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Canadian Journal of Remote Sensing, 2002
This paper is an overview of the most recent results obtained by Italian groups involved in the spaceborne imaging radar-C,X-band synthetic aperture radar (SIR-C/X-SAR) hydrological experiment, by using multi-frequency and multi-polarization synthetic aperture radar (SAR) data measured by JPL/AIRSAR, SIR-C, EMISAR, ERS-1, and JERS-1 sensors. The sensitivity of backscattering coefficients to some geophysical parameters that play a significant role in hydrological processes, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Experimental results show that P band appears to be suitable for the monitoring of forest biomass, whereas L band is mainly sensitive to the biomass of wide-leaf crops and C band to narrow-leaf crops. Moreover, the L-band sensor gives the highest contribution in estimating soil moisture and surface roughness. The sensitivity of backscatter to soil moisture and surface roughness for individual agricultural fields is rather low, since both parameters affect the radar signal. However, by observing data collected at different dates and averaged over a relatively wide agricultural area, the correlation with soil moisture becomes considerable, since the effects of spatial roughness variations are smoothed. Retrievals of both soil moisture and surface roughness were performed using a semi-empirical model. Résumé. Cet article fait le bilan des résultats les plus récents obtenus par les groupes italiens impliqués dans l'expérience hydrologique SIR-C/X-SAR basée sur l'utilisation de données RSO multi-fréquences et multi-polarisations acquises par les capteurs JPL/AIRSAR, SIR-C, EMISAR, ERS-1 et JERS-1. La sensibilité des coefficients de rétrodiffusion à certains paramètres géophysiques jouant un rôle significatif dans les processus hydrologiques, comme la biomasse végétale, l'humidité du sol et la rugosité, est discutée. Les résultats expérimentaux montrent que la bande P semble appropriée pour le suivi de la biomasse forestière, alors que la bande L est plutôt sensible à la biomasse des cultures à feuille large et la bande C aux cultures à feuille étroite. Par ailleurs, la performance du capteur en bande L s'avère supérieure dans l'estimation de l'humidité du sol et de la rugosité de surface. La sensibilité de la rétrodiffusion à l'humidité du sol et à la rugosité de surface dans le cas des champs agricoles individuels est plutôt faible étant donné que ces deux paramètres affectent le signal radar. Toutefois, en observant les données acquises à différentes dates et moyennées par rapport à une zone agricole plus étendue, la corrélation avec l'humidité du sol devient considérable puisque les effets des variations spatiales de la rugosité sont lissés. Enfin, l'extraction de l'humidité du sol et de la rugosité de surface a été accomplie à l'aide d'un modèle semiempirique.
Microwave remote sensing of land
2004
Considering the rapid growth of population, its impact on the environment, and limited available resources on our planet, the need for monitoring the environmental processes and managing our resources is unequivocal. Microwave remote sensing provides a unique capability towards achieving this goal. Over the past decade, significant progress has been made in microwave remote sensing of land processes through development of advanced airborne and space-borne microwave sensors and the tools, such as physics-based models and advanced inversion algorithms, needed for analysing the data. These activities have sharply increased in recent years since the launch of ERS -1/2, JERS-1 and RADARSAT satellites, and with the availability of radiometric data from SSM/I. A new era has begun by the recent space missions ESA-ENVISAT, NASA-AQUA and NASDA-ADEOSII and the upcoming PALSAR and RADARSAT2 missions, which open new horizons for a wide range of operational microwave remote sensing applications. This paper highlights major activities and important results achieved in this area over the past years.
Advances in Space Research, 2007
The goal of this study was to extract from dual-frequency satellite SAR signatures consistent information about moisture in soils and about various features of plants for analyzing crop growth conditions in any agricultural region. The study was carried out on Polish agricultural regions but it is hoped that it will be applicable anywhere on the planet. During a satellite overpass on a particular date, the ground-based measurements required such as soil moisture (SM), Leaf Area Index (LAI), and biomass were collected from 10 to14 May 1998. The backscattering coefficients at various frequencies were collected from ERS-2.SAR (C-VV) on May 10, 1998 and from JERS-SAR (L-HH) on May 14, 1998. The applicability of three different vegetation descriptors to the semi-empirical water-cloud model was investigated. The contribution to the backscatter values of vegetation features such as leaf area expressed in the Leaf Area Index and the dielectric properties of leaf surface expressed in the Leaf Water Area Index (LWAI) and the Vegetation Water Mass (VWM) was examined in order to reveal the best fit of the model. It was found that in C-band, which had an incidence angle of 23°, the soil moisture contribution to the sigma value was predominant over the vegetation contribution. When the canopy cover increases, the sensitivity of a radar signal to dry soil conditions (SM < 0.1) decreased. The sigma value was the most sensitive to vegetation descriptor VWM which described the amount of water in vegetation. Attenuation of soil signal by the canopy was found in all three vegetation descriptors types; the strongest attenuation effect was observed in the case of VWM. In L-band (where the incidence angle was 35°), the dominant signal to total r o value comes from volume scattering of vegetation for LAI > 3. When LAI < 3 the vegetation contribution to total r o value appeared in two-way attenuation. The results gave us the possibility of comparing the modeled with the measured soil and vegetation parameters.
Water, 2017
The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model "IEM", Oh, Dubois, and Advanced Integral Equation Model "AIEM") using a wide dataset of SAR (Synthetic Aperture Radar) data and experimental soil measurements. These forward models reproduce the radar backscattering coefficients ) from soil surface characteristics (dielectric constant, roughness) and SAR sensor parameters (radar wavelength, incidence angle, polarization). The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness). Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB) and better correlation with real data in VV polarization (RMSE = 2.9 dB). The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM). Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B) with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B) is the most adequate to estimate soil moisture and roughness from SAR data.
Journal of Hydrology, 2006
In this study, the spatial distribution of soil water content in an agricultural area of 30 km 2 in Southern Italy has been estimated by using high-resolution space-borne Synthetic Aperture Radar data. Multi-polarised SAR images acquired during the SIR-C mission in April 1994 have been analysed by using the semi-empirical surface backscattering model derived by Oh, Y., Sarabandi K., Ulaby F.T., 1992. An empirical model and an inversion technique for radar scattering from bare soil surface. IEEE Trans. Geosci. Remote Sensing, 30(2), 370381. A site-specific calibration procedure of the cited model has been proposed to derive soil dielectric constant values without a-priori information on the surface roughness by using ground measurements on a regular grid in two bare-soil fields. The calibrated model applied to L-band data reproduced quite satisfactorily the spatial variability of the soil dielectric constant in the two fields. Diversely, C-band data gave poor results. Successively, the calibrated Oh's model was applied to estimate the soil dielectric constant in bare soil and low vegetation fields of the entire irrigation district, where the output of a distributed simulation model of soil water balance were available. From the comparison between the Oh's backscattering model and the soil water balance model, it was confirmed that, under bare soil conditions, the values of soil water content near the soil surface estimated from SIR-C L-band data differ by G20% from the simulated ones. Furthermore, as expected, the presence of a fractional vegetation cover, even if small, reduced the sensitivity of radar backscattering to soil moisture. The results of this study confirmed that L-band SAR data represent a minimum requirement for possible assimilation schemes in regional hydrological modelling.
Vadose Zone Journal, 2010
The study aimed to analyze the spa al variability of surface soil moisture at diff erent spa al scales based on fi eld measurements and remote sensing es mates. Mul temporal Envisat satellite Advanced Synthe c Aperture Radar (ASAR) data were used to derive the surface soil moisture u lizing an empirical C-band retrieval algorithm. Eight wide-swath (WS) images with a spa al resolu on of 150 m acquired between February and October 2008 were used to determine the surface soil moisture contents. The accuracy of the surface soil moisture retrievals was evaluated by comparison with in situ measurements. This comparison yielded a root mean square error of 5% (v/v). Based on our in situ measurements as well as remote sensing results, the rela onship of the coeffi cient of varia on of the spa al soil moisture pa erns and the mean soil moisture was analyzed at diff erent spa al scales ranging from the catchment scale to the fi eld scale. Our results show that the coeffi cient of varia on decreases at all scales with increasing soil moisture. The gain of this rela onship decreases with scale, however, indica ng that at a given soil moisture state, the spa al varia on at the large scale of whole catchments is larger than at the fi eld scale. Knowledge of the spa al variability of the surface soil moisture is important to be er understand energy exchange processes and water fl uxes at the land surface as well as their scaling proper es.
Model investigation about the potential of C band SAR in herbaceous wetlands flood monitoring
International Journal of Remote Sensing, 2008
Wetlands are areas where the presence of water at or near the soil surface drives the natural system. Imaging radars (SARs) have distinct characteristics which make them of significant value for monitoring and mapping wetland inundation dynamics. The presence or absence of water (which has a much higher dielectric constant than dry or wet soil) in wetlands may significantly alter the signal detected from these areas depending on the dominant vegetation type, density, and height. The objective of this paper is to present our current research efforts to explain and correctly simulate the radar response of wetland vegetation/inundation mixtures, and use simulations as an aid for retrieval applications. The radar response of junco marshes under different flood conditions and vegetation stages is analysed using a set of 13 multipolarization ENVISAT ASAR scenes acquired over the Paraná River Delta marshes during the period 2003–2005. The main aspect of the approach followed is the simulation of SAR wave interactions with vegetation and water, using an adapted and improved version of the EM model developed at Tor Vergata University. The results obtained indicate that with the refined EM model, it is possible to represent with a good accuracy VV and HH SAR responses of junco marshes for a variety of environmental conditions. Further work and data are needed to explain measured HV backscattering. The general agreement obtained between simulations and observations permitted the development of a simple retrieval scheme, and estimates of water level below the canopy were obtained for different environmental conditions. RMS errors of forward simulations and retrievals are reported and discussed.
Estimation of forest parameters using CARABAS-II VHF SAR data
IEEE Transactions on Geoscience and Remote Sensing, 2000
The use of airborne CARABAS-II VHF (20-90 MHz) SAR data for retrieval of forest parameters has been investigated. The investigation was performed at a test site located in the southwest of Sweden consisting mainly of Norway spruce forests. Regression models predicting forest parameters from radar backscattering amplitude were developed and evaluated. The results showed a linear relationship between backscattering amplitude and forest stem volume, stem diameter, and tree height. The analysis also showed that the radar signal is strongly affected by ground slope conditions. The root mean square errors from the regression analysis, restricted to forest stands on near-horizontal ground, were found to be 66 m 3 ha 1 , 3.2 cm, and 2.3 m for stem volume, stem diameter, and tree height respectively. No saturation of the backscattered signal was observed up to the maximum stem volume of 625 m 3 ha 1 , corresponding to a biomass of 375 tons ha 1. The results imply that VHF SAR data have significant potential for operational use in forestry.