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Agriculture 2011
Developing a High
Spatial and Temporal
Resolution Database for
Meteorological Based
Agronomical Models
Dr. Offer Beeri and Shay Mey-tal
money on soil/plant sensing and sampling. The main
goal of this project was to replace this method of hand
sampling with a computer-based system. To achieve
this goal, satellite imagery was integrated with climate
dataset in a geographic information system (GIS),
allowing for the collection of the data and the processing
of daily reports for field crops in the project.
Figure 1. Noon-time temperature, 28-March-2009, as captured by
climate stations network (A) and satellite imagery (B).
Local meteorology is an important part of agriculture
crop monitoring as correct management incorporates
crop growth and growth rate with weather data in order
to determine irrigation amounts and timing. The most
important climate data for this monitoring are growing
degree-days (GDD) and evapo-transpiration (ET), where
the former represents the accumulated temperature required
for crop growth, and the latter characterizes the
loss of water to the atmosphere. Both are necessary to
ensure that the supplied water amounts are calculated
based on the current growth rate and the loss of water.
Most farmers are dependent upon climate stations
located 30-50 km (20-30 miles) from each other (Figure
1A). With local changes in topography, soil and drainage,
the huge spatial variability between each pair of stations
does not allow for effective monitoring of local crop
fields (Figure 1B).
As a result, farmers invest large amounts of time and
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Scientific background:
The main method to determine the amounts of water
during any crop irrigation is calculating the potential
ET, multiplied by crop coefficients that are based on
crop growing models. These variables are calculated
from weather stations and known experimental data to
represent the day-to-day changes. Yet, as the spatial
distribution is greater than the average field size, local
differences are not recognizable. Numerous researchers
have attempted to resolve this issue by integrating
remote imagery. These projects have illustrated that as
crops become dryer and require more water, the greater
the difference between crop and air temperatures
(Moran, 1994). To ensure that crop growth stages do
not affect this model, the vegetation temperature is
normalized by the vegetation vigor, both mapped by
satellite imagery. This index represents the vegetation
resistance to transpiration (Nemani and Running, 1989)
with higher values indicating water-stress. Integration
of this method will allow for the mapping of differences
between neighboring crop fields, as well as inside
any plot, and agriculture growth models that use crop
coefficients for monitoring will enable updated irrigation
amounts for each field.
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