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  • 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 Hainich forested hill chain in the state of Thuringia in Germany 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 Europe 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 Denmark 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 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.

  • The BACI Surface State Vector (SSV) dataset for the Horn of Africa 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 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.