Rémy Fieuzal

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

Localization of RFI Sources for the SMOS Mission: A Means for Assessing SMOS Pointing Performances

Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed.

Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield

Crop monitoring at a fine scale and crop yield estimation are critical from an environmental perspective because they provide essential information to combine increased food production and sustainable management of agricultural landscapes. The aim of this article is to estimate soybean yield using an agro-meteorological model controlled by optical and/or synthetic aperture radar (SAR) multipolarized satellite images. Satellite and ground data were collected over seven working farms. Optical and SAR images were acquired by Formosat-2, Spot-4, Spot-5, and Radarsat-2 satellites during the soybean vegetation cycle. A vegetation index (NDVI) was derived from the optical images, and backscattering coefficients and polarimetric indicators were computed from full quad-pol Radarsat-2 images. An angular normalization of SAR data was performed to minimize the incidence angle effects on SAR signals by using the complementarities provided by SAR and optical data. The best results are obtained when the model is controlled by both the leaf area index (LAI) derived from the optical vegetation index modified triangular vegetation index (MTVI2) or from the SAR backscattering coefficient σ°VV (LAIMTVI2 or (LAIσ°VV) and the dry biomass (DB) derived from the SAR Pauli matrix T33 (DBT33)(r2 > 0.83), demonstrating the complementary of optical and SAR data.

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R2) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m3∙m−3 in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops

Abstract. This paper aims to evaluate the contribution of multitemporal polarimetric synthetic aperture radar (SAR) data for winter wheat and rapeseed crops parameters [height, leaf area index, and dry biomass (DB)] estimation, during their whole vegetation cycles in comparison to backscattering coefficients and optical data. Angular sensitivities and dynamics of polarimetric indicators were also analyzed following the growth stages of these two common crop types using, in total, 14 radar images (Radarsat-2), 16 optical images (Formosat-2, Spot-4/5), and numerous ground data. The results of this study show the importance of correcting the angular effect on SAR signals especially for copolarized signals and polarimetric indicators associated to single-bounce scattering mechanisms. The analysis of the temporal dynamic of polarimetric indicators has shown their high potential to detect crop growth changes. Moreover, this study shows the high interest of using SAR parameters (backscattering coefficients and polarimetric indicators) for crop parameters estimation during the whole vegetation cycle instead of optical vegetation index. They particularly revealed their high potential for rapeseed height and DB monitoring [i.e., Shannon entropy polarimetry (r2=0.70) and radar vegetation index (r2=0.80), respectively].

Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series

In the context of climate change, agricultural managers have the imperative to combine sufficient productivity with durability of the resources. Many studies have shown the interest of recent satellite missions as suitable tools for agricultural surveys. Nevertheless, they are not predictive methods. A system able to detect summer crops as early as possible is important in order to obtain valuable information for a better water management strategy. The detection of summer crops before the beginning of the irrigation period is therefore our objective. The study area is located near Toulouse (southwestern France), and is a region of mixed farming with a wide variety of irrigated and non-irrigated crops. Using the reference data for the years concerned, a set of fixed thresholds are applied to a vegetation index (the Normalized Difference Vegetation Index, NDVI) for each agricultural season of multi-spectral satellite optical imagery acquired at decametric spatial resolutions from 2006 to 2013. The performance (i.e., accuracy) is contrasted according to the agricultural practices, the development states of the different crops and the number of acquisition dates (one to three in the results presented here). The detection of summer crops reaches 64% to 88% with a single date, 80% to 88% with two dates and 90% to 99% with three dates. The robustness of this method is tested for several years (showing an impact of meteorological conditions on the actual choice of images), several sensors and several resolutions.

Estimation of sunflower yield using multi-spectral satellite data (optical or radar) in a simplified agro-meteorological model

This paper aims to compare the crop yield retrieval performances, obtained by assimilating the leaf area index derived from multi-temporal satellite signatures (i.e. reflectances and backscattering coefficients) into an agro-meteorological model. The study is based on the Multispectral Crop Monitoring experimental campaign, conducted in 2010 by the CESBIO laboratory. During the agricultural season of sunflower, regular satellite images were quasi-synchronously acquired by 6 sensors (Formosat-2, Spot-4/5, TerraSAR-X, Radarsat-2 and Alos), over a region located in the south west of France. Calibration and validation steps take advantage of the dense network of monitored fields. Among the wide range of the tested image configurations (multi-frequency and multi-polarization), promising results are offered by optical and co-polarized C-band (i.e. HH and VV) data for yield estimate, with correlation superior to 0.74.

Monitoring of RFI localizations for the SMOS mission: Seasonal variations and systematic errors

Artificial sources emitting in the protected part of the L-band are polluting the retrievals of ESAs Soil Moisture and Ocean Salinity (SMOS) satellite. Detection and localization of such sources are of interest for the exploitation of science products as well as for the identification of the emitters. A simple and fast method that provides snapshot-wise information is presented. From a statistical analysis of the results, some systematic errors are reported along with their potential causes and an approach to mitigate them. In the case of sources at high geomagnetic latitudes a seasonal variation of the localization error is also noticed; the origin of such phenomenon is still under investigation.

Estimation of crop parameters using multi-temporal optical and radar polarimetric satellite data

This paper is concerned with the estimation of wheat and rapeseed crops parameters (height, leaf area index and dry biomass), during their whole vegetation cycle, using satellite time series both acquired in optical and microwave domains. Crop monitoring at a fine scale represents an important stake from an environmental point of view as it provides essential information to combine increase of production and sustainable management of agricultural landscapes. The aim of this paper is to compare the potential of optical and SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation. Satellite (Formosat-2, Spot-4/5 and Radarsat-2) and ground data were acquired during the MCM’10 experiment conducted by the CESBIO laboratory in 2010. A vegetation index was derived from the optical images: the NDVI and backscattering coefficients and polarimetric parameters were computed from Radarsat-2 images. Results of this study show the high interest of using SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation during the whole vegetation cycle instead of using optical vegetation index. Polarimetric parameters do not improve wheat parameters estimation (e.g. backscattering coefficient σ° VV corresponds to the best parameter for wheat height estimation (r2 = 0.60)) but show their high potential for rapeseed height and dry biomass monitoring (i.e. Shannon Entropy polarimetry (SEp ; r2 = 0.70) and Radar Vegetation Index (RVI ; r2 = 0.80) respectively).