Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra

Various instruments are used to create images of the earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high-resolution spectral measurements may be too costly to perform on a large sample, and therefore, lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. This work addresses this problem using virtual sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. The models we use Are multilayer perceptrons, support vector machines (SVMs) with radial basis function kernels, and SVMs with mixture density Mercer kernels. We demonstrate this method by using models trained on the high spectral resolution Terra Moderate Resolution Imaging Spectrometer (MODIS) instrument to estimate what the equivalent of the MODIS 1.6-μm channel would be for the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR/2) instrument. The scientific motivation for the simulation of the 1.6-μm channel is to improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.