Search results for "Sentinel"
showing 10 items of 189 documents
Modelo empírico para la determinación de clorofila-a en aguas continentales a partir de los futuros Sentinel-2 y 3. Validación con imágenes HICO
2014
[EN] Chlorophyll-a concentration is one of the main indicators of inland waters quality. Using CHRIS/PROBA images and in situ data obtained in four lakes in Colombia and Spain, we obtained empirical models for the estimation of chlorophyll-a concentration, which can be directly applied to future images of MSI Sentinel-2 and OLCI Sentinel-3 sensors. The models, based on spectral band indices, were validated with data from the hyperspectral sensor HICO, onboard of the International Space Station.
Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content
2011
ESA’s upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the…
Evaluation of different InSAR multi-baseline construction methods over a dam in southern Italy
2018
Monitoring dam displacements using different techniques allows an evaluation of their structural behaviour over time. In this study, dam displacements (for the Castello dam, Agrigento, Italy) have been investigated using different Interferometric Synthetic Aperture Radar (InSAR) techniques exploiting a freely available dataset from the EU Copernicus Sentinel-1 SAR built by the European Space Agency (ESA). The dataset includes Sentinel 1A (S1A) images acquired in dual-polarization and Interferometric Wide (IW) swath using the Terrain Observation with Progressive Scans SAR (TOPSAR) mode. Three main Multi-Baseline Construction methods based on the identification of Persistent Scatterers (PS) h…
Mākoņu noņemšana no satelīta attēliem
2020
Jauni satelītu attēli ir praksē noderīgi, tos izmanto dažādās jomās, piemēram, kartogrāfijā. Šajā darbā tiek apskatīti Copernicus Sentinel-2 satelītu uzņemtie Zemes attēli. Tiek izpētīta satelītu programma, rīki un datu formāti. Tiek apskatīti lēmuma koku un Beijesa klasificēšanas algoritmi, kas ļauj izgūt mākoņus un citus reģionus no attēliem, izpētīti attēlu apstrādes algoritmi, kas ļauj saskaņot dažādu attēlu vizuālo izskatu pēc attēla histogrammas datiem. Darba praktiskajā daļā tiek realizēta sistēma, kas ļauj izgūt lietotājam aktuālāko satelīta attēlu noklājumu pār ģeogrāfisku intereses reģionu. Attēli tiek veidoti kombinējot jaunākos satelīta uzņemtos d…
Clasificación de usos del suelo a partir de imágenes Sentinel-2
2017
[EN] Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improve-ment with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those…
A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUM…
2018
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach f…
Data service platform for sentinel-2 surface reflectance and value-added products: System use and examples
2016
This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.…
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
Permanent Stations for Calibration/Validation of Thermal Sensors over Spain
2016
The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the mars…
Testing Multi-Sensors Time Series of Lai Estimates to Monitor Rice Phenology: Preliminary Results
2018
Timely and accurate information on crop growth and seasonal dynamics are increasingly needed to develop monitoring systems aimed to detect seasonal anomalies, support site specific management and estimate crop yield at the end of the season. In particular, frequent decametric information nowadays being provided exploiting the new generation of Earth Observation (EO) platforms are fundamental for farm level monitoring. This study presents an analysis aimed at fully exploiting dense time series of EO data derived from the combined use of ESA Sentinel-2A and NASA Landsat-7/8 imageries for crop phenological monitoring. Decametric Leaf Area Index (LAI) maps were generated for the year 2016 by in…