ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS
The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-08-01
|
| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/401/2020/isprs-annals-V-3-2020-401-2020.pdf |
| _version_ | 1830477376327254016 |
|---|---|
| author | F. M. C. Pizani P. Maillard A. F. F. Ferreira C. C. de Amorim |
| author_facet | F. M. C. Pizani P. Maillard A. F. F. Ferreira C. C. de Amorim |
| author_sort | F. M. C. Pizani |
| collection | DOAJ |
| description | The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured <i>in loco</i> using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with <i>a priori</i> knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with <i>r</i><sup>2</sup> > 0.75. The models for the optically non-active parameters produced less striking results with <i>r</i><sup>2</sup> as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen). |
| first_indexed | 2024-12-21T16:13:11Z |
| format | Article |
| id | doaj.art-93847bc2da4f43e28f474c080324e553 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-21T16:13:11Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-93847bc2da4f43e28f474c080324e5532022-12-21T18:57:45ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502020-08-01V-3-202040140810.5194/isprs-annals-V-3-2020-401-2020ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORSF. M. C. Pizani0P. Maillard1A. F. F. Ferreira2C. C. de Amorim3Department of Geography, Universidade Federal de Minas Gerais, Belo Horizonte, BrasilDepartment of Geography, Universidade Federal de Minas Gerais, Belo Horizonte, BrasilDepartment of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, BrasilDepartment of Sanitary and Environmental Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, BrasilThe low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured <i>in loco</i> using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with <i>a priori</i> knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with <i>r</i><sup>2</sup> > 0.75. The models for the optically non-active parameters produced less striking results with <i>r</i><sup>2</sup> as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/401/2020/isprs-annals-V-3-2020-401-2020.pdf |
| spellingShingle | F. M. C. Pizani P. Maillard A. F. F. Ferreira C. C. de Amorim ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS |
| title_full | ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS |
| title_fullStr | ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS |
| title_full_unstemmed | ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS |
| title_short | ESTIMATION OF WATER QUALITY IN A RESERVOIR FROM SENTINEL-2 MSI AND LANDSAT-8 OLI SENSORS |
| title_sort | estimation of water quality in a reservoir from sentinel 2 msi and landsat 8 oli sensors |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/401/2020/isprs-annals-V-3-2020-401-2020.pdf |
| work_keys_str_mv | AT fmcpizani estimationofwaterqualityinareservoirfromsentinel2msiandlandsat8olisensors AT pmaillard estimationofwaterqualityinareservoirfromsentinel2msiandlandsat8olisensors AT affferreira estimationofwaterqualityinareservoirfromsentinel2msiandlandsat8olisensors AT ccdeamorim estimationofwaterqualityinareservoirfromsentinel2msiandlandsat8olisensors |