Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments
Satellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observation...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/22/5291 |
| _version_ | 1827638825855221760 |
|---|---|
| author | Sachidananda Mishra Richard P. Stumpf Andrew Meredith |
| author_facet | Sachidananda Mishra Richard P. Stumpf Andrew Meredith |
| author_sort | Sachidananda Mishra |
| collection | DOAJ |
| description | Satellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observational gap from 2012 until the operationalization of OLCI in 2016. Although MODIS-Terra has been used to bridge the gap from 2012 to 2015, differences in band architecture and the absence of the 709 nm band have complicated generating a consistent and continuous CyanoHAB monitoring product. Moreover, several Terra bands often saturate during extreme high-concentration CyanoHAB events. This study trained a fully connected deep network (CyanNet) to model MERIS-Cyanobacteria Index (CI)—a key satellite algorithm for detecting and quantifying cyanobacteria. The network was trained with Rayleigh-corrected surface reflectance at 12 Terra bands from 2002–2008, 2010–2012, and 2017–2021 and validated with data from 2009 and 2016 in Lake Okeechobee. Model performance was satisfactory, with a ~17% median difference in Lake Okeechobee annual bloom magnitude. The median difference was ~36% with 10-day Chlorophyll-a time series data, with differences often due to variations in data availability, clouds or glint. Without further regional training, the same network performed well in Lake Apopka, Lake George, and western Lake Erie. Validation success, especially in Lake Erie, shows the generalizability of CyanNet and transferability to other geographic regions. |
| first_indexed | 2024-03-09T16:29:15Z |
| format | Article |
| id | doaj.art-38c25d5c99ac40b38c8bb35abab90d70 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:29:15Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-38c25d5c99ac40b38c8bb35abab90d702023-11-24T15:04:14ZengMDPI AGRemote Sensing2072-42922023-11-011522529110.3390/rs15225291Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color InstrumentsSachidananda Mishra0Richard P. Stumpf1Andrew Meredith2Consolidated Safety Services Inc., Fairfax, VA 22030, USANational Centers for Coastal Ocean Science, National Oceanic and Atmospheric Administration, Silver Spring, MD 20910, USAConsolidated Safety Services Inc., Fairfax, VA 22030, USASatellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observational gap from 2012 until the operationalization of OLCI in 2016. Although MODIS-Terra has been used to bridge the gap from 2012 to 2015, differences in band architecture and the absence of the 709 nm band have complicated generating a consistent and continuous CyanoHAB monitoring product. Moreover, several Terra bands often saturate during extreme high-concentration CyanoHAB events. This study trained a fully connected deep network (CyanNet) to model MERIS-Cyanobacteria Index (CI)—a key satellite algorithm for detecting and quantifying cyanobacteria. The network was trained with Rayleigh-corrected surface reflectance at 12 Terra bands from 2002–2008, 2010–2012, and 2017–2021 and validated with data from 2009 and 2016 in Lake Okeechobee. Model performance was satisfactory, with a ~17% median difference in Lake Okeechobee annual bloom magnitude. The median difference was ~36% with 10-day Chlorophyll-a time series data, with differences often due to variations in data availability, clouds or glint. Without further regional training, the same network performed well in Lake Apopka, Lake George, and western Lake Erie. Validation success, especially in Lake Erie, shows the generalizability of CyanNet and transferability to other geographic regions.https://www.mdpi.com/2072-4292/15/22/5291algal bloomstime-seriesmulti-missiondeep neural networkscyanobacteria index |
| spellingShingle | Sachidananda Mishra Richard P. Stumpf Andrew Meredith Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments Remote Sensing algal blooms time-series multi-mission deep neural networks cyanobacteria index |
| title | Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments |
| title_full | Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments |
| title_fullStr | Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments |
| title_full_unstemmed | Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments |
| title_short | Constructing a Consistent and Continuous Cyanobacteria Bloom Monitoring Product from Multi-Mission Ocean Color Instruments |
| title_sort | constructing a consistent and continuous cyanobacteria bloom monitoring product from multi mission ocean color instruments |
| topic | algal blooms time-series multi-mission deep neural networks cyanobacteria index |
| url | https://www.mdpi.com/2072-4292/15/22/5291 |
| work_keys_str_mv | AT sachidanandamishra constructingaconsistentandcontinuouscyanobacteriabloommonitoringproductfrommultimissionoceancolorinstruments AT richardpstumpf constructingaconsistentandcontinuouscyanobacteriabloommonitoringproductfrommultimissionoceancolorinstruments AT andrewmeredith constructingaconsistentandcontinuouscyanobacteriabloommonitoringproductfrommultimissionoceancolorinstruments |