hypso.experimental.chlorophyll.chl_algorithms module

Created on Wed June 12, 2019

This module contains a functions to calculate the tropical Pacific chlorophyll algorithm (TPCA) concentrations for the satellite sensors; SeaWiFS, MODIS-Aqua and MERIS Developed for Pittman et al., 2019. JGR: Oceans (2019JC015498)

Notes:

Numpy has been used over pure python or the math library for more efficient implementations. For increased efficiency, dask (dask.array) can be used instead of numpy. These functions are designed for Level 3 Mapped products, which may not precisely reproduce the implementations available on the NASA website (https://oceandata.sci.gsfc.nasa.gov/).

General functions include:

blended_chl calculate_chl_ocx calculate_chl_ci

Sensor specific functions include:

calculate_seawifs_chl calcuate_modis_chl calculate_meris_chl

@author: Nicholas.Pittman @email: Nic.Pittman@utas.edu.au @position: PhD Candidate; Biogeochemistry, remote sensing, oceanography, Tropical Pacific @affiliation1: Institute of Marine and Antarctic Studies, University of Tasmania @affiliation2: Australian Research Council Centre of Excellence for Climate Extremes.

References:

Tropical Pacific Chlorophyll Algorithm (2019JC015498) ‘An assessment and improvement of satellite ocean color algorithms for the tropical Pacific Ocean’ Pittman, N., Strutton, P., Matear, R., Johnson, R., (2019) Submitted: Journal of Geophysical Research: Oceans

CI and blending window Hu, C., Lee, Z., and Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans 117.

Traditional OCx Algorithm O’Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A., Carder, K.L., Garver, S.A., Kahru, M., and McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research: Oceans 103, 24937–24953.

hypso.experimental.chlorophyll.chl_algorithms.blended_chl(chl_ci, chl_ocx, t1, t2)

A general Chl algorithm blending function between Chl_CI to Chl_OCx

Parameters:

  • chl_ci –

  • chl_ocx –

  • t1 –

  • t2 –

Returns:

Return type:

ndarray

hypso.experimental.chlorophyll.chl_algorithms.calculate_chl_ci(ci_poly, CI)

A general Chl_CI algorithm for a linear polynomial (Hu et al., 2012)

Parameters:

  • ci_poly (list) –

  • CI (ndarray) –

Returns:

Return type:

list

hypso.experimental.chlorophyll.chl_algorithms.calculate_chl_ocx(ocx_poly, lmbr)

A general Chl_OCx algorithm for fourth order polynomials (O’Reilly et al., 1998)

Parameters:

  • ocx_poly (list) –

  • lmbr (ndarray) –

Returns:

Return type:

Union[int, float]

hypso.experimental.chlorophyll.chl_algorithms.closest_index(input_list, lookup_value)

Find the closest index to lookup_value

Parameters:

  • input_list – List up to look on the value

  • lookup_value – Value to lookup

Returns:

Closest indext to the loookup_value

Return type:

ndarray

hypso.experimental.chlorophyll.chl_algorithms.modis_aqua_ocx(wl, hypercube, ocx_version)

Calculate the MODIS AQUA OCX for Chlorophyll estimation

Parameters:

  • wl –

  • hypercube –

  • ocx_version –

Returns:

hypso.experimental.chlorophyll.chl_algorithms.sentinel_ocx(wl, hypercube, ocx_version)

Calculate chlorophyll using the Sentinel OCX Polynomial.

Given:

—— MODIS-Aqua RRS values for 443,488,547,667

—— OCx: Polynomial

—— CI: Polynomial

—— l: Low blending cutoff

—— h: High blending cutoff

Calculate:

—— Calculate Chl OCx

—— Calculate Chl CI

—— Return blended chlorophyll esimate (Default product is the Pittman et al., 2019 TPCA)

Parameters:

  • wl –

  • hypercube –

  • ocx_version –

Returns: