A Radiometric Uncertainty Tool for the Sentinel 2 Mission

Gorroño, Javier; Fomferra, Norman; Peters, Marco; Gascon, Ferran; Underwood, Craig; Fox, Nigel; Kirches, Grit; Brockmann, Carsten

doi:10.3390/rs9020178

Cited by 23 publications

(22 citation statements)

References 24 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A state-of-the-art synthetic dataset of R rs and at-sensor reflectances for various sensor configurations with coincident measurements of associated IOPs and optical constituent concentrations was developed using novel techniques suited to high biomass, complex optical systems and cyanobacteria dominated waters. The parameterization of the RTM describing the synthetic dataset utilizes our most current understanding of optical properties and relationships related to eutrophic and cyanobacteria dominated waters and includes four prominent novel aspects: 1) two-layered, FIGURE 13 | SNR for water-leaving radiance (L w ) for S2-MSI developed using the ESA Sentinel 2 radiometric uncertainty tool (Gorroño et al, 2017; for the OWTs defined in this study. The black, red, and blue dashed lines represent the theoretical research, validation, and calibration limits described in Kudela et al (2019).…”

Section: Resultsmentioning

confidence: 99%

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Kravitz

Matthews²,

Lain

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications. This study aims to address this limitation through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which is the first to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size- and type-specific phytoplankton inherent optical properties (IOPs) for mixed eukaryotic/cyanobacteria assemblages; 2) calculations of mixed assemblage chlorophyll-a (chl-a) fluorescence; 3) modeled phycocyanin concentration derived from assemblage-based phycocyanin absorption; 4) and paired sensor-specific top-of-atmosphere reflectances, including optically extreme cases and the contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships of concentrations and IOPs to those of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, and used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and IOPs over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. The results of this work represent a significant leap forward in our capacity for routine, global monitoring of inland water quality.

show abstract

Section: Resultsmentioning

confidence: 99%

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Kravitz

Matthews²,

Lain

et al. 2021

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…The Sentinel-2 sensor has low radiometric calibration uncertainty that makes the radiance of the images to produce reliable results. Gorroño, et al [45] and Gorroño, et al [46] reported radiometric uncertainty ranging from 0.03 to 0.4% for Sentinel-2 images. These values are comparable to other sensors such as the Landsat-8 [45] and thus, Sentinel-2 images have the potential to produce highly accurate information to support different applications.…”

Section: Properties Of Sentinel-2 Datamentioning

confidence: 98%

Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

View full text Add to dashboard Cite

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

show abstract

“…Generally, the outcomes of such comparisons are to compute inter-sensor differences or "biases" in given conditions, but, without uncertainty information, it is difficult to form any definitive quantitative conclusion whether these observed differences indicate the sensors are achieving their specified performance. Efforts are therefore on-going to develop the techniques to facilitate the evaluation and dissemination of satellite sensor uncertainty information [5], with some missions now able to provide this (e.g., Sentinel-2 [6,7]). Sensor uncertainty information is also available for both SLSTR L1 and L2 SST products [3,8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles

Hunt

Mittaz

Smith³

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The Sentinel-3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean, land and atmospheric parameters with its four on-board sensors. Two Sentinel-3 satellites are currently on orbit, providing near-daily global coverage. Sentinel-3A was launched on 16 February 2016 and Sentinel-3B on 25 April 2018. For the early part of its operation, Sentinel-3B flew in tandem with Sentinel-3A, flying 30 s ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work, we consider the tandem-phase data from the infrared channels of one of the on-board instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1, the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of ∼0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level.

show abstract

A Radiometric Uncertainty Tool for the Sentinel 2 Mission

Cited by 23 publications

References 24 publications

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles

Contact Info

Product

Resources

About