Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau
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 spatiotempor...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/13/3063 |
| _version_ | 1797434052576280576 |
|---|---|
| author | Zixuan Hu Linna Chai Wade T. Crow Shaomin Liu Zhongli Zhu Ji Zhou Yuquan Qu Jin Liu Shiqi Yang Zheng Lu |
| author_facet | Zixuan Hu Linna Chai Wade T. Crow Shaomin Liu Zhongli Zhu Ji Zhou Yuquan Qu Jin Liu Shiqi Yang Zheng Lu |
| author_sort | Zixuan Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:25:47Z |
| format | Article |
| id | doaj.art-f3d47f3a4a644c2c8ea22401556896d5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:25:47Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f3d47f3a4a644c2c8ea22401556896d52023-12-01T21:40:32ZengMDPI AGRemote Sensing2072-42922022-06-011413306310.3390/rs14133063Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet PlateauZixuan Hu0Linna Chai1Wade T. Crow2Shaomin Liu3Zhongli Zhu4Ji Zhou5Yuquan Qu6Jin Liu7Shiqi Yang8Zheng Lu9State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaHydrology and Remote Sensing Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USAState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaForschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), 52428 Jülich, GermanyState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSoil 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.https://www.mdpi.com/2072-4292/14/13/3063soil moisturedownscalingoptimizationwavelet transformQinghai–Tibet Plateau |
| spellingShingle | Zixuan Hu Linna Chai Wade T. Crow Shaomin Liu Zhongli Zhu Ji Zhou Yuquan Qu Jin Liu Shiqi Yang Zheng Lu 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 soil moisture downscaling optimization wavelet transform Qinghai–Tibet Plateau |
| title | Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau |
| title_full | Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau |
| title_fullStr | Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau |
| title_full_unstemmed | Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau |
| title_short | Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau |
| title_sort | applying a wavelet transform technique to optimize general fitting models for sm analysis a case study in downscaling over the qinghai tibet plateau |
| topic | soil moisture downscaling optimization wavelet transform Qinghai–Tibet Plateau |
| url | https://www.mdpi.com/2072-4292/14/13/3063 |
| work_keys_str_mv | AT zixuanhu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT linnachai applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT wadetcrow applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT shaominliu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT zhonglizhu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT jizhou applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT yuquanqu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT jinliu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT shiqiyang applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau AT zhenglu applyingawavelettransformtechniquetooptimizegeneralfittingmodelsforsmanalysisacasestudyindownscalingovertheqinghaitibetplateau |