Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs
This study investigates the optimal spatial window size for estimating chlorophyll-a (Chl-a) concentrations using Sentinel-2 imagery in small reservoirs of Extremadura, Spain. While remote-sensing techniques have proven valuable for water quality monitoring, the influence of pixel window size on est...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10710322/ |
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| author | Jose Caceres-Merino Cuartero Aurora Jesus A. Torrecilla-Pinero |
| author_facet | Jose Caceres-Merino Cuartero Aurora Jesus A. Torrecilla-Pinero |
| author_sort | Jose Caceres-Merino |
| collection | DOAJ |
| description | This study investigates the optimal spatial window size for estimating chlorophyll-a (Chl-a) concentrations using Sentinel-2 imagery in small reservoirs of Extremadura, Spain. While remote-sensing techniques have proven valuable for water quality monitoring, the influence of pixel window size on estimation accuracy remains understudied, particularly for smaller water bodies. We analyzed 94 atmospherically corrected Sentinel-2 images using the C2RCC processor, corresponding to 32 reservoirs, and compared the results with in situ measurements collected between 2017 and 2022. Our methodology explored window sizes ranging from 1×1 pixels to 20×20 pixels, employing various statistical estimators. Performance was assessed using root-mean-square relative error, mean absolute percentage error, and Spearman's correlation coefficient (ρ). Results show that window sizes between 5×5 and 9×9 pixels yielded optimal Chl-a estimation accuracy. The Cmax estimator consistently outperformed other methods across different window sizes, particularly for mesotrophic and eutrophic waters. Notably, larger window sizes improved correlation with in situ data but showed diminishing returns beyond 9×9 pixels. This study contributes to refining remote-sensing methodologies for inland water quality monitoring, particularly for small- to medium-sized reservoirs. Our findings suggest that careful consideration of spatial window size and statistical estimators can enhance the accuracy of Chl-a concentration predictions, potentially improving water resource management in regions with diverse aquatic ecosystems. |
| format | Article |
| id | doaj-art-e5c1dd99a55641249f66f4aa2eed57f5 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e5c1dd99a55641249f66f4aa2eed57f52025-08-20T02:55:56ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117187691878310.1109/JSTARS.2024.347697010710322Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small ReservoirsJose Caceres-Merino0https://orcid.org/0000-0001-5603-6545Cuartero Aurora1https://orcid.org/0000-0002-0219-9696Jesus A. Torrecilla-Pinero2https://orcid.org/0000-0001-9406-1100School of Technology, University of Extremadura, Caceres, SpainDepartment of Graphic Expression, School of Technology, University of Extremadura, Caceres, SpainDepartment of Construction, School of Technology, University of Extremadura, Caceres, SpainThis study investigates the optimal spatial window size for estimating chlorophyll-a (Chl-a) concentrations using Sentinel-2 imagery in small reservoirs of Extremadura, Spain. While remote-sensing techniques have proven valuable for water quality monitoring, the influence of pixel window size on estimation accuracy remains understudied, particularly for smaller water bodies. We analyzed 94 atmospherically corrected Sentinel-2 images using the C2RCC processor, corresponding to 32 reservoirs, and compared the results with in situ measurements collected between 2017 and 2022. Our methodology explored window sizes ranging from 1×1 pixels to 20×20 pixels, employing various statistical estimators. Performance was assessed using root-mean-square relative error, mean absolute percentage error, and Spearman's correlation coefficient (ρ). Results show that window sizes between 5×5 and 9×9 pixels yielded optimal Chl-a estimation accuracy. The Cmax estimator consistently outperformed other methods across different window sizes, particularly for mesotrophic and eutrophic waters. Notably, larger window sizes improved correlation with in situ data but showed diminishing returns beyond 9×9 pixels. This study contributes to refining remote-sensing methodologies for inland water quality monitoring, particularly for small- to medium-sized reservoirs. Our findings suggest that careful consideration of spatial window size and statistical estimators can enhance the accuracy of Chl-a concentration predictions, potentially improving water resource management in regions with diverse aquatic ecosystems.https://ieeexplore.ieee.org/document/10710322/C2RCCchlorophyll-a (Chl-a)data retrievaloptimal window sizeSentinel 2 |
| spellingShingle | Jose Caceres-Merino Cuartero Aurora Jesus A. Torrecilla-Pinero Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing C2RCC chlorophyll-a (Chl-a) data retrieval optimal window size Sentinel 2 |
| title | Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs |
| title_full | Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs |
| title_fullStr | Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs |
| title_full_unstemmed | Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs |
| title_short | Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs |
| title_sort | finding optimal spatial window the influence of size on remote sensing based chl a prediction in small reservoirs |
| topic | C2RCC chlorophyll-a (Chl-a) data retrieval optimal window size Sentinel 2 |
| url | https://ieeexplore.ieee.org/document/10710322/ |
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