Spatial Downscaling Based on Spectrum Analysis for Soil Freeze/Thaw Status Retrieved From Passive Microwave

Abstract

Seasonal variations of frozen soil can be effectively monitored at regional scales by using passive microwave remote sensing techniques. However, low spatial resolution of passive microwave remote sensing considerably constrains its application at local scales. Therefore, an effective spatial downscaling approach for frozen soil monitoring based on passive microwave remote sensing is essential to improve the application of passive microwave remote sensing for frozen soil monitoring at both regional and local scales. The present study is aimed to develop an effective spatial downscaling approach with spectrum analysis for frozen soil monitoring based on passive microwave remote sensing. The feasibility of the proposed spatial downscaling approach was investigated and discussed with the field <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> field observations data in northeastern China. The result obtained revealed a quite similar relationship of power spectral density (PSD) and spatial frequency with that between original low spatial resolution and high spatial resolution images in the frequency domain. The amplitude information in unresolved higher spatial resolution image thus can be estimated approximated by the relationship of the PSD with spatial frequency of original low spatial resolution image, whereas the phase information can be extracted by some traditional methods, such as resampling (RES) or geographically weighted regression (GWR) method. The spatial downscaling approach based on spectrum analysis can not only take spatial heterogeneity into account but also reveals the spatial characteristics of the surface soil freeze/thaw status. In addition, it was found that the phase information determined the spatial heterogeneity of downscaled results of surface soil frozen/thaw status.