Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing
Reliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray–Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resoluti...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1684 |
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| author | Ming Li Klaus Joehnk Peter Toscas Luis Riera Garcia Huidong Jin Tapas K. Biswas |
| author_facet | Ming Li Klaus Joehnk Peter Toscas Luis Riera Garcia Huidong Jin Tapas K. Biswas |
| author_sort | Ming Li |
| collection | DOAJ |
| description | Reliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray–Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resolution data in space and time is a major constraint for delivering early warnings. To address data scarcity, we developed a forecasting model integrating remote sensing data and time-series modelling. Using in situ Chl-a measurements from Murray–Darling Basin water bodies, we locally recalibrated a two-band ratio algorithm, namely the Normalized Difference Chlorophyll Index (NDCI), from Sentinel-2 data to derive Chl-a levels. The recalibrated model significantly improved the accuracy of high Chl-a estimates in our dataset after mitigating data heteroscedasticity. Building on these improved satellite-derived Chl-a estimates, we developed a time-series model for forecasting weekly Chl-a levels including quantification of forecast uncertainty through prediction intervals. The developed model, validated at eight sites for 2021–2022 data, performed well at shorter lead times, showing R<sup>2</sup> = 0.41 and RMSE = 8.1 μg/L for overall performance at a one-week lead time. The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. This study provides a valuable tool for the water managers/decision-makers to issue early warnings of algal blooms in the Murray–Darling Basin. |
| format | Article |
| id | doaj-art-0251136cb8a144af8f8612e084762665 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-0251136cb8a144af8f8612e0847626652025-08-20T01:56:42ZengMDPI AGRemote Sensing2072-42922025-05-011710168410.3390/rs17101684Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote SensingMing Li0Klaus Joehnk1Peter Toscas2Luis Riera Garcia3Huidong Jin4Tapas K. Biswas5CSIRO Data61, P.O. Box 1130, Bentley, WA 6102, AustraliaCSIRO Environment, GPO BOX 1700, Canberra, ACT 2601, AustraliaCSIRO Data61, Private Bag 10, Clayton South, VIC 3169, AustraliaCSIRO Data61, P.O. Box 1130, Bentley, WA 6102, AustraliaCSIRO Data61, GPO BOX 1700, Canberra, ACT 2601, AustraliaCSIRO Data61, GPO BOX 1700, Canberra, ACT 2601, AustraliaReliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray–Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resolution data in space and time is a major constraint for delivering early warnings. To address data scarcity, we developed a forecasting model integrating remote sensing data and time-series modelling. Using in situ Chl-a measurements from Murray–Darling Basin water bodies, we locally recalibrated a two-band ratio algorithm, namely the Normalized Difference Chlorophyll Index (NDCI), from Sentinel-2 data to derive Chl-a levels. The recalibrated model significantly improved the accuracy of high Chl-a estimates in our dataset after mitigating data heteroscedasticity. Building on these improved satellite-derived Chl-a estimates, we developed a time-series model for forecasting weekly Chl-a levels including quantification of forecast uncertainty through prediction intervals. The developed model, validated at eight sites for 2021–2022 data, performed well at shorter lead times, showing R<sup>2</sup> = 0.41 and RMSE = 8.1 μg/L for overall performance at a one-week lead time. The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. This study provides a valuable tool for the water managers/decision-makers to issue early warnings of algal blooms in the Murray–Darling Basin.https://www.mdpi.com/2072-4292/17/10/1684chlorophyll-aalgal bloomsremote sensingearly warninguncertainty quantificationMurray–Darling basin |
| spellingShingle | Ming Li Klaus Joehnk Peter Toscas Luis Riera Garcia Huidong Jin Tapas K. Biswas Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing Remote Sensing chlorophyll-a algal blooms remote sensing early warning uncertainty quantification Murray–Darling basin |
| title | Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing |
| title_full | Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing |
| title_fullStr | Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing |
| title_full_unstemmed | Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing |
| title_short | Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing |
| title_sort | forecasting chlorophyll a in the murray darling basin using remote sensing |
| topic | chlorophyll-a algal blooms remote sensing early warning uncertainty quantification Murray–Darling basin |
| url | https://www.mdpi.com/2072-4292/17/10/1684 |
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