Researchprojects

BAB 050/21: Soil Data Analysis - Financial Soil Estimation and Soil Mapping (BODAT)

Objective

In the past, there have been repeated efforts to merge the soil evaluation map with the agricultural soil map. It has been shown that the geometries of the delineated areas can differ significantly. This does not necessarily mean that the geometry of one or the other map is "wrong", but that the recording systems are oriented differently with regard to their objectives and methodological approaches.
By comparing the data sets and their underlying methods, which led to the respective data, the prerequisites are created to identify and work out synergies in order to be able to use both data bases in the best possible way. The comparison with results from the GIS-ELA project as well as with satellite imagery should enable conclusions to be drawn about the geometric data.
The use of the recently available spatially, temporally and spectrally very high resolution Coper-nicus Sentinel-2 satellite imagery holds new potential for improving the available soil data sets in Austria, especially with respect to the current yield capacity of soils.
The subject of this project will be to develop an adequate method for the derivation of this parameter from the satellite images.
The Federal Institute for Agricultural Economics and Mining Research (BAB) has built up a multidimensional data cube (ODC, open.datacube.org) with the satellite imagery needed for the analysis and other raster data sets from the BMLRT departmental GIS (La-serscan elevation models, normalized surface models, etc.) during the last year. This hardware and software infrastructure is intended for the analysis in the project.

Initial situation

Two years have been estimated for the total project duration. This time period seems appropriate in that the first year will require preparation of the ground data as well as the remote sensing data. In addition, data contents will be updated or verified by accompanying field surveys and, if necessary, data gaps will be closed. In the second year, an analysis of the processed data will take place and, in parallel, a guideline will be elaborated in order to be able to use area-related soil information in an optimized way for precision agriculture in the future. The subsequent dissemination of the knowledge gained via the Chamber of Agriculture will also require sufficient time resources.

Work 2021

  • Acquire all available data from financial soil estimation and agricultural soil mapping, as well as Sentinel-2 data and results from the GIS-ELA project.
  • Pilot areas: Selection of five areas of at least one square kilometer in size in the area of the four participating farms in the GIS-ELA project. In addition, a fifth area will be defined in the area of the experimental plots of HBLFA Raumberg-Gumpenstein, so that grassland sites are also included. The spatial distribution of the areas over Austria allows an estimation of a possible application of the method to the whole of Austria.
  • Comparison of estimation and mapping data and identification of comparable data sets and those data sets that are only available for estimation or mapping.
  • Checking of comparable data sets for the necessity of processing in order to be able to actually compare them (e.g. translation of textures of soil estimation into ÖN L 1050, correction of results of deviating analytical methods (pH, Corg, etc.)).
  • Comparison of maps and identification of possible causes for significant geometry deviations, which obviously do not result from inaccuracies.
  • Download and pre-processing (e.g. spatial and atmospheric corrections) of all available satellite data in the project areas Derivation of remotely sensed variables (leaf area index or NDVI) as an indicator of productivity Comparison of results with Sentinel-2 data and data from the GIS-ELA project.
  • Estimation of the added value of harmonized and merged data from both datasets for those areas that overlap in content.
  • Documentation of the workflows from data acquisition to the final structuring of the data, as well as the necessary steps for harmonization.
  • Elaboration of recommendations (guidelines) for optimized use of area-related soil information (soil data strategy).

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