This dataset consists of data from the Earth Radiation Budget Experiment (ERBE) instrument on the 9th NOAA Sun-synchronous operational satellites (NOAA-9). NOAA-9 operated at an altitude of 852-km, with an equatorial crossing local time of 1430, having been launched in December 1984. The ERBE instrument's main aim was to provide accurate measurements of incoming solar energy and shortwave and longwave radiation reflected or emitted from the Earth back into space. This dataset contains colour images (shortwave/longwave/net radiation, albedo, clear-sky albedo, clear-sky shortwave/longwave/net radiation, and shortwave/longwave/net cloud forcing) from scanning radiometer on the NOAA-9 satellite. Monthly average values are included for the time periods during which the scanners were operational. This dataset is public, though NASA noted that this is intended for research purposes and the data has no commercial value.

This dataset consists of combined data from the Earth Radiation Budget Experiment (ERBE) instruments on the Earth Radiaition Budget Satellite (ERBS) and the 10th NOAA Sun-synchronous operational satellites (NOAA-9). ERBS was launched in October 1984 by the Space Shuttle Challenger (STS-41G) into an orbit at 603-km altitude, 57-deg. inclination. NOAA-10 operated at an altitude of 833-km, with an equatorial crossing local time of 0730, having been launched in November 1986. The ERBE instrument's main aim was to provide accurate measurements of incoming solar energy and shortwave and longwave radiation reflected or emitted from the Earth back into space. This dataset contains colour images (shortwave/longwave/net radiation, albedo, clear-sky albedo, clear-sky shortwave/longwave/net radiation, and shortwave/longwave/net cloud forcing) from scanning radiometers on the NOAA-10 ERBE satellites and for combined satellite cases. Monthly average values are included for the time periods during which the scanners were operational.

This dataset consists of data from the Earth Radiation Budget Experiment (ERBE) instrument on the 10th NOAA Sun-synchronous operational satellites (NOAA-10). NOAA-10 operated at an altitude of 833-km, with an equatorial crossing local time of 0730, having been launched in November 1986. The ERBE instrument's main aim was to provide accurate measurements of incoming solar energy and shortwave and longwave radiation reflected or emitted from the Earth back into space. This dataset contains colour images (shortwave/longwave/net radiation, albedo, clear-sky albedo, clear-sky shortwave/longwave/net radiation, and shortwave/longwave/net cloud forcing) from scanning radiometer on the NOAA-10 satellite. Monthly average values are included for the time periods during which the scanners were operational. This dataset is public, though NASA noted that this is intended for research purposes and the data has no commercial value.

The Earth Radiation Budget Experiment (ERBE) instrument aboard the NASA Earth Radiation Budget Satellite (ERBS) was launched from the Space Shuttle Challenger in October 1984 (STS-41G). The ERBE instrument's main aim was to provide accurate measurements of incoming solar energy and shortwave and longwave radiation reflected or emitted from the Earth back into space. This dataset collection contains colour images (shortwave/longwave/net radiation, albedo, clear-sky albedo, clear-sky shortwave/longwave/net radiation, and shortwave/longwave/net cloud forcing) from scanning radiometers on the three ERBE satellites and for combined satellite cases. Monthly average values are included for the time periods during which the scanners were operational.

The BACI Surface State Vector (SSV) dataset for the Kruger National Park track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.

The BACI Surface State Vector (SSV) dataset for Slovenia and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.

The BACI Surface State Vector (SSV) dataset for the Romanian fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.

The BACI Surface State Vector (SSV) dataset for Viterbo and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.

The BACI Surface State Vector (SSV) dataset for Souther African regional site provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.

The BACI Surface State Vector (SSV) dataset for the Southern Somalia fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.