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The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, . First, anin situABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of were estimated by fitting the ratio of the shortest and longest visible wave bands from thein situABI dataset to coincident,in situ data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination ( ) value (0.88) resulted in a root mean square difference of , or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability ( ) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, , values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.
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Spatial modeling of mid-infrared spectral data with thermal compensation using integrated nested Laplace approximation
The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging. Using accurate models for the spectral data is paramount for the success of any classification task. We demonstrate that the thermal compensation of sample heating and spatial variability analysis yield lower modeling errors than non-spatial modeling. Then, we obtain the inference of the spectral data probability density functions using the integrated nested Laplace approximation (INLA) on a Bayesian hierarchical model. To achieve this goal, we use the fast and user-friendly R-INLA package in for the computation. This approach allows affordable and real-time substance identification with fewer SNR sensor measurements, thereby potentially increasing throughput and lowering costs.
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- Award ID(s):
- 2014166
- PAR ID:
- 10305175
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Applied Optics
- Volume:
- 60
- Issue:
- 27
- ISSN:
- 1559-128X; APOPAI
- Format(s):
- Medium: X Size: Article No. 8609
- Size(s):
- Article No. 8609
- Sponsoring Org:
- National Science Foundation
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