ocx: OCX algorithm

In BIO-RSG/oceancolouR: Various Functions for Satellite and Data Analysis

OCX algorithm

Description

Given a set of Rrs and coefficients, calculate chlorophyll using a polynomial band ratio algorithm. See ?optimize_ocx_coefs for example.

Usage

ocx(rrs, blues, green, coefs, use_443nm = FALSE)

Arguments

rrs	Either: Numeric matrix where rows = records, columns = Rrs wavebands, with named columns ("Rrs_XXX", where XXX is a wavelength in nanometres), OR: RasterStack or RasterBrick of rrs layers with stack layers following the same naming convention. Names must match c(blues, green), i.e. same names, from shortest waveband to longest.
blues	Character vector of Rrs wavebands in the blue range (e.g. c("Rrs_443", Rrs_488")), matching column name(s) in rrs, maximum 3 options, arranged from shortest waveband to longest. Note that if use_443nm=FALSE, the 443nm waveband will be removed and another must be used in its place.
green	String, Rrs waveband in the green range (e.g. "Rrs_547"), matching a column name in rrs
coefs	Numeric vector of coefficients corresponding to terms in the polynomial (lowest degree to highest)
use_443nm	Logical value, TRUE to make the 443nm band an option in the band ratio

Value

For matrix rrs: Numeric value (or vector) – chlorophyll as computed by OCX for the given Rrs, sensor, and coefficients. For RasterStack/Brick rrs: equivalent raster with OCx chlorophyll-a.

References

Original paper:

O'Reilly, John & Maritorena, S. & Mitchell, B.G. & Siegel, David & Carder, Kendall & Garver, S.A. & Kahru, Mati & Mcclain, Charles. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research. 103. 937-953.

Reference for regional OCX algorithms tuned to Atlantic and Pacific Canadian coasts:

Clay, S.; Peña, A.; DeTracey, B.; Devred, E. Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans. Remote Sens. 2019, 11, 2609. https://www.mdpi.com/2072-4292/11/22/2609

Link to NASA chlorophyll-a description:

https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/

Examples

# Some in situ chl / modisaqua Rrs data used in Clay et al (2019)
input <- matrix(c(0.118, 0.0072, 0.0064, 0.0035, 0.122, 0.0048, 0.005, 0.0017, 0.128, 0.0076, 0.007, 0.0032, 0.198, 0.0072, 0.007, 0.0035, 0.199, 0.0137, 0.0099, 0.005, 0.206, 0.0049, 0.005, 0.0027, 0.208, 0.0083, 0.0074, 0.0035, 0.213, 0.0035, 0.0036, 0.0023, 0.215, 0.0053, 0.0057, 0.0032, 0.217, 0.0031, 0.0041, 0.0026, 0.22, 0.0067, 0.0066, 0.0034, 0.223, 0.0032, 0.0035, 0.0023, 0.223, 0.0042, 0.0045, 0.0024, 0.249, 0.0185, 0.0125, 0.0062, 0.249, 0.0027, 0.0056, 0.005, 0.254, 0.0048, 0.0055, 0.0035, 0.403, 0.0052, 0.0055, 0.0026, 0.404, 0.0054, 0.0054, 0.0043, 0.404, 0.0026, 0.003, 0.0023, 0.418, 0.004, 0.0042, 0.0028, 0.438, 0.0053, 0.0054, 0.0032, 0.438, 0.0047, 0.0048, 0.0034, 0.5, 0.0045, 0.0048, 0.0038, 0.501, 0.0047, 0.0074, 0.0069, 0.508, 0.0138, 0.0114, 0.0075, 0.511, 0.0047, 0.0053, 0.0037, 0.958, 0.0023, 0.0034, 0.003, 0.971, 0.0072, 0.0054, 0.0038, 1.253, 0.0019, 0.003, 0.0028, 1.253, 0.0108, 0.0058, 0.0034, 1.259, 0.0017, 0.0026, 0.0026, 1.261, 0.0057, 0.0073, 0.0074, 1.264, 0.0031, 0.0032, 0.0027, 1.269, 0.0033, 0.0044, 0.0044, 1.273, 0.0047, 0.0045, 0.0036, 1.311, 0.0043, 0.0046, 0.0031, 1.975, 0.0066, 0.0051, 0.0038, 1.975, 0.0067, 0.0065, 0.0043, 1.994, 0.0016, 0.0026, 0.0029, 1.999, 0.0022, 0.0037, 0.0033, 2.019, 0.0024, 0.0032, 0.0035, 2.551, 0.0059, 0.0043, 0.0024, 3.01, 0.0037, 0.0044, 0.0036, 3.035, 8e-04, 0.0026, 0.0031, 3.064, 0.0043, 0.0042, 0.0034, 3.086, 0.0077, 0.0081, 0.0072, 3.148, 0.0061, 0.0045, 0.0034, 3.216, 0.0027, 0.0034, 0.0035, 3.222, 0.0059, 0.0046, 0.0035, 4.47, 0.0033, 0.0042, 0.0033, 4.558, 0.0052, 0.0053, 0.0037, 4.575, 0.0051, 0.0042, 0.004, 4.613, 0.0031, 0.0034, 0.0034, 4.653, 0.0014, 0.0023, 0.0033, 4.749, 6e-04, 0.0019, 0.0034, 6.644, 0.0046, 0.0039, 0.0037, 6.825, 0.0015, 0.0023, 0.0026, 6.832, 0.0042, 0.0047, 0.0045, 6.954, 0.0053, 0.0045, 0.0034, 7.049, 0.0036, 0.0034, 0.0039, 7.099, 3e-04, 0.0013, 0.0026, 7.162, 0.0027, 0.0027, 0.003, 7.407, 0.0025, 0.003, 0.0035, 7.462, 0.0056, 0.0052, 0.0049, 7.79, 0.0012, 0.0019, 0.0028, 7.89, 0.0013, 0.0022, 0.0028, 8.142, 0.0044, 0.0044, 0.0047, 8.162, 5e-04, 0.0014, 0.0024, 8.869, 0.0011, 0.0022, 0.0029, 9.274, 0.0018, 0.0022, 0.0026, 9.533, 0.0015, 0.0022, 0.003), ncol=4, byrow=TRUE)
colnames(input) <- c("in_situ_chl", "Rrs_443", "Rrs_488", "Rrs_547")
rrs <- input[,2:4]
chl <- input[,1]

# get the default globally-tuned ocx coefficients for MODIS-Aqua
best_alg_coefs <- get_ocx_coefs("modisaqua", region="global", alg="ocx")
# get the wavebands used in the ocx algorithm for modisaqua, and include the 443nm band as an option
use_443nm <- TRUE
lambdas <- get_ocx_bands("modisaqua", use_443nm = use_443nm)
# calculate the band ratio
br <- get_br(rrs=rrs, blues=lambdas$blues, green=lambdas$green, use_443nm=use_443nm)$rrs_ocx
# calculate ocx chl for each data point using those coefficients
sat_ocx_chl <- ocx(rrs=rrs, blues=lambdas$blues, green=lambdas$green, coefs=best_alg_coefs)

# plot in situ against satellite chl
library(ggplot2)
ggplot(data.frame(in_situ_chl=chl, sat_ocx_chl=sat_ocx_chl, stringsAsFactors=FALSE),
       aes(x=in_situ_chl, y=sat_ocx_chl)) +
    geom_point() +
    geom_abline(slope=1, intercept=0) +
    scale_x_log10(limits=c(0.1, 15)) +
    scale_y_log10(limits=c(0.1, 15)) +
    theme_bw()

BIO-RSG/oceancolouR documentation built on Feb. 2, 2025, 2:01 a.m.