Open Access
Evaluation of 2D models for the prediction of surface depression storage using realistic reference values
(Wiley, 2016) Giménez Díaz, Rafael; Mezkiritz Barberena, Irantzu; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Martínez de Aguirre Escobar, Alejandro; Casalí Sarasíbar, Javier; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Depression storage (DS) is the maximum storage of precipitation and runoff in the soil surface at a given slope. The DS is determined by soil roughness that in agricultural soils is largely affected by tillage. The direct measurement of DS is not straightforward because of the natural permeability of the soil. Therefore, DS has generally been estimated from 2D/3D empirical relationships and numerical algorithms based on roughness indexes and height measurements of the soil surface, respectively. The objective of this work was to evaluate the performance of some 2D models for DS, using direct and reliable measurements of DS in an agricultural soil as reference values. The study was carried out in experimental microplots where DS was measured in six situations resulting from the combination of three types of tillage carried out parallel and perpendicular to the main slope. Those data were used as reference to evaluate four empirical models and a numerical method. Longitudinal altitudinal profiles of the relief were obtained by a laser profilometer. Infiltration measurements were carried out before and after tillage. The DS was largely affected by tillage and its direction. Highest values of DS are found on rougher surfaces mainly when macroforms cut off the dominant slope. The empirical models had a limited performance while the numerical method was the most effective, even so, with an important variability. In addition, a correct hydrological management should take into account that each type of soil tillage affects infiltration rate differently.
Open Access
On the influence of spatial resolution in soil surface roughness characterization using Tls and Sfm techniques
(IEEE, 2018) Martínez de Aguirre Escobar, Alejandro; Álvarez-Mozos, Jesús; Giménez Díaz, Rafael; Milenković, Milutin; Pfeifer, Norbert; Ingeniería; Ingeniaritza
Soil surface roughness strongly affects the scattering of microwaves and determines the backscattering coefficient observed by SAR (Synthetic Aperture Radar) sensors. The aim of this study is to analyze the influence of the spatial resolution of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) techniques to parameterize surface roughness over agricultural soils. Three experimental plots (5 x 5 meters) representing different roughness conditions were measured by TLS and SfM techniques. Roughness parameters (s and l) were calculated from profiles obtained at different spatial resolutions in parallel and in perpendicular to the tillage direction on each plot. The results showed minor differences in the parameters values between both techniques and, in general, a decreasing trend and an increasing trend for lower spatial resolutions for parameter s and l, respectively.
Open Access
On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation
(Elsevier, 2018) Loizu Maeztu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Tarpanelli, Angelica; Brocca, Luca; Casalí Sarasíbar, Javier; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve stream flow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the in fluence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03–0.15) and efficiency indices (10–45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve stream flow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required.
Open Access
The added value of stratified topographic correction of multispectral images
(MDPI, 2016) Sola Torralba, Ion; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.
Open Access
Evaluación multitemporal de métodos de corrección topográfica mediante el uso de imágenes sintéticas multiespectrales
(Asociación Española de Teledetección, 2014) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Torres Escribano, José Luis; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
En este trabajo se presentan los resultados de la evaluación multitemporal de varios métodos de corrección topográfica (TOC), cuya bondad se determina de forma cuantitativa mediante el uso de imágenes sintéticas multiespectrales simuladas para diferentes fechas de adquisición a lo largo del año. Para cada fecha se generan dos imágenes sintéticas, una considerando el relieve real (imagen SR), y otra el relieve horizontal (imagen SH). Las imágenes SR se corrigen utilizando distintos TOC y estas imágenes corregidas se comparan con la corrección ideal (imagen SH) mediante el índice de similitud estructural (SSIM). Los valores de SSIM nos permiten evaluar la eficacia de cada corrección para distintas fechas, es decir, para distintos ángulos de elevación solar.
Open Access
On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery
(MDPI, 2016) Larrañaga Urien, Arantzazu; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45º), and also to configurations where several polarimetric features (Pauli and Cloude–Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45º bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.
Open Access
Validation of a simplified model to generate multispectral synthetic images
(MDPI, 2015) Sola Torralba, Ion; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from an area under some specific conditions. TOC algorithms were then applied to synthetic scenes and the resulting corrected scenes were compared with a horizontal synthetic scene free of topographic effect. This comparison enabled an objective and quantitative evaluation of TOC algorithms. This approach showed promising results but had some shortcomings that are addressed herein. First, the model, originally built to simulate only broadband panchromatic scenes, is extended to multispectral scenes in the visible, near infrared (NIR), and short wave infrared (SWIR) bands. Next, the model is validated by comparing synthetic scenes with four Satellite pour l'Observation de la Terre 5 (SPOT5) real scenes acquired on different dates and different test areas along the Pyrenees mountain range (Spain). The results obtained show a successful simulation of all the spectral bands. Therefore, the model is deemed accurate enough for its purpose of evaluating TOC algorithms.
Open Access
Synthetic images for evaluating topographic correction algorithm
(IEEE, 2013) Sola Torralba, Ion; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Torres Escribano, José Luis; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Gobierno de Navarra / Nafarroako Gobernua
In the last years, many topographic correction (TOC) methods have been proposed to correct the illumination differences between the areas observed by optical remote sensors. Although the available number of TOC methods is high, the evaluation of their performance generally relies on the existence of precise land-cover information, and a standardized and objective evaluation procedure has not been proposed yet. In this paper, we propose an objective procedure to assess the accuracy of these TOC methods on the basis of simulated scenes, i.e., synthetically generated images. These images represent the radiance an optical sensor would receive under specific geometric and temporal acquisition conditions and assuming a certain land-cover type. A simplified method for creating synthetic images using the stateof- the-art irradiance models is proposed, both considering the real topography of a certain area [synthetic real (SR) image] or considering the relief of this area as being completely flat [synthetic horizontal image (SH)]. The comparison between the corrected image obtained by applying a TOC method to the SR and SH images of the same area, allows assessing the performance of each TOC algorithm. This comparison is quantitatively carried out using the structural similarity index. The proposed TOC evaluation procedure is applied to a specific case study in northern Spain to explain its implementation and demonstrate its potential. The procedure proposed in this paper could be also used to assess the behavior of TOC methods operating under different scenarios considering diverse topographic, geometrical, and temporal acquisition configurations.
Open Access
Multitemporal evaluation of topographic correction algorithms using synthetic images
(SPIE, 2012) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Torres Escribano, José Luis; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
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 corrected image obtained applying a TOC method to SR image and SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm.
Open Access
Inter-comparison of atmospheric correction methods on Sentinel-2 images applied to croplands
(IEEE, 2018) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Ingeniería; Ingeniaritza
Atmospheric correction of high resolution satellite scenery is a necessary preprocessing step for applications where bottom of atmosphere (BOA) reflectances are needed. The selection of the best atmospheric correction method to use on images acquired from new platforms, such as Sentinel-2, is essential to provide accurate BOA reflectances. In this work the performance of three atmospheric correction methods (6S, MAJA and SEN2COR) applied to Sentinel-2 scenes are compared by evaluating the resultant spectral signatures of six crop types on two specific dates, and their NDVI time series along a complete year. Although SEN2COR introduced greater corrections, especially in the infrared bands, the results suggest a varying performance of the methods depending on the land cover and the atmospheric conditions. Further research, particularly incorporating ground truth data, is recommended to rigorously validate the different atmospheric methods.