Soil Moisture and Remote Sensing
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Precipitation Measurement and Analysis
- Cryospheric studies and observations
- Climate change and permafrost
- Geophysics and Gravity Measurements
- Soil Geostatistics and Mapping
- Soil and Unsaturated Flow
- Geophysical Methods and Applications
- Arctic and Antarctic ice dynamics
- Hydrology and Watershed Management Studies
- Remote Sensing and LiDAR Applications
- Flood Risk Assessment and Management
- Remote Sensing in Agriculture
- GNSS positioning and interference
- Meteorological Phenomena and Simulations
- Icing and De-icing Technologies
- Remote Sensing and Land Use
- Geological Modeling and Analysis
- Particle Accelerators and Free-Electron Lasers
- Landslides and related hazards
- Hydrology and Drought Analysis
- Calibration and Measurement Techniques
- Geomagnetism and Paleomagnetism Studies
- Plant Ecology and Soil Science
Université de Sherbrooke
2013-2023
Harvard University
2002-2004
Institut National de la Recherche Scientifique
2000-2002
Centre d'Études Spatiales de la Biosphère
1997
Université Toulouse III - Paul Sabatier
1997
Centre National de la Recherche Scientifique
1997
Laboratoire d’Étude et de Recherche sur l’Économie, les Politiques et les Systèmes Sociaux
1997
The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need long-duration combined active passive L-band microwave observations. response this need, joint Canada-U.S. field experiment (SMAPVEX12) was conducted Manitoba (Canada) over six-week period 2012....
Core Ideas Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. The accuracy of products annual cropland was assessed. spatial representativeness sparse networks determined. In 2015, NASA launched Soil Moisture Active Passive (SMAP) Data this satellite are being exploited to improve forecasting extreme weather events and delivery disaster response. International core validation sites (CVSs) have been contributing data validate calibrate products. Overall...
The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective contribute and Ocean Salinity (SMOS) mission validation the prelaunch assessment of proposed Active Passive (SMAP) mission. During CanEx-SM10, SMOS data as well other passive active microwave measurements were collected by both airborne satellite platforms. Ground-based soil (moisture, temperature, roughness, bulk density) vegetation...
This study was conducted as part of the Soil Moisture and Ocean Salinity (SMOS) calibration validation activities over agricultural boreal forest sites located in Saskatchewan, Canada. For each site covering 33 km × 71 (i.e., about two SMOS pixels), we examined brightness temperature (L1c) soil moisture (L2) products from May 1 to September 30, 2010. The consistency these data with respect theory temporal variation surface characteristics first discussed at both sites. Then, L1c (prototype...
This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of backscattering contributions from vegetation and underlying soils, simplification an existing is proposed in this study. It aims retrieve by using only surface scattering component, once volume contribution removed. Evaluation algorithm performed extensive ground measurements characteristics time series UAVSAR (Uninhabited...
This letter investigates the polarimetric decomposition for monitoring crop growth status over agricultural fields. Based on an existing decomposition, vegetation volume scattering component is removed from full synthetic aperture radar (SAR). Then, estimated orientation combined with dominant mechanism in remaining ground coherency matrix to define indicators SAR. The proposed method evaluated time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and extensive...
We propose a geodesic distance based scattering power decomposition for compact polarimetric (CP) synthetic aperture radar (SAR) data acquired over agricultural landscapes. The proposed technique decomposes the polarized portion of total backscattered in proportion to normalized target similarity measures. measures are derived from distances, which computed between Kennaugh matrices observed and canonical targets (dihedral or trihedral). pseudo component double bounce power, can be...
Salt significantly changes the backscattering coefficient of wet soil. This is easily observed on RADARSAT-1 synthetic aperture radar (SAR) images acquired over a salty depression located in Egyptian desert. The aim this paper to use models understand behavior salt-affected soils and evaluate possibility monitoring salt content. Simulations conducted show lower sensitivity these soil moisture compared nonaffected soils. Besides, there no model suitable represent variation due salinity. These...
Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This so since the overlying volume interacts with both incoming and backscattered radar signal. Therefore, linked solely to top layer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0-5$</tex-math></inline-formula> notation="LaTeX">$cm$</tex-math></inline-formula> ) of cannot be...
This paper aims to retrieve the temporal dynamics of soil moisture from 2015 2019 over an agricultural site in Southeast Australia using Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well a statistical Ordinary Least Squares (OLS) model were established, with auxiliary data including 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire divided...
The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP appeared over Tibetan Plateau. To address this issue, we proposed two methods, machine learning and geostatistics technique to fill the spatial L3 moisture. For technique, train a Random Forest algorithm which aims match output available using series input variables such as brightness temperature (TBH, TBV) in ascending orbits, surface...