Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important?
The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Tai...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ya Liu [verfasserIn] Qing Zhu [verfasserIn] Kaihua Liao [verfasserIn] Xiaoming Lai [verfasserIn] Junbang Wang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Journal of Water and Climate Change - IWA Publishing, 2021, 12(2021), 5, Seite 1564-1579 |
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| Übergeordnetes Werk: |
volume:12 ; year:2021 ; number:5 ; pages:1564-1579 |
| Links: |
Link aufrufen |
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| DOI / URN: |
10.2166/wcc.2020.131 |
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| Katalog-ID: |
DOAJ075199777 |
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| 245 | 1 | 0 | |a Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? |
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| 520 | |a The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; | ||
| 650 | 4 | |a remote sensing | |
| 650 | 4 | |a soil hydrology | |
| 650 | 4 | |a soil moisture | |
| 650 | 4 | |a taihu lake basin | |
| 650 | 4 | |a watershed | |
| 653 | 0 | |a Environmental technology. Sanitary engineering | |
| 653 | 0 | |a Environmental sciences | |
| 700 | 0 | |a Qing Zhu |e verfasserin |4 aut | |
| 700 | 0 | |a Kaihua Liao |e verfasserin |4 aut | |
| 700 | 0 | |a Xiaoming Lai |e verfasserin |4 aut | |
| 700 | 0 | |a Junbang Wang |e verfasserin |4 aut | |
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10.2166/wcc.2020.131 doi (DE-627)DOAJ075199777 (DE-599)DOAJbdd01ba794a44b19a57935695a66bf6a DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Ya Liu verfasserin aut Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering Environmental sciences Qing Zhu verfasserin aut Kaihua Liao verfasserin aut Xiaoming Lai verfasserin aut Junbang Wang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 12(2021), 5, Seite 1564-1579 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:12 year:2021 number:5 pages:1564-1579 https://doi.org/10.2166/wcc.2020.131 kostenfrei https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a kostenfrei http://jwcc.iwaponline.com/content/12/5/1564 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 12 2021 5 1564-1579 |
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10.2166/wcc.2020.131 doi (DE-627)DOAJ075199777 (DE-599)DOAJbdd01ba794a44b19a57935695a66bf6a DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Ya Liu verfasserin aut Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering Environmental sciences Qing Zhu verfasserin aut Kaihua Liao verfasserin aut Xiaoming Lai verfasserin aut Junbang Wang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 12(2021), 5, Seite 1564-1579 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:12 year:2021 number:5 pages:1564-1579 https://doi.org/10.2166/wcc.2020.131 kostenfrei https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a kostenfrei http://jwcc.iwaponline.com/content/12/5/1564 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 12 2021 5 1564-1579 |
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10.2166/wcc.2020.131 doi (DE-627)DOAJ075199777 (DE-599)DOAJbdd01ba794a44b19a57935695a66bf6a DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Ya Liu verfasserin aut Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering Environmental sciences Qing Zhu verfasserin aut Kaihua Liao verfasserin aut Xiaoming Lai verfasserin aut Junbang Wang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 12(2021), 5, Seite 1564-1579 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:12 year:2021 number:5 pages:1564-1579 https://doi.org/10.2166/wcc.2020.131 kostenfrei https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a kostenfrei http://jwcc.iwaponline.com/content/12/5/1564 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 12 2021 5 1564-1579 |
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10.2166/wcc.2020.131 doi (DE-627)DOAJ075199777 (DE-599)DOAJbdd01ba794a44b19a57935695a66bf6a DE-627 ger DE-627 rakwb eng TD1-1066 GE1-350 Ya Liu verfasserin aut Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; remote sensing soil hydrology soil moisture taihu lake basin watershed Environmental technology. Sanitary engineering Environmental sciences Qing Zhu verfasserin aut Kaihua Liao verfasserin aut Xiaoming Lai verfasserin aut Junbang Wang verfasserin aut In Journal of Water and Climate Change IWA Publishing, 2021 12(2021), 5, Seite 1564-1579 (DE-627)62604703X (DE-600)2552186-X 24089354 nnns volume:12 year:2021 number:5 pages:1564-1579 https://doi.org/10.2166/wcc.2020.131 kostenfrei https://doaj.org/article/bdd01ba794a44b19a57935695a66bf6a kostenfrei http://jwcc.iwaponline.com/content/12/5/1564 kostenfrei https://doaj.org/toc/2040-2244 Journal toc kostenfrei https://doaj.org/toc/2408-9354 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_4046 AR 12 2021 5 1564-1579 |
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Downscaling of ESA CCI soil moisture in Taihu Lake Basin: are wetness conditions and non-linearity important? |
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The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; |
| abstractGer |
The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; |
| abstract_unstemmed |
The coarse spatial resolutions of satellite-based soil moisture (SM) products restrict their applications at smaller spatial scales. In this study, the monthly European Space Agency Climate Change Initiative SM data (ESA CCI SM) from 2000 to 2016 was downscaled from 25- to 1-km resolution in the Taihu Lake Basin, a typical humid area with complex terrain and land uses. The normalized difference vegetation index (NDVI) and land surface temperature (LST) were used as auxiliary data. The regional monthly mean ESA CCI SM values were classified into low value (0.24–0.30 m3m–3), mid-value (0.30–0.33 m3m–3) and high value (0.33–0.39 m3m–3) months by the K-means clustering algorithm. The linear (multiple linear regression) and non-linear (support vector machine) downscaling models were compared. In addition, whether building downscaling models based on wetness conditions could improve the accuracies was tested. Results showed that without considering wetness conditions, the linear method was slightly better than the non-linear method. However, linear models constructed based on wetness conditions performed the best, which demonstrated that wetness conditions should be considered in the downscaling process. Results of this study would improve the accuracies in downscaling satellite-based SM data, facilitating their applications at regional scales. HIGHLIGHTS The quality of ESA CCI SM was evaluated in Taihu Basin Lake.; Linear and non-linear downscaling methods were compared.; Wetness conditions should be considered in the downscaling process.; |
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