A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation
River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge....
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/8/1798 |
| _version_ | 1797434283567087616 |
|---|---|
| author | Hezhen Lou Yujia Zhang Shengtian Yang Xuelei Wang Zihao Pan Ya Luo |
| author_facet | Hezhen Lou Yujia Zhang Shengtian Yang Xuelei Wang Zihao Pan Ya Luo |
| author_sort | Hezhen Lou |
| collection | DOAJ |
| description | River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization. |
| first_indexed | 2024-03-09T10:30:00Z |
| format | Article |
| id | doaj.art-44416d1f46234f86b531ba5644269b27 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:30:00Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-44416d1f46234f86b531ba5644269b272023-12-01T21:21:52ZengMDPI AGRemote Sensing2072-42922022-04-01148179810.3390/rs14081798A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and ValidationHezhen Lou0Yujia Zhang1Shengtian Yang2Xuelei Wang3Zihao Pan4Ya Luo5College of Water Sciences, Beijing Normal University, Beijing 100875, ChinaCollege of Water Sciences, Beijing Normal University, Beijing 100875, ChinaCollege of Water Sciences, Beijing Normal University, Beijing 100875, ChinaMinistry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, ChinaCollege of Water Sciences, Beijing Normal University, Beijing 100875, ChinaSchool of Geographic and Environmental Science, Guizhou Normal University, Guiyang 550001, ChinaRiver discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.https://www.mdpi.com/2072-4292/14/8/1798long-term dischargemultisource remote sensingRSHSdownscalingwater continuity |
| spellingShingle | Hezhen Lou Yujia Zhang Shengtian Yang Xuelei Wang Zihao Pan Ya Luo A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation Remote Sensing long-term discharge multisource remote sensing RSHS downscaling water continuity |
| title | A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation |
| title_full | A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation |
| title_fullStr | A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation |
| title_full_unstemmed | A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation |
| title_short | A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation |
| title_sort | new method for long term river discharge estimation of small and medium scale rivers by using multisource remote sensing and rshs application and validation |
| topic | long-term discharge multisource remote sensing RSHS downscaling water continuity |
| url | https://www.mdpi.com/2072-4292/14/8/1798 |
| work_keys_str_mv | AT hezhenlou anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT yujiazhang anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT shengtianyang anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT xueleiwang anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT zihaopan anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT yaluo anewmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT hezhenlou newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT yujiazhang newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT shengtianyang newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT xueleiwang newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT zihaopan newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation AT yaluo newmethodforlongtermriverdischargeestimationofsmallandmediumscaleriversbyusingmultisourceremotesensingandrshsapplicationandvalidation |