Initial Validation of Sentinel-2 Collection-1 L2A-Products

The Copernicus Sentinel-2 mission provides data since the launch of the Sentinle-2A unit in 2015. With the launch of the Sentinel-2B in 2017 it is a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires high spatial resolution optical data products. The Sentinel-2 mission serves for observation of land-cover change and deriving biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Atmospheric correction processor Sen2Cor was developed to remove the effect of the atmosphere from Sentinel-2 data. Sen2Cor is designed for mono-temporal processing of Sentinel-2 L1C data products providing Level-2A Bottom-of-Atmosphere (BOA) surface reflectance product together with Aerosol Optical Thickness (AOT), Integrated Water Vapour (WV) and Scene Classification (SCL) maps. Since June 2017, ESA uses Sen2Cor for systematic, operational Level-2A processing of Sentinel-2 acquisitions. Products are available on the Copernicus Open Access Hub. However, several evolutions of Sen2Cor and L2A-products since 2017 resulted in a quite inhomogeneous time series. Therefore, ESA started a reprocessing campaign of the complete Sentinel-2 data archive. The resulting Collection-1 of Sentinel data archive provides a real homogeneous time series based on the recent Sen2Cor processor version. The presentation provides initial validation results for AOT, WV and (BOA) surface reflectance retrieval together with quality assessment of cloud screening. Accuracy and uncertainty of AOT and WV retrieval is assessed with reference measurements from AERONET stations. BOA reflectance retrieval can be estimated on a limited number of reference data only from RadCalNet-sites and in-situ campaigns. Reference data for cloud screening are generated by manual labelling of test images

Time series noise of Copernicus Sentinel-2 operational L2A-Products of year 2022

Copernicus Sentinel-2 is the main European land surface observing mission. It serves for observation of land-cover change and deriving biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Data quality of the provided data products is a critical point for all these applications. The Sentinel-2 mission consists of a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an identical optical imaging sensor MSI (Multi-Spectral Instrument) which samples 13 spectral bands: four bands at 10 m in the Visible Near Infrared (VNIR) region, six bands at 20 m and three bands at 60 m spatial resolution in the VNIR to Shortwave Infrared (SWIR) region. Sentinel-2 Level-2A (L2A) data contain Bottom-of-Atmosphere (BOA) surface reflectance products together with Aerosol Optical Thickness (AOT), Integrated Water Vapour (WV) and Scene Classification (SCL) maps. They are generated with Sen2Cor which is the operational atmospheric correction processor that removes the effect of the atmosphere from Top-of-Atmosphere Level-1C data. ESA started the complete reprocessing of the Sentinel-2 data archive named Collection-1 which is tagged with the processing baseline (PB) 5.00. The previous processing baseline PB 4.00 has equivalent evolutions and is very close to the PB 5.00 of Collection-1. Operational L2A products with PB 4.00 were generated from end of January 2022 to beginning of December 2022. In this presentation we propose to study surface reflectance time series smoothness, for several test sites, using L2A products from year 2022. The smoothness of that time series is used as an indicator of data quality of the reprocessed products. Test sites are selected representing different climate zones with different AOT retrieval performance 0.03 ≤ RMSDAOT ≤ 0.20 and different WV retrieval performance 0.12 g/cm2 ≤ RMSDWV ≤ 0.40 g/cm2

Enabling electronic prognostics using thermal data

Prognostics is a process of assessing the extent of deviation or degradation of a product from its expected normal operating condition, and then, based on continuous monitoring, predicting the future reliability of the product. By being able to determine when a product will fail, procedures can be developed to provide advanced warning of failures, optimize maintenance, reduce life cycle costs, and improve the design, qualification and logistical support of fielded and future systems. In the case of electronics, the reliability is often influenced by thermal loads, in the form of steady-state temperatures, power cycles, temperature gradients, ramp rates, and dwell times. If one can continuously monitor the thermal loads, in-situ, this data can be used in conjunction with precursor reasoning algorithms and stress-and-damage models to enable prognostics. This paper discusses approaches to enable electronic prognostics and provides a case study of prognostics using thermal data.Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

SCIAMACHY: The new Level 0-1 Processor

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) is a scanning nadir and limb spectrometer covering the wavelength range from 212 nm to 2386 nm in 8 channels. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002 on the ENVISAT platform. After the platform failure in April 2012, SCIAMACHY is now in the postprocessing phase F. SCIAMACHY�s originally specified in-orbit lifetime was double the planned lifetime. SCIAMACHY was designed to measure column densities and vertical profiles of trace gas species in the mesosphere, in the stratosphere and in the troposphere (Bovensmann et al., 1999). It can detect a large amount of atmospheric gases (e.g. O3 , H2CO, CHOCHO, SO2 , BrO, OClO, NO2 , H2O, CO, CH4 , among others ) and can provide information about aerosols and clouds. The operational processing of SCIAMACHY is split into Level 0-1 processing (essentially providing calibrated radiances) and Level 1-2 processing providing geophysical products. The operational Level 0-1 processor has been completely re-coded and embedded in a newly developed framework that speeds up processing considerably. In the frame of the SCIAMACHY Quality Working Group activities, ESA is continuing the improvement of the archived data sets. Currently Version 9 of the Level 0-1 processor is being implemented. It will include An updated degradation correction Several improvements in the SWIR spectral range like a better dark correction, an improved dead & bad pixel characterisation and an improved spectral calibration Improvements to the polarisation correction algorithm Improvements to the geolocation by a better pointing characterisation Additionally a new format for the Level 1b and Level 1c will be implemented. The version 9 products will be available in netCDF version 4 that is aligned with the formats of the GOME -1 and Sentinel missions. We will present the first results of the new Level 0-1 processing in this paper

Using remote sensing as a support to the implementation of the European Marine Strategy Framework Directive in SW Portugal

The exclusive economic zones (EEZ) of coastal countries are coming under increasing pressure from various economic sectors such as fishing, aquaculture, shipping and energy production. In Europe, there is a policy to expand the maritime economic sector without damaging the environment by ensuring that these activities comply with legally binding Directives, such as the Marine Strategy Framework Directive (MSFD). However, monitoring an extensive maritime area is a logistical and economic challenge. Remote sensing is considered one of the most cost effective, methods for providing the spatial and temporal environmental data that will be necessary for the effective implementation of the MSFD. However, there is still a concern about the uncertainties associated with remote sensed products. This study has tested how a specific satellite product can contribute to the monitoring of a MSFD Descriptor for "good environmental status" (GES). The results show that the quality of the remote sensing product Algal Pigment Index 1 (API 1) from the MEdium Resolution Imaging Spectrometer (MERIS) sensor of the European Space Agency for ocean colour products can be effectively validated with in situ data from three stations off the SW Iberian Peninsula. The validation results show good agreement between the MERIS API 1 and the in situ data for the two more offshore stations, with a higher coefficient of determination (R-2) of 0.79, and with lower uncertainties for the average relative percentage difference (RPD) of 24.6% and 27.9% and a root mean square error (RMSE) of 0.40 and 0.38 for Stations B and C, respectively. Near to the coast, Station A has the lowest R-2 of 0.63 and the highest uncertainties with an RPD of 112.9% and a RMSE of 1.00. It is also the station most affected by adjacency effects from the land: when the Improved Contrast between Ocean and Land processor (ICOL) is applied the R-2 increases to 0.77 and there is a 30% reduction in the uncertainties estimated by RPD. The MERIS API 1 product decreases from inshore to offshore, with higher values occurring mainly between early spring and the end of the summer, and with lower values during winter. By using the satellite images for API 1, it is possible to detect and track the development of algal blooms in coastal and marine waters, demonstrating the usefulness of remote sensing for supporting the implementation of the MSFD with respect to Descriptor 5: Eutrophication. It is probable that remote sensing will also prove to be useful for monitoring other Descriptors of the MSFD.EU (European Space Agency) [308392, 21464/08/1-0, 607325]; Portuguese FCT [FRH/BD/78354/2011, SFRH/BD/78356/2011]; Horizon 2020 AquaSpace [633476]info:eu-repo/semantics/publishedVersio

Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R2, i.e., ~0.6 to ~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions

An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation

Accurate atmospheric correction (AC) is a prerequisite for quantitative ocean colour remote sensing and remains a challenge in particular over coastal waters. Commonly AC algorithms are validated by establishing a mean retrieval error from match-up analysis, which compares the satellite-derived surface reflectance with concurrent ground radiometric observations. Pixel-based reflectance uncertainties however, are rarely provided by AC algorithms and those for the operational Ocean and Land Colour Instrument (OLCI) marine reflectance product are not yet recommended for use. AC retrieval errors and uncertainties directly determine the quality with which ocean colour products can be estimated from the marine surface reflectance. Increasingly there is also the need for reflectance uncertainty products to be used as data assimilation inputs into biogeochemical models. This paper describes the development of a new coastal AC algorithm for Sentinel-3 OLCI that provides pixel-based estimation of the inherent model inversion uncertainty and sensor noise propagation. The algorithm is a full-spectral model-based inversion of radiative transfer (RT) simulations in a coupled atmosphere–ocean system using an ensemble of artificial neural networks (ANN) that were initialized differently during the training process, but composed of the same network architecture. The algorithm has been validated against in-situ radiometric observations across a wide range of optical water types, and has been compared with the latest EUMETSAT operational Level 2 processor IPF-OL-2 v7.01. In this analysis we found that the ensemble ANN showed improved performance over the operational Level 2 processor with a band-averaged (412–708 nm) mean absolute percentage error (MAPE) of 16% compared to 37% and a four-times lower band-averaged bias of -0.00045 sr-1. In the ensemble inversion process we account for three uncertainty components: (1) the total model variance that describes the variance of the data from the different ANNs, (2) the prediction variance of the mean, which is based on calculations of the RT simulations and (3) the instrument noise variance of the mean by propagating the OLCI spectral signal-to-noise ratios (SNR). To study algorithm performance and to quantify the contribution of the different uncertainty components to the total uncertainty, we applied the algorithm to an optically complex full resolution (FR) test scene covering coastal waters of the Great Barrier Reef, Australia. The uncertainties associated with the instrument noise variance were found to be two orders of magnitude lower than the uncertainty components of the prediction and total model variances. The overall largest uncertainty component in our uncertainty framework is attributed to the total model inversion error from averaging the responses of the slightly different adapted networks in the ensemble. The algorithm is made publicly available as a Python/C plugin for the Sentinel Application Platform (SNAP)

Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system

Abstract Dredging activities in estuaries frequently cause deleterious environmental effects on the water quality which can impact flora, fauna, and hydrodynamics, among others. A medium- and high-resolution satellite-based procedure is used in this study to monitor turbidity plumes generated during the dredging operations in the Guadalquivir estuary, a major estuarine system providing important ecosystem services in southwest Europe. A multi-sensor scheme is evaluated using a combination of five public and commercial medium- and high-resolution satellites, including Landsat-8, Sentinel-2A, WorldView-2, WorldView-3, and GeoEye-1, with pixel sizes ranging from 30 m to 0.3 m. Applying a multi-conditional algorithm after the atmospheric correction of the optical imagery with ACOLITE, Sen2Cor and QUAC processors, it is demonstrated the feasibility to monitoring suspended solids during dredging operations at a spatial resolution unachievable with traditional satellite-based ocean color sensors (>300 m). The frame work can be used to map on-going, post and pre-dredging activities and asses Total Suspended Solids (TSS) anomalies caused by natural and anthropogenic processes in coastal and inland waters. These promising results are suitable to effectively improve the assessment of features relevant to environmental policies for the challenging coastal management and might serve as a notable contribution to the Earth Observation Program

Retrieval of evapotranspiration from sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this studyperformeda comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived fromFAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas

Ocean surface currents derived from Sentinel-1 SAR Doppler shift measurements

Reliable information about ocean surface currents is crucial for operational oceanography, regulating weather development, and climate research (e.g., UN SDG 13). Upper-ocean currents are also key for monitoring life below water, including conservation of marine biodiversity at every trophic level (e.g., UN SDG 14). Locating upper ocean currents “with the right strength at the right place and time” is moreover critically needed to support the maritime transport sector, renewable marine energy, and maritime safety operations as well as for monitoring and tracking of marine pollution. In spite of this, upper ocean currents and their variability are mostly indirectly estimated and often without quantitative knowledge of uncertainties. In this thesis, Sentinel-1 Synthetic Aperture Radar (SAR) based Doppler frequency shift observations are examined for the retrievals of ocean surface current velocity in the radar line-of-sight direction. In the first study (Paper 1), Sentinel-1 A/B Interferometric Wide (IW) data acquired along the northern part of the Norwegian coastal zone from October-November 2017 at a spatial resolution of 1.5 km are compared with independent in-situ data, ocean model fields, and coastal High-Frequency Radar observations. Although only a limited dataset was available, the findings and results reveal that the strength of the meandering Norwegian Coastal Current derived from the SAR Doppler frequency shift observations are consistent with observations. However, limitations are encountered due to insufficient calibration and lack of ability to properly partition the geophysical signals into wave and current contributions. A novel approach for calibration of the attitude contribution to the Sentinel-1B Wave Mode (WV) Doppler frequency shift emerged for a test period in December 2017 - January 2018. Building on this calibrated dataset, an empirical model function (CDOP3S) for prediction of the sea state-induced contribution to the Doppler shift observations is developed for the global open ocean in Paper 2. The assessment against collocated surface drifter data are promising and suggest that the Sentinel-1B WV acquisitions can be used to study the equatorial ocean surface currents at a monthly timescale with a 20 km spatial resolution. The calibrated dataset combined with the new geophysical model function developed in Paper 2 also allowed for the study (Paper 3) of ocean surface current retrievals from the high-resolution Sentinel-1B IW swath data acquired along the coastal zone on northern Norway. In this case, the geophysical model function had to be trained and adjusted for fetch limited coastal sea state conditions. The results demonstrate that the Sentinel-1B SAR-derived ocean surface currents significantly improved, compared to the findings reported in Paper 1. Although the thesis builds on a limited period of observations, constrained by the availability of experimental attitude calibration, the results are all in all promising. Reprocessing of the full Sentinel-1 A/B SAR Doppler shift dataset using the novel attitude bias correction is therefore strongly recommended for further improvement of the empirical model function. Regular use of the Sentinel-1 A/B SAR for ocean surface current monitoring would thus be feasible, leading to advances in studies of upper ocean dynamics in support to the Copernicus Marine Environment Monitoring Service (CMEMS) program and the United Nations (UN) Decade of Ocean Sciences.Doktorgradsavhandlin