Frédéric Baup

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.

Radar Signatures of Sahelian Surfaces in Mali Using ENVISAT-ASAR Data

This paper presents an analysis of ENSIVAT advanced synthetic aperture radar data acquired over a Sahelian region located in Mali, West Africa. The considered period is 2004-2005 and includes two rainy seasons. Emphasis is put on two ScanSAR modes, namely, the global monitoring (GM) and the wide swath (WS) modes characterized by spatial resolutions of about 1 km and 150 m, respectively. Results show that the WS mode offers better performance in terms of radiometric resolution, radiometric stability, and speckle reduction than the GM mode. The latter is more appropriate for studies at large scale (> 10 times 10 km). In both modes, pronounced angular and temporal signatures are observed for most soil surfaces, and azimuthal effects are observed on markedly orientated rocky surfaces. In contrast, polarization differences (VV/HH) are small during the dry season except on flat loamy soil surfaces. Finally, a relationship is observed between the normalized WS backscattering signal at HH polarization and the surface soil moisture of sandy soils.

Evaluation of Radar Backscattering Models IEM, Oh, and Dubois for SAR Data in X-Band Over Bare Soils

The objective of this letter is to evaluate the surface radar backscattering models, namely, integral equation model (IEM), Oh, and Dubois, for synthetic aperture radar data in X-band over bare soils. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture “mv” and surface roughness “ h_rms”). Ohs model correctly simulates the radar signal for HH and VV polarizations, whereas the simulations performed with the Dubois model show a poor correlation between TerraSAR-X data and model. The backscattering IEM simulates correctly the backscattering coefficient only for h_rms <; 1.5 cm in using an exponential correlation function and for h_rms >; 1.5 cm in using Gaussian function. However, the results are not satisfactory for the use of IEM in the inversion of TerraSAR-X data. A semiempirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR-X data and the simulations using the calibrated version of the IEM.

Detection of Soil Moisture Variations Using GPS and GLONASS SNR Data for Elevation Angles Ranging From 2° to 70°

We propose a Global Navigation Satellite System-Reflectometry (GNSS-R) interference pattern technique method to estimate the temporal variations of the soil moisture content of the ground surrounding a single geodetic antenna. Three parameters can be inverted from GNSS signal-to-noise ratio (SNR) acquisitions: amplitude/phase of the multipath contribution to SNR and effective antenna height. Our method is applied to determine the surface moisture of a bare soil at Lamasquère, France, from February 5 to March 15, 2014. First, only data from low satellite elevation angles (<; 30°) are taken into consideration and are compared with independent 2-cm depth soil moisture records. The combination of the measurements from all GPS satellites, tested for the first time, improves the quality of the results with a correlation coefficient reaching 0.95, with a 10-min sampling rate. Our study shows that it is also possible to take high satellite elevation angles into account, even if the sign of the correlation appears to be reversed w.r.t. data from low satellite elevation angles. The cutoff angle where the sign of the correlation reverses seems to be around 30°. With regard to the effective antenna height, only a very low correlation is observed for high satellite elevation angles. We propose a new inversion method taking the pseudo-dynamic of the surface into account, which increases the correlation from 0.39 to 0.82. By normalizing and inverting the time series obtained from either low or high satellite elevation angles, it is possible to combine them, which enhances the results (correlation = 0.95).

A new empirical model for radar scattering from bare soil surfaces

The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from synthetic aperture radar (SAR) images and in situ soil surface parameter measurements (moisture content and roughness) is used. The retrieval of soil parameters from SAR images remains challenging because the available backscattering models have limited performances. Existing models, physical, semi-empirical, or empirical, do not allow for a reliable estimate of soil surface geophysical parameters for all surface conditions. The proposed model, developed in HH, HV, and VV polarizations, uses a formulation of radar signals based on physical principles that are validated in numerous studies. Never before has a backscattering model been built and validated on such an important dataset as the one proposed in this study. It contains a wide range of incidence angles (18-57) and radar wavelengths (L, C, X), well distributed, geographically, for regions with different climate conditions (humid, semi-arid, and arid sites), and involving many SAR sensors. The results show that the new model shows a very good performance for different radar wavelengths (L, C, X), incidence angles, and polarizations (RMSE of about 2 dB). This model is easy to invert and could provide a way to improve the retrieval of soil parameters.

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.

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.

Microwave electromagnetic modelling of Sahelian grassland

In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the radiative transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with Envisat Advanced Synthetic Aperture Radar (ASAR) data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in the African Monsoon Multidisciplinary Analysis (AMMA) project, is selected as the test target and a set of ground data was collected during 2004 and 2005. Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches Envisat measurements well (correlation = 0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence on soil moisture variations. The analysis of the different contributions leads to a study of the main scattering mechanisms. For high incidence angles, the backscattering coefficient at HH polarization shows a marked seasonal variation associated with grass presence.

Detection of soil moisture content changes by using a single geodetic antenna: The case of an agricultural plot

As multipaths still represent a major problem for reaching precise GNSS positioning, the mitigation of their influence has been widely investigated. However, previous studies have lately proposed to use these interferences of GNSS electromagnetic waves to estimate parameters related to the reflecting surface (e.g., antenna heights, rugosity,...). Variations of the nature of the surface is likely to modify the properties of the reflected waves, and consequently lead to variations of amplitude / phase of the signal-to-noise ratio (SNR), e.g. recorded at 1 Hz by a GNSS receiver. By analyzing the time variations of SNR measurements linked to the dielectric constant of the surrounding soil, we use a method to recover the local fluctuations of the soil moisture content. It is simply based on the obvious linear correlation between SNR amplitude / phase and retrieved antenna height time series and independent measurements of humidity probe at 2 and 5 cm depths. This method of combination is applied to determine soil moisture in a corn and soya field at Lamasquère, France, for 21 successive days. Results show a good correlation (e.g. 0.96 with GPS PRN-01 satellite) between SNR inversion and humidity probes for most satellites.

Application of a coherent modeling on Sahelian grassland

The validity of a coherent Sahelian-grassland scattering model is determined by comparing the model predictions with satellite measurements of a representative site. This model considers the realistic botanical structure of grassland. The site Agoufou, located in the Northern Mali, was selected as the test target. This site is governed by a semi-arid tropical climate. Its vegetation is mainly composed of shrubs and annual grass. HH polarization backscattering data was collected over an entire growing season at different incidence angles by means of the ENVISAT ASAR. Simulations provided by the coherent model show a good agreement with measured data having a correlation coefficient equal to 0.92. Model predictions show that the HH polarization component is higher than the W polarization component during all growing season. Significant parameters are shown to be the grass density, the soil moisture content and the grass moisture content. The most sensitive parameter is the ground soil moisture content. Moreover, it is observed that the variation of the backscattering coefficient for all parameters can be represented by a linear regression function.