0000000000195431
AUTHOR
Ana B. Ruescas
showing 15 related works from this author
Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data
2016
Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the ESA's Sentinel 3 (S3) satellite, accurate LST retrieval methodologies exploiting the synergy between OLCI and SLSTR instruments can be developed. In this paper we propose a candidate methodology for retrieving LST from data acquired with the forthcoming S3 instruments. The LST algorithm is based on the Split-Window (SW) technique with an explicit dependence on surface emissivity, in contrast to the AATSR level 2 algorithm…
Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian lan…
2011
In past decades, the Iberian Peninsula has been shown to have suffered vegetation changes such as desertification and reforestation. Normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters, estimated from data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series, are particularly adapted to assess these changes. This work presents an application of the yearly land-cover dynamics (YLCD) methodology to analyse the behaviour of the vegetation, which consists of a combined multitemporal study of the NDVI and LST parameters on a yearly basis. Throughout the 1…
Retrieval of Case 2 Water Quality Parameters with Machine Learning
2018
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X). The used data are based on Hydrolight in-water radiative transfer simulations at Sentinel-3 OLCI wavebands, and the application is done exclusively for absorbing waters with high concentrations of coloured dissolved organic matter (CDOM). The regression approaches are: regularized linear, random forest, Kernel ridge, Gaussian process and support vector regressors. The validation is made with and an independent simulation dataset. A comparison with the OLCI Neural Network Swarm (ONSS) is made as well. The best approached is applied to a sample scene and compared with t…
ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING
2020
Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potentia…
Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications
2008
A description of thermal radiometric field measurements carried out in the framework of the European project SENtinel-2 and Fluorescence Experiment (SEN2FLEX) is presented. The field campaign was developed in the region of Barrax (Spain) during June and July 2005. The purpose of the thermal measurements was to retrieve biogeophysical parameters such as land surface emissivity (LSE) and temperature (LST) to validate airborne-based methodologies and to characterize different surfaces. Thermal measurements were carried out using two multiband field radiometers and several broadband field radiometers, pointing at different targets. High-resolution images acquired with the Airborne Hyperspectral…
Examining the Effects of Dust Aerosols on Satellite Sea Surface Temperatures in the Mediterranean Sea Using the Medspiration Matchup Database
2011
Abstract Dust aerosol plumes from the Sahara cover the Mediterranean Sea regularly during the summer months (June–August) and occasionally during other seasons. Dust can absorb infrared longwave radiation, thus causing a drop in sea surface temperature (SST) retrievals from satellite. To quantify the magnitude of this absorption and to understand the sources of the biases that might be introduced when trying to validate SST algorithms with in situ bulk temperatures, the effects of the dust absorption are studied using the Medspiration Match-up Database. This database provides in situ and satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR) and the Advanced Along-…
Mapping sub-pixel burnt percentage using AVHRR data. Application to the Alcalaten area in Spain
2010
The purpose of this work is to estimate at sub-pixel scale the percentage of burnt land using the Advanced Very High Resolution Radiometer AVHRR through a simple approach. This methodology is based on multi-temporal spectral mixture analysis MSMA, which uses a normalized difference vegetation index NDVI and a land-surface temperature LST image as input bands. The area of study is located in the Alcalaten region in Castellon Spain, a typical semi-arid Mediterranean region. The results have shown an extension of approximately 55 km2 affected by fire, which is only 5% lower than the statistic reports provided by the Environmental Ministry of Spain. Finally, we include a map of the area showing…
Fluorescence estimation in the framework of the CEFLES2 campaign
2011
International audience; Chlorophyll fluorescence (ChF) is a relevant indicator of the actual plant physiological status. In this article different methods to measure ChF from remote sensing are evaluated: The Fraunhofer Line Discrimination (FLD), theFluorescence Radiative Method (FRM) and the improved Fraunhofer Line Discrimination (iFLD). The three methods have been applied to data acquired in the framework of the CarboEurope, FLEX and Sentinel-2 (CEFLES2) campaign in Les Landes, France in September 2007. Comparing with in situ measurements, the results indicate that the methods that provide the best results are the FLD and the iFLD with root mean square errors (RMSEs) of 0.4 and 0.5 mW m-…
An optical classification tool for global lake waters
2017
Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral…
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
2020
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative so…
The Land Surface Temperature Synergistic Processor in BEAM: A Prototype towards Sentinel-3
2016
Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Syner…
Supervised Classifications of Optical Water Types in Spanish Inland Waters
2022
Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality al…
Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data
2018
The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative t…
Retrieval of coloured dissolved organic matter with machine learning methods
2017
The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the absorption coefficient (a) at a certain wavelength (e.g. 440nm). This paper presents a comparison of four machine learning methods for the retrieval of CDOM from remote sensing signals: regularized linear regression (RLR), random forest (RF), kernel ridge regression (KRR) and Gaussian process regression (GPR). Results are compared with the established polynomial regression algorithms. RLR is revealed as the simplest and most efficient method, followed closely by its nonlinear counterpart KRR.
HyperLabelMe : A Web Platform for Benchmarking Remote-Sensing Image Classifiers
2017
HyperLabelMe is a web platform that allows the automatic benchmarking of remote-sensing image classifiers. To demonstrate this platform's attributes, we collected and harmonized a large data set of labeled multispectral and hyperspectral images with different numbers of classes, dimensionality, noise sources, and levels. The registered user can download training data pairs (spectra and land cover/use labels) and submit the predictions for unseen testing spectra. The system then evaluates the accuracy and robustness of the classifier, and it reports different scores as well as a ranked list of the best methods and users. The system is modular, scalable, and ever-growing in data sets and clas…