Institute for Hydrology and Water Management (HyWa), University of Natural Resources and Life Sciences, Vienna, Austria
Abstract. The large number of spatially distributed earth observation products, i.e. time series of surface emissions and reflectances at different wavelengths with increasing spatial resolution, contribute to the derivation of surface characteristics, e.g. vegetation or soil parameters in the environmental sciences. These derivatives usually build upon complex algorithms consisting of atmospheric corrections and process descriptions.
The testing scheme presented here seeks a different approach to identifying these surface characteristics that control the generation of such observation time series. Spatially distributed patterns of these characteristics of different persistence usually dominate parts of a time series because of their very specific reaction to and interaction with environmental influences. We test these characteristics' patterns for their existence in a rotated vector space of elementary patterns derived from a principal component analysis of an observational time series. With the result of this test we can then make valid assumptions, e.g. with regard to the importance of the surface properties for the emittance or reflectance, or their spatial uncertainties.
We demonstrate the functionality of this rather simple test algorithm for a synthetic and fully traceable example, and an application in a medium hydrological catchment for a time series of thermal satellite data. Possible future applications for this scheme are the prioritization and improvement of model input, data assimilation, or the evaluation and validation of model output.
How to cite. Müller, B., Bernhardt, M., and Schulz, K.: PAttern REtrieval or deNegation Testing Scheme (PARENTS) v.1.0 – Identifying the degree of presence of given patterns in spatial time series, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2019-563, 2019.
Received: 22 Oct 2019 – Discussion started: 03 Dec 2019
Time series of thermal remote sensing images include more information than usually used. Land surface related processes are combined into a single image. Activity of these processes change from image to image. Thus, information on land surface characteristics is to be found somewhere in between the images.
We provide an algorithm to test the presence of such characteristics within a set of images. The algorithm can be used for process understanding, model evaluation, data assimilation, etc.
Time series of thermal remote sensing images include more information than usually used. Land...