Search results for "Sentinel-2"
showing 10 items of 47 documents
Sentinel-1 & Sentinel-2 Data for Soil Tillage Change Detection
2018
In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately similar to 100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three …
Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression
2021
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…
Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2
2014
La Biosfera es uno de los principales sistemas que conforman la Tierra. Su estudio permite comprender la relación entre la vegetación y el ciclo del carbono y cómo éste puede ser afectado por los cambios en los niveles de CO2 y los usos de suelo. Para el estudio de estas dinámicas a escala global y local, han sido desarrollados diversos modelos que son representaciones de la realidad en una escala y complejidad más simple. Parte de las variables de entrada de estos modelos son obtenidas mediante medidas de teledetección gracias al Global Climate Observing System (GCOS), que ha determinado un conjunto de 50 variables climáticas esenciales que contribuyen a los estudios de cambio climático qu…
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…
Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions
2021
The estimation of biophysical variables from remote sensing data raises important challenges in terms of the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains the spatial resolution while significantly increasing the subpixel land-cover heterogeneity. Precisely, this spatial variability often makes that rather different canopy structures are aggregated together, which eventually generates important deviations in the corresponding parameter quantification. In the context of the Copernicus program (and other related Earth Explorer missions), this article propose…
Fire severity estimation in southern of the Buenos Aires province, Argentina, using Sentinel-2 and its comparison with Landsat-8
2018
[EN] Assessment of rural fire severity is fundamental to evaluate fire damages and to analyze recovery processes in a low-cost and efficient way. Burnt areas covering shrubs and grasslands were estimated in more than 30,000 km2 in Argentina from December 2016 to January 2017. The study area presented in this work is located in the South of the Buenos Aires province, and it covers a semiarid area with the presence of xerophilous shrubs and grasslands. This is one of the most abundant ecosystem in Central and Southern Argentina. Field campaigns were carried out over the area affected by the fire in order to georreference the burnt plots and characterized the fire severity in 5 levels. The obj…
A global Canopy Water Content product from AVHRR/Metop
2020
Abstract Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related …
Canopy chlorophyll content and LAI estimation from Sentine1-2: Vegetation indices and Sentine1-2 Leve1-2A automatic products comparison
2019
The aim of this work is to analyze different methodologies for the estimation of leaf area index (LAI) and canopy chlorophyll content (CCC), using the Sentine1-2 satellite. LAI and CCC are biophysical parameters indicator of crop health state and fundamental in the productivity prediction. The purpose is to define the most optimal LAI and CCC estimation method for operational use in the monitoring of agricultural areas. Moreover, the CCC and LAI automatic products obtained directly through the Sentinel Application Platform Software (SNAP) biophysical processor and Sentine1-2 images by means of an artificial neural network (ANN) are validated. On the other hand, common vegetation indices use…
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…