Modelling and Simulation (M & S)

SBI uses a range of models to describe, explain and understand various geospatial observations (i.e. spatiotemporal) and processes. Once we have developed a model, we can harness its predictive power by combining it with simulation techniques (e.g. Monte Carlo) to obtain different “what-if” settings. We simulate a variety of scenarios and explain the possible outcomes from the influence of different settings. SBI’s yield prediction model, for instance, accounts for different scenarios, including drought or flood, and their effects on plant physiology. We also pay careful attention to key M&S issues, such as their usefulness, efficiency, limitations, calibration, testing and validation, as well as their reliability and implementation in efficient computer programs. An overview of our regular processes is set out below:

  • One dimensional models: Here we use curve fitting techniques, combined with integral equation, to model crop growth performances in the various phenological stages of plant development
  • Models with multivariate dependencies: Ascertaining the best fit in the case of multidimensional dependency (e.g. weight factors influenced by the temperature, precipitation) for a specific crop in a specific region in order to train and localize our models.
  • Filter models: Satellite imagery may contain clouds or, due to its spatial resolution, the pixel reflectance values may consist of the reflectance of a mixture of crops. Our rule-based model allows us to filter out the crops we are not interested in.
  • Similarity models: Models featuring a similarity analysis of time- and spatial-varying processes are a specialist area of SBI. The model enables us to compare the properties of a pair of agricultural land sites.
  • Time-varying process models: Soil moisture determination is a typical example where radar satellite signals can be used in time-varying process modelling. We can calculate the surface soil moisture with the properties of the reflected waves, for instance, and, with the help of local probing by solving diffusion equations, efficiently determining the soil moisture in changing depths.
  • Simulation techniques: We deploy simulation techniques, such as the Monte Carlo technique, which result in statements of defined prognostic confidence. Once we have a well-tested model of yield results, we can vary the influencing factors to determine the yield results in different scenarios artificially
  • Implementing models and simulations into our IT environment: SBI has translated a large part of the afore-mentioned models into software. To facilitate scalability we generally use the ORACLE database when implementing our models. SBI’s Spatial Data Infrastructure, in a high-speed distributed network, ensures efficient access to spatial information for non-geotech scientists

SBI embraces highly atomized data quality checking and data filling procedures, thereby avoiding the axiom of “garbage in, garbage out”, meaning that poor quality input will produce faulty output. The data and information derived are therefore guaranteed to be of a very high quality.

Data processing routine automation

In the era of Big Data, having processing routines which can handle a huge volume of different types of data is essential. Growing geospatial data requirements make the need for efficient processing algorithms and infrastructure more pressing as one of the key implications of project cost optimization. With its sights set firmly on this aspect, SBI has automated and realized the software for several processing routines, which allows us to provide global support to our customers at very short notice.

SBI places huge emphasis on having dependable pre-processing routines, data gap filling, data assimilations (imagery from different sensors with varying spatial resolution), advanced satellite image analysis techniques and interpretations, and on ensuring efficient handling capacities and capabilities for BIG geospatial data with the aid of machine learning techniques.

SBI has also mastered the requisite cutting-edge technologies and has in-depth experience in handling a range of public and commercially available satellite imagery. These capabilities assist us in monitoring continuous crop health/biomass development, conducting Remote Field Analysis (RFA), Crop Specific Analysis (CSA) (i.e. land use/crop, cover classification), Crop Disease Detection (CDD), to name a few of the activities).

Developing and supporting business models

SBI develops and supports business models using various methods (e.g. ITIL) and drawing on its extensive experience from project collaboration with public authorities and power supply companies. SBI provides you with guidance on how to use the geo information efficiently to your advantage. Public authorities, for instance, are required to ensure that their geo data management complies with INSPIRE. Backed by its in-depth expertise, SBI can advise its customers on the administrative aspects, as well as in on the topic of geo information systems.

Geo information, in the form of satellite images, for example, harbours huge potential for value creation. Realizing such value necessitates intelligent business models. We analyze customer benefits and design the business processes, while evaluating them in terms of the costs incurred. We conduct a cost-benefit analysis to ascertain the financial requirements and calculate the profitability. We follow this up by developing rollout plans, defining milestones and the criteria for economic fine tuning. By adopting this approach, we actively support our customers in developing stable, successful and profitable business models which realize the value potential of spatial information.

SBI also understands the requirements placed on earth observation information, combined with other spatial information which can be effectively applied to different stages of a project/programme. It knows all about strategic planning (definition of priority intervention and understanding risks and vulnerabilities), implementation and rollout, (simulating how and to what extent geo properties will be influenced and develop under realistic assumptions), monitoring and evaluation (involving additional SBI checks on whether your project was successful by applying the appropriate monitoring), and other services.

Experience in developing business automation and management systems

SBI develops Spatial Data Infrastructure (SDI) and back-office solutions which combine Earth Observation Information, agrostatistics, weather and soil data, with user-friendly interfaces.

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