ALGORITHMS

SEBAL AND ET-LOOK

SEBAL and ET-Look are the core algorithms eLEAF uses to produce PiMapping® data components. Both solve the energy balance at the earth’s surface. Essentially, they look at how much energy is available from the sun for any given day. Some of this energy is reflected back into space and some of it is consumed by processes at the earth surface, such as soil and air temperature changes. The residual amount is available for crop growth and associated water use. Our algorithms translate this residual energy into absolute numbers. Crop growth in kg/ha and crop water consumption in mm/week. For a better understanding of our algorithms feel free to contact us.

METEO-LOOK

To produce our core data products, eLEAF needs some key meteorological input parameters. Unfortunately those are not always readily available. In remote areas weather stations are few and far between, which makes simple data interpolation of the nearest ground stations inaccurate. To overcome this problem eLEAF has developed Meteo-Look. Besides measurements from meteorological ground stations, Meteo-Look adds satellite based data and pixel specific physical conditions into the equation.

The outputs are air temperature, wind speed and relative humidity on a grid of 250m. These are the main parameters influencing the crop production process. Having them available at this resolution substantially improves the quality of eLEAF’s biomass production and evapotranspiration data.

IRRI-LOOK

Irri-Look is the underlying algorithm for eLEAF’s irrigation planner which supports farmers to irrigate the right amount, at the right time and in the right location. Irri-Look combines the energy balance and the soil water balance per pixel to calculate the volume of water available for plant growth in the root zone. Combined with the weather forecast, water availability can be predicted up to 5 days in advance. Crucial water stress levels will prompt a message via email or SMS to warn farmers to irrigate. The model runs independent of any soil sensor input data, however additional field inputs from farmers do improve the accuracy. Best results are achieved when information on crop type, dates of sowing and emergence, rooting depth, irrigation gifts and groundwater levels are incorporated.