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High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations

Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the...

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Autores principales: Tian, Haifeng, Wang, Jian, Pei, Jie, Qin, Yaochen, Zhang, Lijun, Wang, Yongjiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506707/
https://www.ncbi.nlm.nih.gov/pubmed/32872219
http://dx.doi.org/10.3390/s20174872
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author Tian, Haifeng
Wang, Jian
Pei, Jie
Qin, Yaochen
Zhang, Lijun
Wang, Yongjiu
author_facet Tian, Haifeng
Wang, Jian
Pei, Jie
Qin, Yaochen
Zhang, Lijun
Wang, Yongjiu
author_sort Tian, Haifeng
collection PubMed
description Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km(2) on 2 March 2017 to 1042.57 km(2) on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km(2) (i.e., increasing from 474.70 km(2) up to 708.12 km(2)). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.
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spelling pubmed-75067072020-09-26 High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations Tian, Haifeng Wang, Jian Pei, Jie Qin, Yaochen Zhang, Lijun Wang, Yongjiu Sensors (Basel) Article Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km(2) on 2 March 2017 to 1042.57 km(2) on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km(2) (i.e., increasing from 474.70 km(2) up to 708.12 km(2)). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales. MDPI 2020-08-28 /pmc/articles/PMC7506707/ /pubmed/32872219 http://dx.doi.org/10.3390/s20174872 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tian, Haifeng
Wang, Jian
Pei, Jie
Qin, Yaochen
Zhang, Lijun
Wang, Yongjiu
High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title_full High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title_fullStr High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title_full_unstemmed High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title_short High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations
title_sort high spatiotemporal resolution mapping of surface water in the southwest poyang lake and its responses to climate oscillations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506707/
https://www.ncbi.nlm.nih.gov/pubmed/32872219
http://dx.doi.org/10.3390/s20174872
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