eof_chl: EOF chlorophyll-a model

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

EOF chlorophyll-a model

Description

Calculate chlorophyll-a for input Rrs (remote sensing reflectances) using the EOF (empirical orthogonal function) method tested in the Gulf of Saint Lawrence.

Usage

eof_chl(rrs, training_set)

Arguments

rrs	Either a dataframe containing numeric columns of Rrs data with the naming format "Rrs_XXX", where XXX is the wavelength in nanometres, or a RasterStack where each raster contains the Rrs data and follows the same naming format.
training_set	Dataframe with numeric columns of Rrs data with the same naming format as in rrs, and a column named "chla" containing the corresponding in situ chlorophyll-a.

Details

The training set should be created using in situ chlorophyll-a values matched to satellite Rrs. Use caution if you try to predict chlorophyll-a from Rrs where the region / water type / time period is very different from those used in the training set. The training set should contain at least 50 matchups (i.e. 50 chla values paired with satellite Rrs, see figure 4 from Laliberté et al, 2018).

Value

Numeric vector of chla values (for dataframe input) or raster of chla values (for RasterStack input)

References

Laliberté, Julien & Larouche, Pierre & Devred, Emmanuel & Craig, Susanne. (2018). Chlorophyll-a Concentration Retrieval in the Optically Complex Waters of the St. Lawrence Estuary and Gulf Using Principal Component Analysis. Remote Sensing. 10. 10.3390/rs10020265.

Function written by Julien Laliberté, July 2020. Source here:

https://catalogue.ogsl.ca/data/mpo/dcb7ed2e-bce9-40c5-a197-71ac6b46bd5c/algorithme_predict_chl-a.pdf

Modified 18 Dec 2020 by Stephanie Clay for use in the oceancolouR package.

Examples

# create training set dataframe and rrs dataframe to test the function
# source: https://catalogue.ogsl.ca/data/mpo/dcb7ed2e-bce9-40c5-a197-71ac6b46bd5c/algorithme_predict_chl-a.pdf
training <- data.frame(id = c(8141L, 8489L, 11385L, 5627L, 9885L, 5027L, 7158L, 5027L, 5028L, 10812L),
                       chla = c(0.26, 1.94, 4.33, 0.43, 1.8975, 0.635, 2.3025, 0.51, 0.6025, 0.245),
                       Rrs_412 = c(0.00186400086658978, 0.00182400086669077, 0.000450000870159784, 0.00164600086714017,
                                   0.00350800086243908, 0.00242000086518601, 0.00187000086657463,
                                   0.000630000869705327, 0.000138000870947508, 0.00433400086035363),
                       Rrs_443 = c(0.00233000086541324, 0.00199200086626661, 0.000990000868796415,0.00165800086710988,
                                   0.00382400086164125, 0.00274000086437809,0.00201400086621106,
                                   0.00144000086766027, 0.00072200086947305,0.00408600086097977),
                       Rrs_490 = c(0.00282600086416096, 0.00196600086633225,0.00161800086721087, 0.00161200086722602,
                                   0.00352400086239868,0.00311600086342878, 0.00220000086574146,
                                   0.00204000086614542,0.00124600086815008, 0.0038020008616968),
                       Rrs_510 = c(0.00250600086496888,0.00205400086611007, 0.00169800086700889, 0.00153000086743305,
                                   0.00315600086332779, 0.00316000086331769, 0.00225200086561017,
                                   0.00205600086610502, 0.00140200086775621, 0.00333600086287333),
                       Rrs_555 = c(0.00185600086660997, 0.00189000086652413, 0.00181600086671097,0.00126400086810463,
                                   0.00214000086589294, 0.00281800086418116,0.0023640008653274,
                                   0.00177200086682205, 0.0013380008679178,0.0027960008642367),
                       Rrs_670 = c(0.000248000870669784, 0.000220000870740478,0.000292000870558695, 6.40008711343398e-05,
                                   0.000324000870477903,0.000432000870205229, 0.000628000869710377,
                                   0.000168000870871765,0.000144000870932359, 0.000304000870528398),
                       stringsAsFactors = FALSE)
rrs <- data.frame(Rrs_412 = c(0.000932000868942851,0.00205000086612017, 0.000216000870750577, 0.00226600086557482,
                              0.00264000086463057, 0.000746000869412455, 0.00179000086677661,
                              0.0042260008606263, 0.00189200086651908, 0.000468000870114338),
                  Rrs_443 = c(0.00167600086706443, 0.00230000086548898, 0.000754000869392257,0.00247000086505977,
                              0.00274000086437809, 0.00201800086620096,0.00221400086570611,
                              0.00489600085893471, 0.00260200086472651,0.000840000869175128),
                  Rrs_490 = c(0.0023600008653375, 0.00269000086450433,0.00139000086778651, 0.00268000086452957,
                              0.00283600086413571,0.00316000086331769, 0.00282200086417106,
                              0.00516200085826313,0.0027920008642468, 0.00149200086752899),
                  Rrs_510 = c(0.00213200086591314,0.00270000086447908, 0.00157000086733206, 0.00254800086486284,
                              0.00272200086442353, 0.00337800086276729, 0.00267400086454472,
                              0.00416400086078283, 0.00249200086500423, 0.00160400086724621),
                  Rrs_555 = c(0.00159800086726136, 0.00292800086390343, 0.00159400086727146,0.00232400086542839,
                              0.00223000086566572, 0.00331000086293898,0.00207400086605958,
                              0.00264000086463057, 0.00162600086719067,0.0013260008679481),
                  Rrs_670 = c(0.000132000870962656, 0.000762000869372059,0.000186000870826319, 0.000774000869341762,
                              0.00022800087072028,0.000566000869866912, 0.000358000870392061,
                              0.000338000870442556,0.000360000870387012, 6.40008711343398e-05),
                  stringsAsFactors = FALSE)

# test with a dataframe of rrs
chl_vector <- eof_chl(rrs=rrs, training_set=training)
print(chl_vector)

# transform rrs dataframe into RasterStack, and test it
library(raster)
library(magrittr)
rrs_stack <- rrs[1:9,] %>% lapply(FUN=matrix, nrow=3) %>% lapply(raster) %>% stack()
names(rrs_stack) <- colnames(rrs)
chl_raster <- eof_chl(rrs=rrs_stack, training_set=training)
plot(chl_raster)

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