Modelling high resolution RS data with the aid of coarse resolution data and ancillary data

Abstract

In environmental applications, the data have a large variety of resolutions carrying information at different scales. Various approaches have been used to include in models information from sources at different scales combining multi-resolution products in order to integrate the spatio-temporal variability of sub-pixel pattern. A methodology is proposed for the integration of the results obtained with a geostatistical downscaling algorithm, based on block-to-point-kriging, in a General Additive Models interpolation framework to enhance the spatio-temporal resolution of remote sensing data. This allows a good reproduction of the overall spatial pattern of the target images and of their local values. The developed framework was tested using MODIS land surface temperature (LST) with the thermal band of Landsat in a situation of high contamination of clouds for the high resolution dataset. The method proved to be flexible and able to blend data from different sensors maintaining the finer spatial structure of the higher resolution data. The method combines strengths from different approaches: (1) it uses of information held in covariates to provide more accurate results; (2) it is applicable to a variety of remote sensing products as the method does not rely on predetermined functional relationships; (3) it can cope with cloud-rich high resolution images as only a subset of high resolution pixels is needed. This approach is general and can be used with numerous combinations of high and low resolution images, such as MODIS-derived variables, using related band ratios from Landsat or other higher resolution sensors. This approach is a valuable addition to space–time measuring and modelling of ecosystems functions from remote sensing.