This website uses cookies. By continuing to use this website you are agreeing to our use of cookies. 



BACI: System State Vector (SSV) land surface time series dataset for the Denmark fast track site, 2000-2015, v1.0

Update Frequency: Not Planned
Latest Data Update: 2019-05-22
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2019-11-14
DOI Publication Date: 2020-01-30
Download Stats: last 12 months
Dataset Size: 28 Files | 28GB


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.

Citable as:  Disney, M.; Chernetskiy, M.; Gomez-Dans, J.; Lewis, P.; Urban, M.; Schmullius, C. (2020): BACI: System State Vector (SSV) land surface time series dataset for the Denmark fast track site, 2000-2015, v1.0. Centre for Environmental Data Analysis, 30 January 2020. doi:10.5285/5f1d4afa090a4e5c9937ec3362ffd77e.
Abbreviation: Not defined
Keywords: BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Denmark, albedo, mircrowave, backscatter


Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Access to these data is available to any registered CEDA user. Please Login or Register for an account to gain access.
Use of these data is covered by the following licence: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Provided by Mathias Disney of the University College London BACI project team to CEDA for publication

Data Quality:
BACI data validated by Maxim Chernetskiy UCL project team
File Format:
netCDF version 4

Process overview

This dataset was generated by the computation detailed below.

BACI State Surface Vector Computation (SSV)


The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information.

The process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.
The main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.

Inputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains .

Input Description


Output Description


Software Reference


  • var_id: date_jul
  • var_id: date_jul_obs
  • units: string representation of date:
  • var_id: date_str
  • var_id: geo_transform
  • units: Julian day
  • var_id: julday
  • var_id: lat
  • units: latitude
  • var_id: lon
  • units: longitude
  • var_id: lst
  • var_id: lst_orig
  • var_id: lst_orig_sd
  • var_id: lst_sd
  • var_id: proj

Co-ordinate Variables

Temporal Range
Start time:
End time:
Geographic Extent