Papers by María González de Audícana Amenábar
EGU General Assembly Conference Abstracts, May 1, 2010
Atmospheric correction of high resolution satellite scenery is a necessary preprocessing step for... more 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 (SEN2COR, MAJA and 6S) applied to Sentinel-2 scenes are compared by evaluating the resultant spectral signatures of six crop types on a single date, 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.
Remote Sensing
The aim of this study was to compare the available tools in R for downloading and processing Mode... more The aim of this study was to compare the available tools in R for downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) data, specifically the Enhanced Vegetation Index (EVI) product. The R tools evaluated were the MODIS package, RGISTools, MODISTools, R Google Earth Engine (RGEE) package, MODIStsp, and the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Each tool was used to download the same product (EVI) corresponding to the same day (3 December 2015), and downloaded data were used to analyze the urban growth of Tarija (Bolivia) as an interesting application. The following features were analyzed: download time and memory used during the download, additional post-processing time, local memory occupied on the computer, and downloaded file formats. Results showed that the most efficient R tools were those that work directly in the “cloud” or use text queries (RGEE and AppEEARS, respectively) and provide, as a final p...
A New Methodology for Wheat Attenuation Correction at C-Band VV-Polarized Backscatter Time Series
IEEE Transactions on Geoscience and Remote Sensing
One of the aims of our research team is to develop algorithms to assign automatically a crop to a... more One of the aims of our research team is to develop algorithms to assign automatically a crop to a cadastral parcel, matching raster information (multispectral classified images) and vectorial information (polygons defining parcel borderlines). The use of multispectral images with high spatial resolution would assist these assignations. In this work, new image-fusion methods are presented and described. These methods, based on the use of the discrete wavelet transform (DWT), are improved alternatives of the standard IntensityHue-Saturation (IHS) or Principal Component Analysis (PCA) mergers. Quantitative indicators have been used to assess the spectral and spatial quality of the images resulting when IHS PCA standard mergers and IHS PCA improved mergers are used to fuse SPOT images. Finally, the utility of these merged images for obtaining “crop distribution maps” via a supervised classification has also been tested. We used ground data to classify the original and the merged images,...
Remote Sensing, 2021
The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fra... more The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m2 surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R2 = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of al...
Resumen . Probablemente, los algoritmos de Mallat y el ‘ a trous ’ sean los algoritmos de transfo... more Resumen . Probablemente, los algoritmos de Mallat y el ‘ a trous ’ sean los algoritmos de transformacion wavelet discreta mas empleados en el ambito de la fusion de imagenes. Cada uno, con distintas propiedades matematicas, conduce a distintas descomposiciones y por lo tanto, a distintas imagenes fusionadas. En este trabajo se comparan ambos algoritmos, analizando la calidad espectral y espacial de imagenes QuickBird fusionadas obtenidas aplicando cada uno de ellos. A pesar de que desde el punto de vista teorico el algoritmo ‘a trous’ es menos adecuado que el de Mallat para extraer detalle espacial en el ambito del analisis multirresolucion, este ha permitido obtener imagenes con una calidad global sensiblemente mayor que el de Mallat.
Sentinel-1 and Sentinel-2 Based Crop Classification Over Agricultural Regions of Navarre (Spain)
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
The objective of this article is to compare the classification accuracies obtained using Sentinel... more The objective of this article is to compare the classification accuracies obtained using Sentinel-1 and Sentinel-2 data and to evaluate the eventual benefits of their combination, as a means to support Common Agricultural Policy controls. With this aim, data from two contrasting agricultural regions of Navarre (Spain) for year 2017 were used. The available Sentinel-1 and Sentinel-2 scenes were processed and a sample of farmers' CAP declarations and field inspections were used for training and testing a Random Forests classifier. Results showed a slightly better performance of Sentinel-2 data, which improved ~5% when combining both data types. A classification based on a selection of features (NDVI, Sentinel-2 B11 and VV backscatter) performed almost the same with a much smaller computational cost.
Revista de Teledetección, 2016
en el óptico en alta resolución espacial. Mientras que los productos de imagen a Nivel 1, radianc... more en el óptico en alta resolución espacial. Mientras que los productos de imagen a Nivel 1, radiancias geo-referenciadas a nivel de sensor, se encuentran en una fase avanzada de desarrollo existiendo para ello un contrato industrial, los productos de Nivel 2 deben ser desarrollados por los propios usuarios. Este hecho limita el uso de las imágenes a la comunidad científica, restringiendo sus posibles aplicaciones fuera de ésta. Así pues, bajo el marco de un proyecto coordinado y motivados por ofrecer productos de Ingenio/SEOSAT de Nivel 2 a disposición de cualquier usuario, se origina y desarrolla este trabajo. En este artículo se presentan los diferentes procesos desarrollados para la elaboración de productos a Nivel 2, desde reflectividades en superficie a la resolución nominal del sensor hasta imágenes con información espacial realzada y la posibilidad de crear mosaicos espaciales y compuestos temporales. Por una parte, en el caso de los productos de reflectividad en superficie se propone una técnica de corrección atmosférica basada en el uso de la información espacial, previo enmascaramiento de las nubes y una exhaustiva corrección de sombras morfológicas y/o topográficas. Por otra parte, para el realce de la información espacial, han sido evaluados diferentes métodos basados en la fusión de bandas multiespectrales con una banda pancromática así como la aplicación de técnicas llamadas de "Super-resolución". Finalmente, se proporcionan las herramientas necesarias para la realización de mosaicos tanto espaciales como temporales para todo tipo de usuarios interesados en la explotación de las imágenes.
RADARSAT based surface soil moisture retrieval on agricultural catchments of navarre (Spain)
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004
ABSTRACT The present paper reports the first results of an ongoing research whose main objective ... more ABSTRACT The present paper reports the first results of an ongoing research whose main objective is the development of a simple methodology for initialising and updating the soil moisture component of distributed hydrological models using SAR data. Five RADARSAT-1 images acquired between 27 February 2003 and 2 April 2003 over the Navarre region (Northern Spain) have been processed. Soil moisture, surface roughness and crop parameters have been monitored coinciding with image acquisition dates over La Tejeria experimental watershed (Navarre). Calculated backscattering values have been compared to soil moisture and surface roughness ground measurements fitting empirical linear regression models. In the case of vegetated fields a semi-empirical water cloud model has been applied to account for vegetation effects on the observed backscattering values. Acceptable correlation has been observed between calculated backscattering values and ground measured soil moisture at field and watershed scale, although variability was high between fields belonging to the same vegetation and roughness classes. The physically based Integral Equation Method (IEM) model has been applied seeking for a more consistent approach. IEM reflected observed backscattering trends. However, dispersion was high probably due to an inadequate characterisation of surface roughness variability
Revista de Teledetección, 2014
Resumen: En este trabajo se presentan los resultados de la evaluación multitemporal de varios mét... more Resumen: 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.
Journal of Agricultural, Biological, and Environmental Statistics, 2006
International Journal of Remote Sensing, 2008
At present, radar-based surface soil moisture (SM) retrieval is hampered by the influence of surf... more At present, radar-based surface soil moisture (SM) retrieval is hampered by the influence of surface roughness on the backscattering coefficient (s 0). Surface roughness is typically represented by two parameters, namely the standard deviation of surface heights (s) and the surface correlation length (l). The latter is a very problematic parameter, since it is extremely variable and very difficult to measure adequately. Therefore, several authors proposed calibrating it using backscattering models yielding optimum or effective l values. Baghdadi et al. found that those effective l were related to the parameter s and the configuration of the sensor, and proposed an approach to calculate it. The objective of this study is to evaluate the validity of that approach using data acquired on a complementary test site. RADARSAT-1 scenes acquired over an experimental watershed are used. Soil moisture and surface roughness parameters were measured in detail, coinciding with satellite overpasses. The effective l values calculated from the equations of Baghdadi et al. (2006) are used to perform forward and inverse simulations using the Integral Equation Model that are compared with radar observations and ground measurements of SM. The results obtained highlight the potential of the evaluated approach towards an operational radar based soil moisture estimation.
International Journal of Remote Sensing, 2005
In the last few years, several researchers have proposed different procedures for the fusion of m... more In the last few years, several researchers have proposed different procedures for the fusion of multispectral and panchromatic images based on the wavelet transform, which provide satisfactory high spatial resolution images keeping the spectral properties of the original multispectral data. The discrete approach of the wavelet transform can be performed with different algorithms, Mallat's and the 'à trous' being the most popular ones for image fusion purposes. Each algorithm has its particular mathematical properties and leads to different image decompositions. In this article, both algorithms are compared by the analysis of the spectral and spatial quality of the merged images which were obtained by applying several wavelet based, image fusion methods. All these have been used to merge Ikonos multispectral and panchromatic spatially degraded images. Comparison of the fused images is based on spectral and spatial characteristics and it is performed visually and quantitatively using statistical parameters and quantitative indexes. In spite of its a priori lower theoretical mathematical suitability to extract detail in a multiresolution scheme, the 'à trous' algorithm has worked out better than Mallat's algorithm for image merging purposes.
IEEE Transactions on Geoscience and Remote Sensing, 2006
The present paper focuses on the ability of currently available RADARSAT-1 data to estimate surfa... more The present paper focuses on the ability of currently available RADARSAT-1 data to estimate surface soil moisture over an agricultural catchment using the theoretical integral equation model (IEM). Five RADARSAT-1 scenes acquired over Navarre (north of Spain) between February 27, 2003 and April 2, 2003 have been processed. Soil moisture was measured at different fields within the catchment. Roughness measurements were collected in order to obtain representative roughness parameters for the different tillage classes. The influence of the cereal crop that covered most of the fields was taken into account using the semiempirical water cloud model. The IEM was run in forward and inverse mode using vegetation corrected RADARSAT-1 data and surface roughness observations. Results showed a great dispersion between IEM simulations and observations at the field scale, leading to inaccurate estimations. As the surface correlation length is the most difficult parameter to measure, different approaches for its estimation have been tested. This analysis revealed that the spatial variability in the surface roughness parameters seems to be the reason for the dispersion observed rather than a deficient measurement of the correlation length. At the catchment scale, IEM simulations were in good agreement with observations. The error values obtained in the inverse simulations were in the range of in situ soil moisture measuring methods (0.04 cm 3 cm 3). Taking into account the small size of the catchment studied, these results are encouraging from a hydrological point of view.
IEEE Transactions on Geoscience and Remote Sensing, 2005
Usual image fusion methods inject features from a high spatial resolution panchromatic sensor int... more Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method.
Biosystems Engineering, 2005
Surface soil moisture is a variable of great importance in several agronomic, hydrological and me... more Surface soil moisture is a variable of great importance in several agronomic, hydrological and meteorological processes. The knowledge of its magnitude and its spatial distribution is essential to adequately describe and model the processes where it is involved. Radar remote sensors measure microwave energy backscattered by natural surfaces. This scattered energy depends on the geometrical and the dielectric properties of surfaces. In the case of bare soil surfaces, the dielectric properties are directly related to the soil water content, so theoretically, radar remote sensing allows the extraction of spatially distributed soil moisture information. However, the influence of surface roughness on the scattering process limits the ability to correctly estimate volumetric soil moisture values unless detailed roughness measurements are acquired. The present paper reports the results of a study where five images from the remote radar sensor on the satellite RADARSAT-1 were processed and correlated to ground measured soil moisture values over an agricultural catchment. Linear regression models were fitted between RADARSAT-1 derived backscattering coefficient s 0 and the soil moisture at different spatial scales: point scale, field scale and catchment scale. Three soil moisture classes were identified according to their implications for crop growth: (1) low moisture values which contributed to water stress in plants; (2) medium moisture content that allowed an optimal crop growth; and (3) high moisture values which affected crop growth by other means, such as by fungal disease. Results show a direct relation between s 0 and the soil moisture. At the catchment scale the observed correlation is high. At detailed scales, however, variability increases, causing a decrease of correlation values. The ability of s 0 to discriminate between the considered moisture classes seems to be adequate. The accuracy of the estimation increases from the detailed to coarser scales. In the case of vegetated fields, the vegetation cover can cause a certain attenuation of the radar pulse resulting in reduced s 0 values. In this research, vegetation-induced attenuation was considered by applying the semi-empirical 'Water Cloud' model. The presented technique is useful for crop growth monitoring and modelling at medium to large scales, particularly in early growing seasons where the attenuation of vegetation is not too high. It is also applicable to irrigation planning or crop health studies. However, regression lines are site specific and can be affected by the surface roughness variability and vegetation cover of fields.
Radar based surface soil moisture retrieval has been subject of intense research during the last ... more Radar based surface soil moisture retrieval has been subject of intense research during the last decades. However, the space-borne radar sensors available so far provided single configuration observations where the influence of surface roughness frequently caused the solution of the backscattering process to be ill-posed. The ENVISAT/ASAR sensor offers new perspectives since the alternating polarization (AP) mode acquires two simultaneous observations in different polarizations. However, the benefits of the AP mode for surface soil moisture retrieval are still unclear. In this paper a methodology proposed by Pauwels et al. [1] is adapted to ENVISAT/ASAR AP observations. The methodology combines two backscattering models and provides an estimation of the roughness and moisture of the soil surface. The results obtained show that the soil surface conditions and sensor configuration significantly affect the estimations, thus, the approach lacks robustness and cannot be generalized to any situation.
Revista de teledetección: Revista de la Asociación Española de Teledetección, 2005
This article studies the usefulness of radar images for soil moisture estimation over a cultivate... more This article studies the usefulness of radar images for soil moisture estimation over a cultivated catchment of Navarre. With this aim, RADARSAT-1 backscattering observations, acquired during spring 2003, are compared with backscattering values simulated with the Integral Equation Method (IEM) model, from surface soil moisture and roughness ground measurements. Results show a high dependence on an adequate characterization of the surface roughness. The high spatial variability of surface roughness and its strong influence in the backscattering make it difficult to operatively estimate soil moisture at detailed scales.
Uploads
Papers by María González de Audícana Amenábar