Hydrological and Agro-ecological Data Acquisition using Remotely Sensed Data from Satellite Sensors

Abstract

Research at Imperial College developed a methodology to extract time-space dependent hydrological parameters form Remote Sensing data in order to predict land surface temperature from satellite observations. Time series of actual ET were estimated using both the SVAT model and the water balance model. The comparison of cumulative ET between the two different methods shows a good agreement and validates the developed parameterisation. Plant growth as it is described by the temporal signal of SAR data and interpreted by the optical data for the presence and status of vegetation in a distributed manner over the entire area, is proven to be very close to the traditionally reliable method of estimating losses using a hydrologic engineering model calibrated against hourly river flow data. The cumbersome process of selecting parameters for every discredited modelling unit has been replaced by a simple automated procedure after converting the SAR temporal signal into a dimensionless growth factor. Different runs of the system of SVAT columns employing different functions linking the derived growth factor and biophysical parameters, as well as, different sets of maximum and minimum values for the time dependent plant growth parameters were used.