This report describes the results of an extended national field spectroscopy campaign designed to validate the Landsat 8 and Sentinel 2 Analysis Ready Data (ARD) surface reflectance (SR) products generated by Digital Earth Australia. Field spectral data from 55 overpass coincident field campaigns have been processed to match the ARD surface reflectances. The results suggest the Landsat 8 SR is validated to within 10%, the Sentinel 2A SR is validated to within 6.5% and Sentinel 2B is validated to within 6.8% . Overall combined Sentinel 2A and 2B are validated within 6.6% and the SR for all three ARD products are validated to within 7.7%.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 130853 GA Landsat 5 TM Analysis Ready Data Collection 3</b> Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 132310 GA Landsat 7 ETM+ Analysis Ready Data Collection 3</b> Surface Reflectance (SR) is a suite of Earth Observation (EO) products from GA. The SR product suite provides standardised optical surface reflectance datasets using robust physical models to correct for variations in image radiance values due to atmospheric properties, and sun and sensor geometry. The resulting stack of surface reflectance grids are consistent over space and time which is instrumental in identifying and quantifying environmental change. SR is based on radiance data from the Landsat TM/ETM+ and OLI sensors.

Landsat 8 has a higher radiometric resolution than the previous Landsat series which offers the possibility that, if well processed, the data will be more suitable for effective monitoring of coastal and inland waters. In this paper, as part of a validation of Landsat 8 surface reflectance over water surfaces, some issues in calibration and radiative transfer modelling are investigated. Atmospheric correction using the MODTRAN 5.4 radiative transfer model is applied to Landsat 8 images at a site in Northern Queensland where ground aerosol and water reflectance measurements are available from an AERONET site to create a matched data series. The atmospheric corrections included aerosol and Rayleigh scattering, gas and aerosol absorption as well as sky and sun glint effects. The surface reflectance values from Landsat 8 were then compared with surface reflectance measurements. The results show that with a suitable solar irradiance model and accounting for surface roughness, the retrieved surface reflectance values have good agreement with surface measured values. It also achieves an acceptable reflectance signature for inland and ocean water. These signature are very important for inland water quality and shallow water bathymetry application. Presented at the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS2019) - https://igarss2019.org/

Many atmospheric correction schemes of radiance-based optical satellite data require the selection of normalized solar spectral irradiance models at the top of atmosphere (TOA). However, there is no scientific consensus in literature as to which available model is most suitable. This article examines five commonly used models applied to Landsat 8 Operational Land Imager (OLI) TOA radiance and reflectance products to assess the accuracy and stability between models used to derive surface reflectance products. It is assumed that the calibration of the United States Geological Survey (USGS) Landsat 8 OLI TOA reflectance and radiance products are accurate to currently claimed levels. The results show that the retrieved surface reflectance can exhibit significant variations when different solar irradiance models are used, especially in the OLI coastal blue band at 443 nm. From the five solar irradiance models, the Kurucz 2005 model showed the least bias compared with OLI TOA reflectance product and least variance in surface reflectance. Furthermore, improvement was obtained by adjusting the total solar irradiance (TSI) normalization, and additional validation was provided using observed in situ water leaving reflectance data. The results from this article are particularly relevant to aquatic applications and to satellite sensors that provide TOA radiance such as previous Landsat and other current and historical missions. <b>Citation:</b> F. Li, D. L. B. Jupp, S. Sagar and T. Schroeder, "The Impact of Choice of Solar Spectral Irradiance Model on Atmospheric Correction of Landsat 8 OLI Satellite Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 4094-4104, May 2021, doi: 10.1109/TGRS.2020.3011006.

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established. <b>Citation:</b> Li, F.; Jupp, D.L.B.; Schroeder, T.; Sagar, S.; Sixsmith, J.; Dorji, P. Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-BasedWater-Leaving Reflectance Using Match-Up Sites in Australian CoastalWaters. Remote Sens. 2021, 13, 1927. https://doi.org/10.3390/rs13101927

A `weighted geometric median’ approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 OLI observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted.

An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 – 2018). The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8’s OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

A `weighted geometric median' approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 Operational Land Image (OLI) observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. <b>Value: </b>Has broad application in mapping surface geochemistry and mineralogy of exposed soil and bedrock. Has applications in geological mapping and natural resource management including mapping of soil characteristics. <b>Scope: </b>Two enhanced bare earth products have been generated reflecting different Landsat satellites and acquisition periods. The first only uses Landsat 8 observations from 2013 to 2018. The second incorporates the full 30+ year archive combining Landsat 5, 7, and 8 from 1986 to 2018.

<b>This record was retired 15/09/2022 with approval from S.Oliver as it has been superseded by eCat 146091 DEA Water Observations Statistics (Landsat)</b> In previous versions of WOfS, the basic water classifications, statistical summaries and confidence products were contained within one product with several datasets. As of version 2.1.5, WOfS is split into three products: Water Observation Feature Layers (WO_25_2.1.5), Summary Statistics (WO-STATS_25_2.1.5), and Filtered Summary Statistics (WO-FILT-STATS_25_2.1.5). This product is Water Observations from Space - Filtered Statistics (WO-FILT-STATS), consisting of a Confidence layer that compares the WO-STATS water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WO-STATS water summary where Confidence is low. The Filtered Water Summary provides the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WO-FILT-STATS consists of the following datasets: Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. This layer gives a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist.