On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing

Pahlevan, Nima; Lee, Zhongping; Wei, Jianwei; Schaaf, Crystal B.; Schott, John R.; Berk, Alexander

doi:10.1016/j.rse.2014.08.001

Cited by 200 publications

(117 citation statements)

References 42 publications

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“…In particular, S2A TOA radiances were computed from the S2A-MSI level2 TOA reflectance images (downloaded from Copernicus Open Access Hub) by using the SNAP tool ReflectanceToRadianceOp. For L8-OLI, (downloaded from U.S. Geological Survey EarthExplorer portal) the level1B products were converted into TOA radiances by applying radiometric calibration gains and then rescaled using aquaticspecific gains (Pahlevan et al, 2014). Both S2A and L8 TOA radiance products were atmospherically corrected through an algorithm based on the vector version (6SV) of Second Simulation of the Satellite Signal in the Solar Spectrum (Vermote et al, 2006).…”

Section: Image Processingmentioning

confidence: 99%

“…Differently from ocean colour satellites, they have not been specifically designed for observing water but they are both promising for detailed water quality analysis (Kutser, 2004;Pahlevan et al, 2014) due to (i) radiometric sensitivity ([ 12-bit quantization) (Hedley et al, 2012;; (ii) spatial resolution of 10-30 m; (iii) frequency of overpass (up to every 2-3 days combining L8 and S2 satellites); and (iv) the improved spectral band configuration in the visible-near-infrared wavelengths range.…”

Section: Introductionmentioning

confidence: 99%

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Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

et al. 2018

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For effective lakes' management, highfrequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m -3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.

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Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

et al. 2018

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“…The algorithm (version 2016.05.20) allows the user to choose some inputs for the atmospheric correction: (i) derive ε fixe on scene, per pixel or user-defined; (ii) gain factors for radiometric calibration [56,57]; (iii) atmospheric pressure; (iv) smooth window applied to aerosol reflectance values; and (v) cloud mask threshold (default: 0.0215 on the 1610 nm band). In our study, atmospheric correction was performed using the SWIR band approach, as recommended for turbid water [55]; aerosol correction per-pixel; a smooth window of 25 pixels; and cloud dilatation of 16 pixels (default).…”

Section: Acolite Algorithmmentioning

confidence: 99%

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

et al. 2017

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Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance (R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency's Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%-96%) and blue (84%-92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes (R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

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“…The RSR curves in the visible and NIR bands are shown as dashed lines in Figure 2. Although L8/OLI is not designed for ocean monitoring, its advantages of high spatial resolution and the increased number of bands, combined with its improved signal-tonoise ratio and data quality, make it increasingly used in ocean color remote sensing research and especially in estuarine and coastal research [26][27][28][29][30][31][32][33].…”

Section: Satellite Imagesmentioning

confidence: 99%

Fusion of Landsat-8/OLI and GOCI Data for Hourly Mapping of Suspended Particulate Matter at High Spatial Resolution: A Case Study in the Yangtze (Changjiang) Estuary

2018

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Suspended particulate matter (SPM) concentrations ([SPM]) in the Yangtze estuary, which has third-order bifurcations and four outlets, exhibit large spatial and temporal variations. Studying the characteristics of these variations in [SPM] is important for understanding sediment transport and pollutant diffusion in the estuary as well as for the construction of port and estuarine engineering structures. The 1-h revisit frequency of the Geostationary Ocean Color Imager (GOCI) sensor and the 30-m spatial resolution of the Landsat 8 Operational Land Imager (L8/OLI) provide a new opportunity to study the large spatial and temporal variations in the [SPM] in the Yangtze estuary. In this study, [SPM] images with a temporal resolution of 1 h and a spatial resolution of 30 m are generated through the product-level fusion of [SPM] data derived from L8/OLI and GOCI images using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The results show that the details and accuracy of the spatial and temporal variations are maintained well in the [SPM] images that are predicted based on the fused images. Compared to the [SPM] observations at fixed field stations, the mean relative error (MRE) of the predicted SPM is 17.7%, which is lower than that of the GOCI-derived [SPM] (27.5%). In addition, thanks to the derived high-resolution [SPM] with high spatiotemporal dynamic changes, both natural phenomena (dynamic variation of the maximum turbid zone) and human engineering changes leading to the dynamic variability of SPM in the channel are observed.

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On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing

Cited by 200 publications

References 42 publications

Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Fusion of Landsat-8/OLI and GOCI Data for Hourly Mapping of Suspended Particulate Matter at High Spatial Resolution: A Case Study in the Yangtze (Changjiang) Estuary

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