Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jianbo Wang [verfasserIn] Jinyang Wang [verfasserIn] Shunde Chen [verfasserIn] Jianbo Luo [verfasserIn] Mingzhi Sun [verfasserIn] Jialong Sun [verfasserIn] Jiajia Yuan [verfasserIn] Jinyun Guo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 7, p 1746
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:7, p 1746

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15071746

Katalog-ID:	DOAJ089347617

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520			\|a Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.
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allfields	10.3390/rs15071746 doi (DE-627)DOAJ089347617 (DE-599)DOAJa49941620db749eda0202b86864b5abe DE-627 ger DE-627 rakwb eng Jianbo Wang verfasserin aut Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q Jinyang Wang verfasserin aut Shunde Chen verfasserin aut Jianbo Luo verfasserin aut Mingzhi Sun verfasserin aut Jialong Sun verfasserin aut Jiajia Yuan verfasserin aut Jinyun Guo verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 7, p 1746 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:7, p 1746 https://doi.org/10.3390/rs15071746 kostenfrei https://doaj.org/article/a49941620db749eda0202b86864b5abe kostenfrei https://www.mdpi.com/2072-4292/15/7/1746 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 7, p 1746
spelling	10.3390/rs15071746 doi (DE-627)DOAJ089347617 (DE-599)DOAJa49941620db749eda0202b86864b5abe DE-627 ger DE-627 rakwb eng Jianbo Wang verfasserin aut Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q Jinyang Wang verfasserin aut Shunde Chen verfasserin aut Jianbo Luo verfasserin aut Mingzhi Sun verfasserin aut Jialong Sun verfasserin aut Jiajia Yuan verfasserin aut Jinyun Guo verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 7, p 1746 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:7, p 1746 https://doi.org/10.3390/rs15071746 kostenfrei https://doaj.org/article/a49941620db749eda0202b86864b5abe kostenfrei https://www.mdpi.com/2072-4292/15/7/1746 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 7, p 1746
allfields_unstemmed	10.3390/rs15071746 doi (DE-627)DOAJ089347617 (DE-599)DOAJa49941620db749eda0202b86864b5abe DE-627 ger DE-627 rakwb eng Jianbo Wang verfasserin aut Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q Jinyang Wang verfasserin aut Shunde Chen verfasserin aut Jianbo Luo verfasserin aut Mingzhi Sun verfasserin aut Jialong Sun verfasserin aut Jiajia Yuan verfasserin aut Jinyun Guo verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 7, p 1746 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:7, p 1746 https://doi.org/10.3390/rs15071746 kostenfrei https://doaj.org/article/a49941620db749eda0202b86864b5abe kostenfrei https://www.mdpi.com/2072-4292/15/7/1746 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 7, p 1746
allfieldsGer	10.3390/rs15071746 doi (DE-627)DOAJ089347617 (DE-599)DOAJa49941620db749eda0202b86864b5abe DE-627 ger DE-627 rakwb eng Jianbo Wang verfasserin aut Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q Jinyang Wang verfasserin aut Shunde Chen verfasserin aut Jianbo Luo verfasserin aut Mingzhi Sun verfasserin aut Jialong Sun verfasserin aut Jiajia Yuan verfasserin aut Jinyun Guo verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 7, p 1746 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:7, p 1746 https://doi.org/10.3390/rs15071746 kostenfrei https://doaj.org/article/a49941620db749eda0202b86864b5abe kostenfrei https://www.mdpi.com/2072-4292/15/7/1746 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 7, p 1746
allfieldsSound	10.3390/rs15071746 doi (DE-627)DOAJ089347617 (DE-599)DOAJa49941620db749eda0202b86864b5abe DE-627 ger DE-627 rakwb eng Jianbo Wang verfasserin aut Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q Jinyang Wang verfasserin aut Shunde Chen verfasserin aut Jianbo Luo verfasserin aut Mingzhi Sun verfasserin aut Jialong Sun verfasserin aut Jiajia Yuan verfasserin aut Jinyun Guo verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 7, p 1746 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:7, p 1746 https://doi.org/10.3390/rs15071746 kostenfrei https://doaj.org/article/a49941620db749eda0202b86864b5abe kostenfrei https://www.mdpi.com/2072-4292/15/7/1746 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 7, p 1746
language	English
source	In Remote Sensing 15(2023), 7, p 1746 volume:15 year:2023 number:7, p 1746
sourceStr	In Remote Sensing 15(2023), 7, p 1746 volume:15 year:2023 number:7, p 1746
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Jianbo Wang @@aut@@ Jinyang Wang @@aut@@ Shunde Chen @@aut@@ Jianbo Luo @@aut@@ Mingzhi Sun @@aut@@ Jialong Sun @@aut@@ Jiajia Yuan @@aut@@ Jinyun Guo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ089347617
language_de	englisch
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Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. 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author	Jianbo Wang
spellingShingle	Jianbo Wang misc water volume variation in Lake Qinghai misc multi-source altimeter satellite misc optical remote sensing satellite misc satellite applications misc Qinghai–Tibet Plateau misc Science misc Q Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
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topic_title	Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data water volume variation in Lake Qinghai multi-source altimeter satellite optical remote sensing satellite satellite applications Qinghai–Tibet Plateau
topic	misc water volume variation in Lake Qinghai misc multi-source altimeter satellite misc optical remote sensing satellite misc satellite applications misc Qinghai–Tibet Plateau misc Science misc Q
topic_unstemmed	misc water volume variation in Lake Qinghai misc multi-source altimeter satellite misc optical remote sensing satellite misc satellite applications misc Qinghai–Tibet Plateau misc Science misc Q
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title	Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
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title_full	Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
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author_browse	Jianbo Wang Jinyang Wang Shunde Chen Jianbo Luo Mingzhi Sun Jialong Sun Jiajia Yuan Jinyun Guo
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title_sort	study on the variations in water storage in lake qinghai based on multi-source satellite data
title_auth	Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
abstract	Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.
abstractGer	Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.
abstract_unstemmed	Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10<sup<9</sup< m<sup<3</sup< from July 2002 to December 2022, with a growth rate of 5.3766 × 10<sup<8</sup< m<sup<3</sup</a. The growth rate from January 2005 to January 2015 is 4.4850 × 10<sup<8</sup< m<sup<3</sup</a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10<sup<8</sup< m<sup<3</sup</a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.
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title_short	Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
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