Applying a wavelet transform technique to optimize general fitting models for SM analysis: A case study in downscaling over the Qinghai-Tibet Plateau

Authors: HU, Z. - Beijing Normal University; CHAI, L. - Beijing Normal University; Crow, Wade; LIU, S. - Beijing Normal University; ZHU, Z. - Beijing Normal University; ZHOU, J. - Hohai University; QU, Y. - Chengdu University; YANG, S. - Capital Normal University; LU, Z. - Capital Normal University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/22/2022
Publication Date: 6/25/2022
Citation: Hu, Z., Chai, L., Crow, W.T., Liu, S., Zhu, Z., Zhou, J., Qu, Y., Yang, S., Lu, Z. 2022. Applying a wavelet transform technique to optimize general fitting models for SM analysis: A case study in downscaling over the Qinghai-Tibet Plateau. Remote Sensing. 14(13):3063. https://doi.org/10.3390/rs14133063.
DOI: https://doi.org/10.3390/rs14133063

Interpretive Summary: The quality of soil moisture products derived from remote sensing observations has improved significantly within the past decade. Such products are now being used in several key agricultural applications. However, despite this progress, they still suffer from important limitations including coarse spatial resolution and temporal gaps in their coverage. As a result, multiple studies have proposed downscaling and gap-filling approaches based on ancillary observations (e.g., vegetation health, land surface temperature, and topographic position) that are related to soil moisture. Most of these approaches are based on the application of statistical models to fit the relationship between these ancillary variables and soil moisture. This paper makes a major methodological advance in these efforts by proposing that such fitting occurs after the application of a wavelet transformation. Results demonstrate that such fitting (in wavelet space) significantly improves the quality of these statistical models and, as a result, our ability to improve the spatial resolution and temporal coverage of existing soil moisture products using ancillary observations. This approach will eventually be used to provide higher-resolution and more timely estimates of fine-scale soil moisture for agricultural monitoring applications.

Technical Abstract: Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GM) for SM analysis topics (e.g., gap filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method, OWCM) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (LAI, FVC, Albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST and Random Forest Soil Moisture (RFSM) data sets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are then analyzed using direct comparisons with in-situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. Results demonstrate that OWCM-derived SM products are generally closer to the in-situ SM observations and better capture in-situ SM dynamics during the wet season than the corresponding GM-derived SM product - which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCM over GM are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low frequency image features than in the regular space.