LSDS-1433 Version 2.0

Department of the Interior U.S. Geological Survey

Landsat Provisional Aquatic Reflectance Algorithm Description Document (ADD)

Version 2.0

February 2020

Landsat Provisional Aquatic Reflectance Algorithm Description Document (ADD)

February 2020

Document Owner:

______Vaughn Ihlen Date LSRD Project Manager U.S. Geological Survey

Approved By:

______Karen Zanter Date LSDS CCB Chair U.S. Geological Survey

EROS Sioux Falls, South Dakota

- ii - LSDS-1433 Version 2.0 Executive Summary

This algorithm description document outlines the Landsat Provisional Aquatic Reflectance algorithm as derived directly from the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS) package distributed by the National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group (OBPG). The SeaDAS Remote Sensing Reflectance is converted to Aquatic Reflectance in this application. For more information about the atmospheric correction algorithm in SeaDAS, please see Mobley et al., 2016 in the References section.

The Provisional Aquatic Reflectance is derived from the Landsat Level 1 (L1) reflective bands over aquatic environments. The Top of Atmosphere (TOA) reflectance is input to an atmospheric correction algorithm to retrieve the water-leaving radiance at visible wavelengths. The water-leaving radiances are then normalized by downwelling solar irradiance to remove the remaining effects of solar orientation and atmospheric attenuation to compute the spectral Remote Sensing Reflectance (Rrs). To minimize the effect of solar angle, the Rrs values are multiplied by π to produce dimensionless Aquatic Reflectance (AR) values. Once obtained, the Aquatic Reflectance can be input to other aquatic science algorithms for retrieval of various quantities of scientific interest (e.g., total suspended solids).

This document is under Landsat Satellites Data System (LSDS) Configuration Control Board (CCB) control. Please submit changes to this document, as well as supportive material justifying the proposed changes, via Change Request (CR) to the Process and Change Management Tool.

- iii - LSDS-1433 Version 2.0 Document History

Document Document Publication Change Number Version Date Number LSDS-1433 Version 1.0 January 2020 CR 15087 LSDS-1433 Version 2.0 February 2020 CR 15306

- iv - LSDS-1433 Version 2.0 Contents

Executive Summary ...... iii Document History ...... iv Contents ...... v List of Tables ...... v Section 1 Introduction ...... 1 1.1 Background ...... 1 1.2 Purpose and Scope ...... 1 1.3 Document Organization ...... 1 Section 2 Application Inputs and Outputs ...... 2 2.1 Inputs ...... 2 2.1.1 Auxiliary Data ...... 2 2.2 Outputs ...... 2 Section 3 Procedure ...... 3 3.1 Algorithm Description ...... 3 3.1.1 Implementation ...... 4 3.2 Source Code Availability ...... 7 Appendix A Acronyms ...... 8 References ...... 9

List of Tables

Table 2-1. Application Inputs ...... 2 Table 2-2. Application Outputs ...... 2 Table 3-1. Implementation Inputs ...... 6 Table 3-2. Sensor-Specific Information ...... 6

- v - LSDS-1433 Version 2.0 Section 1 Introduction

1.1 Background Ocean color remote sensing algorithms typically utilize Remote Sensing Reflectance or Aquatic Reflectance as the basis of their higher-level processing. For instance, Aquatic Reflectance can be input to other aquatic science algorithms for retrieval of various quantities of scientific interest, such as chlorophyll-a concentration, diffuse attenuation, or inherent optical properties.

This document describes the algorithm used to generate the Landsat Provisional Aquatic Reflectance Science Product. The NASA Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS) algorithm is used to calculate the Remote Sensing Reflectance. The Remote Sensing Reflectance is then multiplied by π to obtain a dimensionless reflectance referred to as Aquatic Reflectance.

For additional information about the characteristics of the Landsat Provisional Aquatic Reflectance product please see the Landsat Provisional Aquatic Reflectance Product Guide.

1.2 Purpose and Scope The primary purpose of this document is to describe the algorithm for generation of Remote Sensing Reflectance as derived directly from the SeaDAS and as implemented in the application used by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center.

1.3 Document Organization This document contains the following sections:

• Section 1 provides an introduction • Section 2 provides a list of program inputs and outputs • Section 3 describes the algorithm • Appendix A provides a list of acronyms • The References section provides a list of reference documents

- 1 - LSDS-1433 Version 2.0 Section 2 Application Inputs and Outputs

2.1 Inputs Table 2-1 provides the list of inputs to the Landsat Provisional Aquatic Reflectance algorithm.

Description Size Source Type

Level 1 Band QA Npixels Level 1 data Integer

Level 1 TOA Reflectance Nbands x Npixels Level 1 data Scaled Integer

Per-pixel Solar Zenith Angles Npixels Metadata Scaled Integer

Per-Pixel Solar Azimuth Angles Npixels Metadata Scaled Integer

Per-pixel View Zenith Angles Npixels Metadata Scaled Integer

Per-Pixel View Azimuth Angles Npixels Metadata Scaled Integer NCEP MET file Auxiliary data Floating point TOMS/OMI ozone file Auxiliary data Integer NSIDC Sea Ice Concentration file Auxiliary data Integer

Table 2-1. Application Inputs

2.1.1 Auxiliary Data The National Centers for Environmental Prediction (NCEP) Meteorological (MET) data, Total Ozone Mapping Spectrometer (TOMS)/ Ozone Monitoring Instrument (OMI) ozone, and National Snow and Ice Data Center (NSIDC) sea ice extent auxiliary files are downloaded from the Ocean Color website (https://oceandata.sci.gsfc.nasa.gov/). NCEP MET inputs should be the nearest 6-hour meteorological auxiliary data file for the current year and day. This file contains the zonal wind, meridional wind, atmospheric pressure at mean sea level, relative humidity, and water vapor. The ozone inputs should be the 24-hour TOMS/OMI data file for the current year and day. Similarly, the NSIDC sea ice concentration input should be the 24-hour sea ice file for the current year and day. The NCEP MET and NSIDC sea ice data products tend to be populated on the Ocean Color website on the same day as acquisition; however, the ozone products usually have a latency of two days.

2.2 Outputs Description Size Type Nbands x Level 2 Aquatic Reflectance Scaled Integer Npixels

Table 2-2. Application Outputs

- 2 - LSDS-1433 Version 2.0 Section 3 Procedure

3.1 Algorithm Description This algorithm derives the spectral radiance upwelling from beneath the ocean surface, normalized by the downwelling solar irradiance and expressed as spectral Remote Sensing Reflectance, Rrs(λ) at each sensor wavelength, λ, in the visible domain with -1 units of sr . The Rrs(λ) is then normalized by the Bidirectional Reflectance Distribution Function (BRDF) of a perfectly reflecting Lambertian surface (multiplied by π) to produce the dimensionless aquatic reflectance ρw(λ). The Aquatic Reflectance algorithm below is provided by NASA’s Ocean Biology Processing Group (OBPG) via the Rrs Algorithm Theoretical Basis Document (ATBD), currently available from https://oceancolor.gsfc.nasa.gov/atbd/rrs/.

The fundamental quantity to be derived from ocean color sensors is the spectral distribution of reflected visible solar radiation upwelling from below the ocean surface and passing though the sea-air interface. Spaceborne ocean color sensors, however, measure the spectral radiance exiting the Top of Atmosphere (TOA). The majority of that observed TOA radiance is light reflected by air molecules and aerosols within the atmosphere, and those contributions must be accurately modeled and removed from the observed signal. Similarly, surface contributions from whitecaps and sun glint, the specular reflection of the sun into the sensor field of view, must be estimated and removed. Finally, the attenuating effects of absorbing atmospheric gases and scattering losses due to transmittance of the water-leaving radiance through the atmosphere must be corrected. The process of retrieving water-leaving radiance from TOA radiance is typically referred to as atmospheric correction.

The retrieved water-leaving radiances, Lw(λ), at each sensor wavelength, λ, are then normalized to remove remaining effects of solar orientation and atmospheric attenuation of the downwelling radiation to produce normalized water-leaving radiance, nLw(λ), which is often expressed as a radiance reflectance, Rrs(λ) or Remote Sensing Reflectance, by simply dividing by the mean extraterrestrial solar irradiance, F0(λ). In this algorithm, the TOA radiance is assumed to be partitioned linearly into various distinct physical contributions as shown below:

where: Lr(λ) = the radiance contribution due to Rayleigh scattering by air molecules La(λ) = the contribution due to scattering by aerosols, including multiple scattering interactions with the air molecules Lf(λ) = the contribution from surface whitecaps and foam Lw(λ) = the water-leaving component tdv(λ) = the transmittance of diffuse radiation through the atmosphere in the viewing path from surface to sensor

- 3 - LSDS-1433 Version 2.0 tgv(λ) = the transmittance loss due to absorbing gases for all upwelling radiation traveling along the sensor view path tgs(λ) = the transmittance to the downwelling solar radiation due to the presence of absorbing gases along the path from Sun to surface fp(λ) = is an adjustment for effects of polarization

The atmospheric correction algorithm retrieves Lw(λ) by estimating and subtracting the terms on the right-hand side of the above equation from Lt(λ). The Rrs(λ) is then computed as:

where: F0 = extraterrestrial solar irradiance (Thuillier et al., 2003) fs = adjustment of F0 for variation in Earth-Sun distance fb = bidirectional reflectance correction fλ = correction for out-of-band response

Most of the terms in the above equations are estimated through precomputed radiative transfer simulations or models that depend only on the sensor spectral response, solar and sensor viewing geometry, and ancillary information such as atmospheric gas concentrations, surface windspeeds, and surface pressure. The primary challenge in atmospheric correction is the estimation of the aerosol contribution, as aerosols are highly variable and must be inferred from the sensor observations. The aerosol estimation follows the work of (Gordon and Wang, 1994), with updated aerosol models and model selection approach detailed in (Ahmad et al., 2010). The Landsat aerosol estimation algorithm relies on sensor observations from two bands in the near-infrared and shortwave infrared regions (e.g., 865nm and 1609nm for Landsat 8 OLI; Pahlevan et al., 2017), where the water-leaving radiance contributions are generally small and can be accurately estimated through an iterative bio-optical modeling approach as described in (Bailey et al., 2010).

For a full description of the atmospheric correction algorithm, including details on the estimation of each term in the above equations, the reader is referred to the document titled Atmospheric Correction for Satellite Ocean Color Radiometry and the associated Web Book on Atmospheric Correction. The web book was developed as an online resource for the theoretical basis and implementation of the current standard atmospheric correction algorithm employed by NASA for all ocean color missions, and it will be maintained as the algorithm evolves.

3.1.1 Implementation The SeaDAS l2gen (level 2 generation) application is responsible for generating the remoter-sensing reflectance, among other products. This application supports a variety of sensors, including SeaWiFS, Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Spaceborne Thermal Emission and Reflection (ASTER). The USGS EROS version of this l2gen application supports only Landsat 8. However, other Landsat sensors will be supported in the future. There are a

- 4 - LSDS-1433 Version 2.0 variety of options and static inputs (some are sensor-specific, and others are user- specific), which are used by the application and listed with their values in Table 3-1. In addition, the USGS EROS version converts the Remote Sensing Reflectance to Aquatic Reflectance, which is then delivered to the user for only the visible bands.

Input Variable Value Description proc_land On turn off land processing multi-scattering with 2-band, RH-based model aer_opt -2 (AERRHNIR) selection and iterative NIR correction atmocor On turn on atmospheric correction shortest sensor wavelength for aerosol model aer_wave_short 865 selection longest sensor wavelength for aerosol model aer_wave_long 1609 selection maximum number of iterations for aerosol aer_iter_max 10 estimation glint_opt 1 standard glint correction out-of-band correction for water-leaving outband_opt 2 radiances filter_opt On (uses a 5x5 average) filter input data option 7 (Morel f/Q + Fresnel solar + brdf_opt bidirectional reflectance correction Fresnel sensor) gas_opt 15 (ozone, CO2, NO2, & H2O) gaseous transmittance bitmask selector pol_opt 0 (no correction) sensor-specific polarization correction $OCDATAROOT/common/anc anc_cor_file ancillary correction file _cor_file_28jan2014.nc $OCDATAROOT/common/lan land static land mask file (currently set to NULL) dmask_null.dat $OCDATAROOT/common/wat static shallow water mask file (currently set to water ermask.dat NULL) $OCDATAROOT/common/ET demfile static digital elevation file OPO1_ocssw.nc sea ice threshold, above which will be flagged ice_threshold 0.1 as sea ice $OCDATAROOT/common/sst sstfile static sea surface temperature reference file _climatology.hdf $OCDATAROOT/common/no2 no2file static NO2 auxiliary file _climatology_v2013.hdf offset 0.1 calibration offset adjustment gain 0.1 calibration gain multiplier [1.1,0.9,0.75,1.85,1.0,1.65,0.6, coccolith coccolithophore algorithm coefficients 1.15] cloud_thresh 0.01 cloud reflectance threshold cloud_wave 2201.0 wavelength of cloud reflectance test cloud_eps -1.0 (disabled) cloud reflectance ratio threshold glint_thresh 0.005 high sun glint threshold minimum NIR aerosol reflectance to attempt rhoamin 0.0005 model lookup sunzen 70.0 solar zenith angle threshold (degrees) satzen 60.0 satellite zenith angle threshold (degrees)

- 5 - LSDS-1433 Version 2.0 Input Variable Value Description minimum epsilon to trigger atmospheric epsmin 0.0 correction failure flag maximum epsilon to trigger atmospheric epsmax 3.0 correction failure flag minimum nLw(green band) to trigger the low nLwmin 0.15 Lw flag windspeed limit on white-cap correction wsmax 12.0 (meters/sec) maskland On land mask option maskbath Off shallow water mask option maskcloud On cloud mask option maskglint Off glint mask option masksunzen Off large solar zenith angle mask option masksatzen Off large satellite zenith angle mask option

Table 3-1. Implementation Inputs

3.1.1.1 Masking Land, Cloud, and Cloud Shadow The current input land and water masks do not mask any land or water pixels, and therefore the land pixels are identified instead by the internal “cloud” mask. The input cloud_thresh value specifies the cloud threshold value. After applying atmospheric and surface reflectance correction (for water), the general masks and flags are set. In this process the albedo is computed. If that albedo is greater than the cloud threshold, then the pixel is masked as “cloud”. In this case, the SEADAS_CLOUD bit will be turned on in the l2_flags QA band and the output Rrs value will be fill.

Given that Landsat products have a reliable QA band for clouds, the USGS EROS version of l2gen uses the Level 1 Quality Assessment Band (BQA) to mask clouds and shadows in addition to the normal masks being used by the SeaDAS l2gen application.

3.1.1.2 Remote Sensing Reflectance Calculation Table 3-2 lists the sensor-specific center wavelengths, λ, at which Rrs(λ) is generated for the standard ocean color product. Also shown are the default band pair used in standard processing to derive the aerosol contribution. Alternative Bands, when available, can also be used to form alternate band pairs for aerosol determination in special circumstances (e.g., very turbid waters), but they are not currently used in the generation of standard products.

Sensor Rrs Wavelengths (nm) Aerosol Bands (nm) Alternative Bands (nm) OLI (Landsat 8) 443, 482, 561, 655 865, 1609 2201

Table 3-2. Sensor-Specific Information

- 6 - LSDS-1433 Version 2.0 3.1.1.3 Aquatic Reflectance Calculation The Rrs(λ) bands are normalized by the Bidirectional Reflectance Distribution Function (BRDF) of a perfectly reflecting Lambertian surface (multiplied by π) to produce the dimensionless Aquatic Reflectance.

3.2 Source Code Availability The source code for the USGS EROS Landsat water-leaving reflectance correction is not publicly available at the current time. The OBPG provides the source code at https://oceancolor.gsfc.nasa.gov/docs/ocssw/dir_5516ca9d074c0c1cd79f549c776fccc5. html

- 7 - LSDS-1433 Version 2.0 Appendix A Acronyms

AR Aquatic Reflectance ASTER Advanced Spaceborne Thermal Emission and Reflection ATBD Algorithm Theoretical Basis Document BQA Quality Assessment Band BRDF Bidirectional Reflectance Distribution Function CCB Configuration Control Board CO2 Carbon Dioxide CR Change Request EROS Earth Resources Observation and Science H2O Water L1 Level 1 L2 Level 2 LSDS Landsat Satellites Data System MET NCEP Meteorological Data MODIS Moderate Resolution Imaging Spectroradiometer MTL Metadata text file extension NASA National Aeronautics and Space Administration NCEP National Centers for Environmental Prediction NIR Near Infrared NO2 Nitrogen Dioxide NSIDC National Snow and Ice Data Center OBPG Ocean Biology Processing Group OLI Operational Land Imager OMI Ozone Monitoring Instrument QA Quality Assessment Rrs Remote-Sensing Reflectance SeaDAS SeaWiFS Data Analysis System SeaWiFS Sea-viewing Wide Field-of-View Sensor TOA Top of Atmosphere TOMS Total Ozone Mapping Spectrometer USGS U.S. Geological Survey

- 8 - LSDS-1433 Version 2.0 References

Please see https://www.usgs.gov/land-resources/nli/landsat/landsat-acronyms for a list of acronyms.

Ahmad, Z., Franz, B.A., McClain, C.R., Kwiatkowska, E.J., Werdell, P.J., Shettle, E.P., & Holben, B.N. (2010). New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and Open Oceans. Applied Optics, 49(29). doi:10.1364/ao.49.005545

Bailey, S.W., & Werdell, P.J. (2006). A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment 102, 12-23. doi:10.1016/j.rse.2006.01.015

Franz, B.A., Bailey, S.W., Kuring, N., & Werdell, P.J. (2015). Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS. Journal of Applied Remote Sensing, 9(1), 096070. https://doi.org/10.1117/1.JRS.9.096070

Gordon, H.R., & Wang, M. (1994). Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied optics, 33(3), 443-452. https://doi.org/10.1364/AO.33.000443

Mobley, C.D., Werdell, J., Franz, B., Ahmad, Z., & Bailey, S. (2016). Atmospheric correction for satellite ocean color radiometry. NASA Tech. Memo, NASA/TM-2016- 217551, p. 85.

Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S. & Strait, C.M. (2017). Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements. Remote sensing of environment, 190, 289-301. https://doi.org/10.1016/j.rse.2016.12.030

Pahlevan, N., & Schott, J.R. (2013). Leveraging EO-1 to evaluate capability of new generation of Landsat sensors for coastal/inland water studies. IEEE Journal of selected topics in applied earth observations and remote sensing, 6(2), 360-374. https://doi.org/10.1109/JSTARS.2012.2235174

Thuillier, G., Hersé, M., Foujols, T., Peetermans, W., Gillotay, D., Simon, P.C., & Mandel, H. (2003). The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions. Solar Physics, 214(1), 1-22.

USGS/EROS. LSDS-1422. Landsat Provisional Aquatic Reflectance Product Guide https://www.usgs.gov/media/files/landsat-provisional-aquatic-reflectance-product-guide

- 9 - LSDS-1433 Version 2.0