Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau

Zixuan Hu, Linna Chai, Wade T. Crow, Shaomin Liu, Zhongli Zhu, Ji Zhou, Yuquan Qu, Jin Liu, Shiqi Yang, Zheng Lu

Research output: Contribution to JournalArticleAcademicpeer-review

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 (GMs) 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 (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The 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 unfrozen season, compared to 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 OWCMs over GMs 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.
Original languageEnglish
Article number3063
JournalRemote Sensing
Volume14
Issue number13
DOIs
Publication statusPublished - 1 Jul 2022
Externally publishedYes

Funding

The work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20100101), the National Natural Science Foundation of China (42171319, 41871241), and the State Key Laboratory of Earth Surface Processes and Resource Ecology (2021-ZD-04). The presentation, findings, and conclusions in this publication are those of the authors, and should not be construed to represent any official USDA or U.S. Government determination or policy. The USDA ARS is an equal opportunities employer. Funding: The work was supported by the Strategic Priority Research Program of the Chinese AcademyofSciences(XDA20100101),theNationalNaturalScienceFoundationofChina(42171319, 41871241), and the State Key Laboratory of Earth Surface Processes and Resource Ecology (2021-ZD-04). The presentation, findings, and conclusions in this publication are those of the authors,andshouldnotbeconstruedtorepresentanyofficialUSDAorU.S.Governmentdetermina-tionorpolicy.TheUSDAARSisanequalopportunitiesemployer.

FundersFunder number
Strategic Priority Research Program of the Chinese AcademyofSciences
U.S. Department of Agriculture
National Natural Science Foundation of China41871241, 42171319
Chinese Academy of SciencesXDA20100101
State Key Laboratory of Earth Surface Processes and Resource Ecology2021-ZD-04

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