0000000000074748
AUTHOR
Julia Amorós-lópez
showing 26 related works from this author
Multitemporal fusion of Landsat and MERIS images
2011
Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monito…
Design of a configurable multispectral imaging system based on an AOTF.
2011
In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm.
Diurnal Cycle Relationships between Passive Fluorescence, PRI and NPQ of Vegetation in a Controlled Stress Experiment
2017
In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal dissipation. Non-photochemical quenching (NPQ) processes are controlled photoprotective mechanisms which; once activated; strongly control the dynamics of photochemical efficiency. With illumination conditions increasing and decreasing during a diurnal cycle; photoprotection mechanisms needs to change accordingly. The goal of this work is to quantify dynamic NPQ; measured from active fl…
Randomized kernels for large scale Earth observation applications
2020
Abstract Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop st…
A kernel regression approach to cloud and shadow detection in multitemporal images
2013
Earth observation satellites will provide in the next years time series with enough revisit time allowing a better surface monitoring. In this work, we propose a cloud screening and a cloud shadow detection method based on detecting abrupt changes in the temporal domain. It is considered that the time series follows smooth variations and abrupt changes in certain spectral features will be mainly due to the presence of clouds or cloud shadows. The method is based on linear and nonlinear regression analysis; in particular we focus on the regularized least squares and kernel regression methods. Experiments are carried out using Landsat 5 TM time series acquired over Albacete (Spain), and compa…
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
2020
Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites. Matching the landmark accurately is of paramount relevance, and the process can be strongly impacted by the cloud contamination of a given landmark. This paper introduces a complete pattern recognition methodology able to detect the presence of clouds over landmarks using Meteosat Second Generation (MSG) data. The methodology is based on the ensemble combination of dedicated support vector machines (SVMs) dependent…
Analysis of acousto-optic tunable filter performance for imaging applications
2010
Acousto-optic tunable filters (AOTFs) can be used as spec- tral filters in multispectral imaging applications. Acousto-optic crystals diffract a single wavelength from a broadband light beam, depending on the applied radio frequency signal. However, experimental measurements show that the actual performance is far from the expected behavior. We present an experimental characterization of several commercial off-the- shelf AOTFs for the implementation of multispectral imaging instruments. The diffraction performance of three bare crystals is compared, while a fourth AOTF crystal is mounted on the optical path of a multispectral im- ager to evaluate its performance. The experiments show that t…
400– to 1000–nm imaging spectrometer based on acousto-optic tunable filters
2004
An imaging spectrometer covering the 400-1000 nm band has been conceived and developed. The system is based on an Acousto-Optic Tunable Filter (AOTF) attached to a high performance digital camera. The AOTF permits the selection of spectral bands with an RF signal in the range of 70-210 MHz. The range is covered using two transducers attached to a single crystal. Although the idea is not new it covers a broader spectrum than previous systems. It includes a telecentric optical system that enhances system efficiency, by ensuring that the chief ray of each light cone emerges out of this doublet parallel to the optical axes. Additionally, an smart choice of integration time reduces the dependenc…
Evaluation of remote sensing of vegetation fluorescence by the analysis of diurnal cycles
2008
Chlorophyll fluorescence (ChF) emission is a direct indicator of the photosynthetic activity of vegetation, which is a key parameter of the carbon cycle. This paper analyses chlorophyll fluorescence evolution at leaf level during a complete diurnal cycle in simulated and natural conditions, for two species under different stress conditions. Absolute spectral radiance of the ChF emission is obtained allowing a quantitative derivation of the fluorescence yield of the ChF, which correlates well with established fluorescence instruments. The studied cases show that the ChF emission is mainly driven by the photosynthetic active radiation during the whole cycle, but the fluorescence yield is seve…
Sensitivity analysis of the fraunhofer line discrimination method for the measurement of chlorophyll fluorescence using a field spectroradiometer
2007
The Fraunhofer Line Discrimination (FLD) principle is established as a good method for remote sensing of solar induced chlorophyll fluorescence. Some improvements to the method are analysed in order to determine and reduce the sources of error in the estimation of the fluorescence emission. A sensitivity analysis has been performed over simulated data generated from real diurnal cycle measurements.
Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring
2013
Abstract Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using …
Learning main drivers of crop progress and failure in Europe with interpretable machine learning
2021
Abstract A wide variety of methods exist nowadays to address the important problem of estimating crop yields from available remote sensing and climate data. Among the different approaches, machine learning (ML) techniques are being increasingly adopted, since they allow exploiting all the information on crop progress and environmental conditions and their relations with crop yield, achieving reliable and accurate estimations. However, interpreting the relationships learned by the ML models, and hence getting insights about the problem, remains a complex and usually unexplored task. Without accountability, confidence and trust in the ML models can be compromised. Here, we develop interpretab…
Spectro-temporal reflectance surfaces: a new conceptual framework for the integration of remote-sensing data from multiple different sensors
2012
The conflict between spatial and temporal resolution of satellite systems, as well as the frequent presence of clouds in the images, has been a traditional limitation of remote sensing in the optical domain. Nevertheless, most of the conceptual tools and algorithms developed classically in remote sensing are based on the input of a series of cloud-free images from identical sensors. In this study, we propose a conceptual framework that is able to ingest data from several different sensors, make them homogeneous, eliminate clouds virtually, and make them usable in a flexible, efficient, and transparent way. The methodology is based on previous developments such as spatial ‘downscaling’, temp…
Configurable Passband Imaging Spectrometer Based on Acousto-optic Tunable Filter
2008
This work presents a new configurable imaging spectrometer called Autonomous Tunable Filtering System (ATFS). The system can be configured to acquire a single narrow spectral band, a composite multispectral image, or a broad pass-band. This flexibility is given by the use of an Acousto-Optic Tunable Filter (AOTF) driven by a programmable radio frequency (rf) signal generator. The AOTF acts as a light-diffraction element which output wavelength is selected by the frequency of an rf signal applied to it. The designed rf driver is based on a high-speed Digital-to-Analog converter, which can synthesize any composite rf waveform formed by a combination of sine signals. The images are formed thro…
Gridding artifacts on medium-resolution satellite image time series: MERIS case study
2011
Earth observation satellites provide a valuable source of data which when conveniently processed can be used to better understand the Earth system dynamics. In this regard, one of the prerequisites for the analysis of satellite image time series is that the images are spatially coregistered so that the resulting multitemporal pixel entities offer a true temporal view of the area under study. This implies that all the observations must be mapped to a common system of grid cells. This process is known as gridding and, in practice, two common grids can be used as a reference: 1) a grid defined by some kind of external data set (e.g., an existing land-cover map) or 2) a grid defined by one of t…
Study of the diurnal cycle of stressed vegetation for the improvement of fluorescence remote sensing
2006
Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Ch…
Remote sensing of chlorophyll fluorescence for estimation of stress in vegetation. Recommendations for future missions
2007
Vegetation monitoring is a key issue in Earth Observation due to its relation with the global CO2 cycle. Chlorophyll fluorescence (ChF) emitted by the vegetation is an accurate indicator of the plant status and their photosynthetic activity. This work analyses the diurnal evolution of the ChF emission spectrum and the fluorescence yield in order to determine the best conditions for remote sensing of ChF from a satellite platform. The ChF evolution is studied at leaf level during several diurnal cycles, in simulated conditions, for two species under different stress conditions. The analysis of the signal levels gives an estimation of the values of ChF emission which could be observed from a …
Configurable bandwidth imaging spectrometer based on acousto-optic tunable filter
2005
This paper presents a new portable instrument called Autonomous Tunable Filtering System (ATFS), developed for highly customisable imaging spectrometry in the VIS-NIR range. The ATFS instrument consists of an Acousto-Optic Tunable Filter (AOTF), an optical system, a Radio Frequency (RF) driver based on a Direct Digital Synthesiser (DDS) and control software. The ATFS can be attached to a variety of high-performance monochrome cameras. The system works as a spectral bandpass filter whose wavelength can be selected between 400nm and 1000nm and whose bandwidth can be adjusted between 4nm and 50nm. The filter can be tuned electronically at a very high speed and accuracy, thanks to the DDS versa…
Operational cloud screening service for Sentinel-2 image time series
2015
This paper deals with the development and implementation of a cloud screening algorithm for image time series, with the focus on the forthcoming Sentinel-2 satellites to be launched under the ESA Copernicus Programme. The proposed methodology is based on kernel ridge regression and exploits the temporal information to detect anomalous changes that correspond to cloud covers. The huge data volumes to be processed when dealing with high temporal, spatial, and spectral resolution datasets motivate the implementation of the algorithm within distributed computer resources. In consequence, an operational cloud screening service has been specifically designed and implemented in the frame of the Se…
Regularized multiresolution spatial unmixing for ENVISAT/MERIS and landsat/TM image fusion
2011
Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach ba…
Cloud masking and removal in remote sensing image time series
2017
Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clo…
Retrieval of oceanic chlorophyll concentration with relevance vector machines
2006
Abstract In this communication, we evaluate the performance of the relevance vector machine (RVM) for the estimation of biophysical parameters from remote sensing data. For illustration purposes, we focus on the estimation of chlorophyll-a concentrations from remote sensing reflectance just above the ocean surface. A variety of bio-optical algorithms have been developed to relate measurements of ocean radiance to in situ concentrations of phytoplankton pigments, and ultimately most of these algorithms demonstrate the potential of quantifying chlorophyll-a concentrations accurately from multispectral satellite ocean color data. Both satellite-derived data and in situ measurements are subject…
Improving the performance of acousto-optic tunable filters in imaging applications
2010
Acousto-optic tunable filters (AOTFs) can be used as spectral filters for the implementation of multispectral imaging systems. However, obtaining quality images is challenging. In this work, we propose several improvements that enable the use of these systems in quantitative spectroscopic imaging applications. The improvements are based on three pillars: 1. a finer spectral bandpass shaping by dynamically optimizing the radio frequency (rf) driving signal, 2. an extensive calibration process, and 3. careful image preprocessing that uses calibration data to correct some well known AOTF issues in imaging applications. A novel multispectral imaging instrument is built using commercial off-the-…
Estimation of solar-induced vegetation fluorescence from space measurements
2007
[1] A characteristic spectral emission is observed in vegetation chlorophyll under excitation by solar radiation. This emission, known as solar-induced chlorophyll fluorescence, occurs in the red and near infra-red spectral regions. In this paper a new methodology for the estimation of solar-induced chlorophyll fluorescence from spaceborne and airborne sensors is presented. The fluorescence signal is included in an atmospheric radiative transfer scheme so that chlorophyll fluorescence and surface reflectance are retrieved consistently from the measured at-sensor radiance. This methodology is tested on images acquired by the Medium Resolution Imaging Spectrometer (MERIS) on board the ENVIron…
Multitemporal Cloud Masking in the Google Earth Engine
2018
The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these r…
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…