Remote Sensing of Soil

Remote sensing has shown a high potential in soil characteristics retrieving in the last three decades. Different methodologies have been proposed for the estimation of soil parameters, based on different remote sensing sensors and techniques (passive and active)

Multitemporal observations of sugarcane by TerraSAR-X sensor

International audienceLe potentiel du capteur radar TerraSAR-X (bande X) pour le suivi de la croissance de la canne à sucre a été étudié sur l'ile de la Réunion. Des données multitemporelles du capteur TerraSAR-X acquises à différentes angles d'incidence (17°, 31°, 37°, 58°) et polarisations (HH, HV, VV) ont été analysées dans le but d'étudier le comportement du signal radar en fonction de la hauteur de la canne à sucre. Le potentiel de TerraSAR pour la cartographie de la coupe a également été étudié.Le signal radar augmente rapidement avec la hauteur de la canne jusqu'à une hauteur seuil qui dépend de la polarisation et de l'angle d'incidence. Au delà de ce seuil, le signal croit légèrement ou reste constant. Les résultats montrent également que le signal TerraSAR augmente après des fortes pluies suite à une augmentation de la contribution du sol pour des hauteurs de canne inférieures à 50 cm (2 mois d'age). Finalement, les données TerraSAR à haute résolution spatiale sont très utiles pour le suivi de la coupe quand les champs de canne à sucre sont de petites tailles ou quand la coupe est échelonnée dans le temps. L'incidence radar de 37° est la plus convenable pour le suivi et la cartographie de la coupe de canne. / The potential of TerraSAR-X (X-band) in monitoring sugarcane growth was investigated on Reunion Island. Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height. The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. TerraSAR data showed that after strong rains the soil contribution for the backscattering of sugarcane fields can be consequent for canes with heights of terminal visible dewlap (htvd) less than 50cm (total cane heights around 155cm). Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The radar incidence of 37° is more suitable to monitor the sugarcane harvest

Use of TerraSAR-X data to retrieve soil moisture over bare soil agricultural fields

The retrieval of the bare soil moisture content from TerraSAR-X data is discussed using empirical approaches. Two cases were evaluated: 1) one image at low or high incidence angle and 2) two images, one at low incidence and one at high incidence. This study shows by using three databases collected between 2008 and 2010 over two study sites in France (Orgeval and Villamblain) that TerraSAR-X is a good remote sensing tool for the retrieving of surface soilmoisture with accuracy of about 3% (rmse).Moreover, the accuracy of the soil moisture estimate does not improve when two incidence angles (26◦–28◦ or 50◦–52◦) are used instead of only one. When compared with the result obtained with a high incidence angle (50◦–52◦), the use of low incidence angle (26◦–28◦) does not enable a significant improvement in estimating soil moisture (about 1%)

Semi-empirical calibration of the Integral Equation Model for SAR data in C-band and cross polarization using radar images and field measurements

The estimation of surface soil parameters (moisture and roughness) from Synthetic Aperture Radar (SAR) images requires the use of well-calibrated backscattering models. The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed by Baghdadi et al. (2004 and 2006) for HH and VV polarizations to HV polarization. The approach consisted in replacing the measured correlation length by a fitting/calibration parameter so that model simulations would closely agree with radar measurements. This calibration in C-band covers radar configurations with incidence angles between 24° and 45.8°. Good agreement was found between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM

Desert roughness retrieval using CYGNSS GNSS-R data

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (G). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS G measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coeffcient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness

Control of Vibrations due to Moving Loads on Suspension Bridges

The flexibility and low damping of the long span suspended cables in suspension bridges makes them prone to vibrations due to wind and moving loads which affect the dynamic responses of the suspended cables and the bridge deck. This paper investigates the control of vibrations of a suspension bridge due to a vertical load moving on the bridge deck with a constant speed. A vertical cable between the bridge deck and the suspended cables is used to install a hydraulic actuator able to generate an active control force on the bridge deck. Two control schemes are proposed to generate the control force needed to reduce the vertical vibrations in the suspended cables and in the bridge deck. The proposed controllers, whose design is based on Lyapunov theory, guarantee the asymptotic stability of the system. The MATLAB software is used to simulate the performance of the controlled system. The simulation results indicate that the proposed controllers work well. In addition, the performance of the system with the proposed controllers is compared to the performance of the system controlled with a velocity feedback controller

Analysis of Sentinel-1 radiometric stability and quality for land surface applications

International audienceLand monitoring using temporal series of Synthetic Aperture Radar (SAR) images requires radiometrically well calibrated sensors. In this paper, the radiometric stability of the new SAR Sentinel-1A 'S-1A' sensor was first assessed by analyzing temporal variations of the backscattering coefficient (sigma°) returned from invariant targets. Second, the radiometric level of invariant targets was compared from S-1A and Radarsat-2 "RS-2" data. The results show three stable sub-time series of S-1A data. The first (between 1 October 2014 and 19 March 2015) and third (between 25 November 2015 and 1 February 2016) sub-time series have almost the same mean sigma°-values (a difference lower than 0.3 dB). The mean sigma°-value of the second sub-time series (between 19 March 2015 and 25 November 2015) is higher than that of the first and the third sub-time series by roughly 0.9 dB. Moreover, our results show that the stability of each sub-time series is better than 0.48 dB. In addition, the results show that S-1A images of the first and third sub-time series appear to be well calibrated in comparison to RS-2 data, with a difference between S-1A and RS-2 lower than 0.3 dB. However, the S-1A images of the second sub-time series have sigma°-values that are higher than those from RS-2 by roughly 1 dB

Coupling SAR C-band and optical data for soil moisture and leaf area index retrieval over irrigated grasslands

International audienceThe objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric Synthetic Aperture Radar (SAR) and optical data. A huge dataset of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual- and full-polarizations configurations) and the Normalized Difference Vegetation Index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters (Shannon entropy and Pauli components) on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested.The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. An RMSE for soil moisture estimates of approximately 6 vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately 0.37 m²/m². The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and 1.5 kg/m², respectively (RMSE is of 0.38 kg/m² for BIO and 0.32 kg/m² for VWC). In addition, a high under-estimate was observed for BIO and VWC higher than 2 and 1.5 kg/m², respectively (a bias of -0.65 kg/m² on BIO estimates and -0.49 kg/m² on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm

Bare soil moisture retrieval from multi-temporal X-band TerraSAR-X SAR images

IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015International audienceThe aim of the present study is to analyze the sensitivity of X-band SAR (TerraSAR-X) signals as a function of different physical bare soil parameters (soil moisture, soil roughness), and to evaluate the accuracy of change detection approach proposed for soil moisture estimation. Firstly, we presented a brief description of our ground and satellite database. Secondly, we considered the main results of our statistical analysis of the relationships between radar and soil parameters: soil moisture and different roughness parameters (the rms height, Zs parameter, and a new roughness parameter Zg. Finally, we proposed an algorithm combing multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements for the retrieval of surface soil moisture at a high spatial resolution

Comparison between backscattered TerraSAR signals and simulations from the radar backscattering models IEM, Oh, and Dubois

The objective of this paper is to evaluate on bare soils the surface backscattering models IEM, Oh, and Dubois in X-band. This analysis uses a large database of TerraSAR-X images and in situ measurements (soil moisture and surface roughness). Oh's 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 data and model. The backscattering Integral Equation Model (IEM) model simulates correctly the backscattering coefficient only for rms1.5 cm in using Gaussian function. However, the results are not satisfactory for a use of IEM in the inversion of TerraSAR data. A semi-empirical calibration of IEM was done in X-band. Good agreement was found between the TerraSAR data and the simulations using the calibrated version of the IEM