Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART)
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1 Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) Yongzhen Fan, Nan Chen, Wei Li, and Knut Stamnes Light and Life Laboratory, Stevens Institute of Technology, Hoboken, NJ, USA
2 OC-SMART Introduction The Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a toolbox designed to retrieve aerosol and ocean color products from satellite remote sensing images. It utilizes multilayer neural networks (MLNNs) driven by extensive radiative transfer simulations using our coupled atmosphere-ocean RT model, AccuRT, to perform atmospheric correction (AC).
3 Frequency of negative Rrs from SeaDAS standard AC Percentage of negative remote sensing reflectance (Rrs) in 8 day averaged 4 km Aqua MODIS L3 data from
4 The Multilayer Neural Network (MLNN) based AC Algorithm The MLNN AC algorithm is a spectral matching algorithm based on the spectral similarity between Rayleigh corrected TOA radiances (L rc ) and the water-leaving radiances (L w ). Therefore it does not require the aerosol radiances to be retrieved. Keys to success of the algorithm include: The MLNN AC rely on extensive and accurate RT simulations from a coupled atmosphere-ocean RT model, AccuRT, that accurately computes multiple scattering and BRDF effects between the atmosphere and ocean. Multiple flexible water IOP models (modified GSM, CCRR, Morel 2002) and aerosol models (Ahmad 2010, OPAC 4.0b) are used in the RT simulations to create a comprehensive global dataset that is representative of most marine and aerosol conditions. Realistic input parameter distributions for the aerosol and water IOP models are obtained by analyzing level-3 global ocean color products from current ocean color sensors.
5 OC-SMART Flowchart
6 The Neural Network based Cloud Mask The cloud mask (CM) algorithm in OC-SMART is threshold-free and based on a neural network classifier driven by extensive radiative transfer simulations. Example below shows the cloud screening results from our CM applied to MODIS by using ONLY 4 bands (469, 555, 645, and 859 nm). Left: Aqua MODIS RGB image on 03/09/2014. Middle: Cloud mask results from neural network cloud mask algorithm using only 4 MODIS bands (469, 555, 645, and 859 nm). Right: Cloud mask results from threshold-based method used in SeaDAS. Blue: deep clean water, magenta: coastal/turbid water, orange: heavy aerosol loading over water, white/grey: clouds.
7 OC-SMART MODIS image retrieval
8 OC-SMART MODIS image retrieval
9 OC-SMART VIIRS image retrieval (heavy aerosol and extremely turbid water) OC-SMART is applicable to heavily polluted continental aerosols and extremely turbid water conditions. VIIRS RGB, 03/09/ :42 UTC
10 OC-SMART VIIRS image retrieval (heavy aerosol and extremely turbid water) OC-SMART significantly improves R rs retrievals in areas with heavily polluted aerosols and turbid water. The standard SeaDAS algorithm produces a large number of negative R rs (purple color) and large areas with no retrievals.
11 OC-SMART VIIRS image retrieval (dust storm) OC-SMART better (than SeaDAS) reveals algal bloom patterns in the water under dust storm conditions. VIIRS RGB, 03/10/ :48 UTC OCSMART CHLa SeaDAS CHLa
12 OC-SMART Validation on SeaWiFS ( )
13 OC-SMART Validation on VIIRS ( )
14 Summary OC-SMART, a new satellite remote sensing image analysis tool, based on coupled atmosphere-ocean radiative transfer simulations and multilayer neural network (MLNN) techniques has been developed and validated for several OC sensors based on global field measurements. OC-SMART is applicable in both open ocean and coastal/inland water areas. OC-SMART has superior cloud screening over water areas based on machine learning method. OC-SMART completely eliminates the negative remote sensing reflectance issue which plagues many other AC algorithms. OC-SMART improves retrievals of normalized remote sensing reflectances (nr rs ) compared to the SeaDAS NIR algorithm. OC-SMART improves data quality of derived products, such as CHLa, because of the improved nr rs retrievals. OC-SMART is applicable to heavy aerosol and extremely turbid water conditions and is resilient to sunglint and adjacency effects from bright targets. OC-SMART does not require data from SWIR bands, and is therefore applicable to all ocean color sensors. In fact, we have already developed algorithms for MODIS, VIIRS, SeaWiFS, Sentinel-3 OLCI, and GOCI. OC-SMART algorithms for Sentinel-2 MSI, Landsat-8 OLI, DSCOVR EPIC and JAXA GCOM-C SGLI sensor are under development. OC-SMART is very fast (7 times faster than SeaDAS) and suitable for operational use. We are developing plugins of OC- SMART for the ESA SNAP platform. Reference: Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder and K. Stamnes, Atmospheric correction over coastal waters using multilayer neural networks, Remote Sensing of Environment, Vol. 199, p , (2017). DOI: /j.rse N. Chen, W. Li, C. Gatebe, T. Tanikawa, M. Hori, R. Shimada, T. Aoki, & K. Stamnes, New cloud mask algorithm based on machine learning methods and radiative transfer simulations, Remote Sensing of Environment, Vol. 219, p62-71, (2018).
15 Thank you! Questions?
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