The accuracy of surface reflectance estimation for satellite sensors using radiance-based calibrations can depend significantly on the choice of solar spectral irradiance (or solar spectrum) model used for atmospheric correction. Selecting an accurate solar spectrum model is also important for radiance-based sensor calibration and estimation of atmospheric parameters from irradiance observations. Previous research showed that Landsat 8 could be used to evaluate the quality of solar spectrum models. This paper applies the analysis using five previously evaluated and three more recent solar spectrum models using both Landsat 8 (OLI) and Landsat 9 (OLI2). The study was further extended down to 10 nm resolution and a wavelength range from Ultraviolet A (UVA) to shortwave infrared (SWIR) (370–2480 nm) using inversion of field irradiance measurements. The results using OLI and OLI2 as well as the inversion of irradiance measurements were that the more recent Chance and Kurucz (SA2010), Meftah (SOLAR-ISS) and Coddington (TSIS-1) models performed better than all of the previous models. The results were illustrated by simulating dark and bright surface reflectance signatures obtained by atmospheric correction with the different solar spectrum models. The results showed that if the SA2010 model is assumed to be the “true” solar irradiance, using the TSIS-1 or the SOLAR-ISS model will not significantly change the estimated ground reflectance. The other models differ (some to a large extent) in varying wavelength areas. <b>Citation:</b> Li, F.; Jupp, D.L.B.; Markham, B.L.; Lau, I.C.; Ong, C.; Byrne, G.; Thankappan, M.; Oliver, S.; Malthus, T.; Fearns, P. Choice of Solar Spectral Irradiance Model for Current and Future Remote Sensing Satellite Missions. <i>Remote Sens.</i> <b>2023</b>, <i>15</i>, 3391. https://doi.org/10.3390/rs15133391

CEOS Analysis Ready Data for Land (CARD4L) are satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets [1]. In this paper, key input data (e.g. aerosol optical depth, precipitable water, BRDF parameters) needed for atmospheric and BRDF corrections of Landsat data are identified and a sensitivity analysis is conducted using outputs of a physics based atmospheric and BRDF model. The results show that aerosol impacts more on the visible bands where the average variation of reflectance could reach 0.05 of reflectance unit. The variation over dark targets can be much higher so that it is a critical parameter for aquatic applications. By contrast, precipitable water (water vapor in the rest of the paper) only impacts the near-infrared (NIR) and shortwave (SWIR) bands and the extent of change is much smaller. BRDF parameters impact time series most on winter and summer images of highly anisotropic areas and when they are normalized to 45º solar angle. Different BRDF levels for different spectrum ranges not only impact the magnitude of reflectance, but also the signature for these areas. It seems that it is necessary to normalize surface BRDF to ensure time series consistency of the Landsat ARD product. Abstract presented at 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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