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

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.

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

“…This dataset displays high accuracy when compared with the measured data. The Daily 0.01 • × 0.01 • Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005, 2010, 2015, 2017, and 2018) was downloaded from National Tibetan Plateau Data Center [43][44][45]. This dataset was downscaled from Land Surface Soil Moisture Dataset of SMAP Time-Expanded Daily 0.25 • × 0.25 • over the Qinghai-Tibet Plateau Area.…”

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

“…This approach, characterized by its simplicity and accessibility, contrasts with the intricate methodologies employed by other researchers in optimizing and fusing reanalysis data. These methodologies encompass sophisticated techniques, including artificial neural networks [59], wavelet transform methods [60], genetic algorithms [61,62], and machine learning [63,64]. However, it is noteworthy that the efficacy of the aforementioned optimization is constrained to situations akin to CMADS, where certain metrics exhibit suboptimal performance relative to others.…”

Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

“…The input satellite data sources include the 16-day NDVI from MOD13A2 with 1-km spatial resolution, 8-day LAI and FPAR from the Global LAnd Surface Satellite (GLASS) with 500-m spatial resolution (Xiao et al, 2015;Xiao et al, 2014), instantaneous 1-km all-weather LST dataset (both daytime and nighttime) from the National Tibetan Plateau Data Center (Zhang et al, 2021b), and the daily SM dataset from the European Space Agency Climate Change Initiative (ESA CCI) program with 0.25-degree spatial resolution. We used a wavelet transform (WT) method provided by Hu et al (2022) combined with finer resolution satellite products (i.e., LAI, FPAR, LST) to downscale the SM dataset to 1km spatial resolution.…”

“…For instance, a previous study found that the effects of topography, vegetation, precipitation, and LST greatly impact the spatial distribution of SM, resulting in large biases in the existing SM products of the TP (Liu et al, 2021). At the same time, the downscaling of coarser resolution SM products will also affect the accuracy of ET estimation (Hu et al, 2022;Sabaghy et al, 2020). Therefore, uncertainties could be inherited through errors from these input data sources.…”