About Vista
Methodology
Vista’s core competencies are the analysis of satellite data an environmental modelling. The existing methods are constantly further developed to ensure that the latest scientific findings are always put into practice.
Unlike many other companies, Vista uses complex physics-based models to analyse satellite data and model environmental processes. Machine learning and AI methods complement the Digital Twinning processes.
All methods used can be easily applied worldwide.
Satellite data analysis
Vista uses the radiative transfer model SLC to analyse optical satellite data. Thereby the saturation problems of common indices (such as NDVI) can be avoided.
The SLC model allows to derive information about various plant parameters. In addition to the derivation of the green leaf area, a key parameter for assessing stand development, for example the plant water content or the maturity of the crop can also be derived as well. SLC delivers plant parameters in physical values, a necessity to calculate precise prescription maps.
Supplementary to optical satellite data, Vista also uses RADAR and thermal satellite data where their use is appropriate. Vista uses proprietary software to pre-process satellite data and has a large archive of pre-processed satellite scenes to provide customers with products very quickly. The satellite data archive currently includes Europe and some African countries, but can be expanded at any time to include our customers’ target countries. Vista uses the latest cloud technologies to handle the huge amounts of data.
Environmental modelling
In addition to the evaluation of satellite data, environmental modelling is the core expertise of Vista GmbH. Vista uses the in-house environmental model PROMET. It was developed in cooperation with the LMU Munich Department of Geography.
PROMET is a physically based model that simulates the components of water and energy balance for the main areas of soil, vegetation and atmosphere and can be adapted to any place worldwide. Satellite data can be assimilated into the modelling process in order to have a constant comparison with measurements.
In contrast to statistical models, since the physical basis for plant growth is the same worldwide, PROMET can be used worldwide without prior calibration. Adjustments may be necessary to reflect different land use practices.
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