Prof. Jesús Álvarez-Mozos Profile

Prof. Jesús Álvarez-Mozos

Assistant Professor at Univ Pública de Navarra

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 8 November 2012 Paper

Multitemporal evaluation of topographic correction algorithms using synthetic images

I. Sola, M. González de Audícana, J. Álvarez-Mozos, J. Torres

Proceedings Volume 8537, 853706 (2012) https://doi.org/10.1117/12.974535

KEYWORDS: Sensors, Reflectivity, Remote sensing, Quantitative analysis, Optical sensors, Visualization, Scene simulation, Image processing, Spatial resolution, Image quality

Read Abstract +

Land cover classification and quantitative analysis of multispectral data in mountainous regions is considerably hampered by the influence of topography on the spectral response pattern. In the last years, different topographic correction (TOC) algorithms have been proposed to correct illumination differences between sunny and shaded areas observed by optical remote sensors. Although the available number of TOC methods is high, the evaluation of their performance usually relies on the existence of precise land cover information, and a standardised and objective evaluation procedure has not been proposed yet. Besides, previous TOC assessment studies only considered a limited set of illumination conditions, normally assuming favourable illumination conditions. This paper presents a multitemporal evaluation of TOC methods based on synthetically generated images in order to evaluate the influence of solar angles on the performance of TOC methods. These synthetic images represent the radiance an optical sensor would receive under specific geometric and temporal acquisition conditions and assuming a certain land-cover type. A method for creating synthetic images using state-of-the-art irradiance models has been tested for different periods of the year, which entails a variety of solar angles. Considering the real topography of a specific area a Synthetic Real image (SR) is obtained, and considering the relief of this area as being completely flat a Synthetic Horizontal image (SH) is obtained. The comparison between the corrected image obtained applying a TOC method to a SR image and the SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm. This performance is quantitatively measured through the widely accepted Structural Similarity Index (SSIM) on four selected TOC methods, assessing their behaviour over the year. Among them, C- Correction has ranked first, giving satisfying results in the majority of the cases, while other algorithms show a good performance in summer but give worse results in winter.

Proceedings Article | 20 October 2005 Paper

Radar based surface soil moisture retrieval through the combined use of two backscattering models

Jesús Álvarez-Mozos, Niko Verhoest, Javier Casali, María González-Audícana

Proceedings Volume 5976, 59761O (2005) https://doi.org/10.1117/12.627513

KEYWORDS: Soil science, Backscatter, Surface roughness, Radar, Dielectrics, Error analysis, Agriculture, Sensors, Remote sensing, Polarization

Read Abstract +

Proceedings Article | 26 October 2004 Paper

Surface soil moisture retrieval on agricultural catchments of Navarre, Spain through RADARSAT-1 SAR data: first results

Jesus Alvarez, Niko Verhoest, Javier Casali, Maria Gonzalez-Audicana, Jose Lopez

Proceedings Volume 5568, (2004) https://doi.org/10.1117/12.565239

KEYWORDS: Soil science, Vegetation, Surface roughness, Backscatter, Synthetic aperture radar, Agriculture, Clouds, Sensors, Remote sensing, Microwave radiation

Read Abstract +

The importance of soil moisture on many scientific fields like hydrology, meteorology, crop growth or soil erosion has been addressed frequently. Its characterisation has been a difficult task because of its high spatial and temporal variability. Several point based measurement techniques have been developed with different degree of success, but their conversion to spatially distributed values depends on complex geostatistical techniques. Furthermore, sensor installation and maintenance can be quite tedious. In this background, SAR remote sensing sensors provide valuable information on land surface parameters. The backscattering of the SAR signal depends amongst others on the dielectric constant of the observed surface, which is mainly related to the soil surface water content. It also gives spatially distributed information with a resolution adequate for different spatial scales: from medium or small watersheds to agricultural fields. Its periodicity can be appropriate for calibrating, on a monthly basis, the simulations of distributed hydrologic modelling tools. The present paper reports the first results of an ongoing research of which the main objective is the development of a simple methodology for the calibration of the soil moisture component of distributed hydrological models using SAR data. Five RADARSAT-1 images, acquired between 27/02/2003 and 02/04/2003 over the Navarre region (Northern Spain) have been processed. The calculated backscattering values have been compared to soil moisture and surface roughness ground measurements. Empirical linear regression models have been fitted at three different scales: point scale, field scale and catchment scale, showing acceptable correlation between calculated backscattering values and ground measured soil moisture specially at field and watershed scale. However, consistent trends have not been found probably due to differing local conditions such as surface roughness or vegetation cover. Seeking for a more consistent approach, the physically based Integral Equation Method (IEM) model has been applied. Yet, simulations run by the IEM have not been completely successful probably due to an inadequate characterisation of surface roughness.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years