Global solar exposure is the total amount of solar energy falling on a horizontal surface. The hourly global solar exposure is the total solar energy for one hour. Typical values for hourly global exposure range up to 4 MJ/m2 (megajoules per square metre). The values are usually highest in the middle of the day and around summer, with localised variations caused mainly by variations in atmospheric conditions, primarily cloudiness. See metadata statement for more information.

The Australian Solar Energy Information System V3.0 has been developed as a collaborative project between Geoscience Australia and the Bureau of Meteorology. The product provides pre-competitive spatial information for investigations into suitable locations for solar energy infrastructure. The outcome of this project will be the production of new and improved solar resource data, to be used by solar researchers and the Australian solar power industry. it is aimed to facilitate broad analysis of both physical and socio-economic data parameters which will assist the solar industry to identify regions best suited for development of solar energy generation. It also has increased the quality and availability of national coverage solar exposure data, through the improved calibration and validation of satellite based solar exposure gridded data. The project is funded by the Australian Renewable Energy Agency. The ASEIS V3.0 has a solar database of resource mapping data which records and/or map the following Solar Exposure over a large temporal range, energy networks, infrastructure, water sources and other relevant data. ASEIS V3.0 has additional solar exposure data provided by the Bureau of Meteorology. - Australian Daily Gridded Solar Exposure Data now ranges from 1990 to 2013 - Australian Monthly Solar Exposure Gridded Data now ranges from 1990 to 2013 - Australian Hourly Solar Exposure Gridded Data now ranges from 1990 to 2012 ASEIS V3.0 also has a new electricity transmission reference dataset which allows for information to be assessed on any chosen region against the distance to the closest transmission powerline.

Hourly direct normal solar exposure is the total amount of direct beam solar energy falling over one hour on a surface whose orientation is maintained perpendicular to the solar beam. Typical values for hourly direct normal exposure range up to around 3 MJ/m2 (megajoules per square metre). The values are usually highest in clear skies and decrease rapidly with increasing cloudiness, and also decrease to a lesser extent with increasing haziness and decreasing solar elevation. Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer and lowest during the winter or very cloudy days. See LINEAGE below for more information.

Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer, and lowest during winter or very cloudy days. The monthly means are derived from the daily global solar exposure. See metadata statement for more information.

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.

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