Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water
Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/709 |
| _version_ | 1797396440471830528 |
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| author | JongCheol Pyo Yong Sung Kwon Jae-Hyun Ahn Sang-Soo Baek Yong-Hwan Kwon Kyung Hwa Cho |
| author_facet | JongCheol Pyo Yong Sung Kwon Jae-Hyun Ahn Sang-Soo Baek Yong-Hwan Kwon Kyung Hwa Cho |
| author_sort | JongCheol Pyo |
| collection | DOAJ |
| description | Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R<sup>2</sup> values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified. |
| first_indexed | 2024-03-09T00:51:19Z |
| format | Article |
| id | doaj.art-3c433c4f77be408a88b66cde2021607f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:51:19Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3c433c4f77be408a88b66cde2021607f2023-12-11T17:10:50ZengMDPI AGRemote Sensing2072-42922021-02-0113470910.3390/rs13040709Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake WaterJongCheol Pyo0Yong Sung Kwon1Jae-Hyun Ahn2Sang-Soo Baek3Yong-Hwan Kwon4Kyung Hwa Cho5Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, KoreaEnvironmental Impact Assessment Team, Division of Ecological Assessment, National Institute of Ecology, Seocheon 33657, KoreaKorea Ocean Satellite Center, Korea Institute of Ocean Science and Technology, Busan 49111, KoreaSchool of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, KoreaElectronics and Telecommunication Research Institute, 218 Gajeong-ro, Yeseong-gu, Daejeon 305-700, KoreaSchool of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, KoreaRemote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R<sup>2</sup> values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified.https://www.mdpi.com/2072-4292/13/4/709HydroLightgraphical user interfacesensitivity analysislake waterreflectance vertical profile |
| spellingShingle | JongCheol Pyo Yong Sung Kwon Jae-Hyun Ahn Sang-Soo Baek Yong-Hwan Kwon Kyung Hwa Cho Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water Remote Sensing HydroLight graphical user interface sensitivity analysis lake water reflectance vertical profile |
| title | Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water |
| title_full | Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water |
| title_fullStr | Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water |
| title_full_unstemmed | Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water |
| title_short | Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water |
| title_sort | sensitivity analysis and optimization of a radiative transfer numerical model for turbid lake water |
| topic | HydroLight graphical user interface sensitivity analysis lake water reflectance vertical profile |
| url | https://www.mdpi.com/2072-4292/13/4/709 |
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