Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters
The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/7/1238 |
| _version_ | 1797212025627082752 |
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| author | Raphael M. Kudela Stanford B. Hooker Liane S. Guild Henry F. Houskeeper Niky Taylor |
| author_facet | Raphael M. Kudela Stanford B. Hooker Liane S. Guild Henry F. Houskeeper Niky Taylor |
| author_sort | Raphael M. Kudela |
| collection | DOAJ |
| description | The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (<i>R</i><sub>rs</sub>) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and <i>R</i><sub>rs</sub> uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited. |
| first_indexed | 2024-04-24T10:35:49Z |
| format | Article |
| id | doaj.art-a203e286b13a4d16b0b453aa39bd8c01 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:35:49Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a203e286b13a4d16b0b453aa39bd8c012024-04-12T13:25:43ZengMDPI AGRemote Sensing2072-42922024-03-01167123810.3390/rs16071238Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland WatersRaphael M. Kudela0Stanford B. Hooker1Liane S. Guild2Henry F. Houskeeper3Niky Taylor4Ocean Sciences Department, University of California Santa Cruz, Santa Cruz, CA 95064, USANASA Goddard Space Flight Center Code 616.2 Bldg. 28 Rm. W120D, Greenbelt, MD 20771, USABiospheric Science Branch, Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94035, USAApplied Ocean Physics & Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USAU.S. Geological Survey Western Geographic Science Center, 350 N. Akron Rd., Moffett Field, CA 94035, USAThe launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (<i>R</i><sub>rs</sub>) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and <i>R</i><sub>rs</sub> uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.https://www.mdpi.com/2072-4292/16/7/1238signal-to-noise ratioocean colorcoastal and inland watersNDVIkelpchlorophyll |
| spellingShingle | Raphael M. Kudela Stanford B. Hooker Liane S. Guild Henry F. Houskeeper Niky Taylor Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters Remote Sensing signal-to-noise ratio ocean color coastal and inland waters NDVI kelp chlorophyll |
| title | Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters |
| title_full | Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters |
| title_fullStr | Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters |
| title_full_unstemmed | Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters |
| title_short | Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters |
| title_sort | expanded signal to noise ratio estimates for validating next generation satellite sensors in oceanic coastal and inland waters |
| topic | signal-to-noise ratio ocean color coastal and inland waters NDVI kelp chlorophyll |
| url | https://www.mdpi.com/2072-4292/16/7/1238 |
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