DOI: 10.5061/dryad.w6m905r1j
title: Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations

description: Photosynthesis-irradiance (PI) relationships are important for
 phytoplankton ecology and for quantifying carbon fixation rates in the
 environment. However, the parameters of PI relationships are typically
 unknown across space and time. Here we use machine learning, satellite
 remote-sensing, and a database of in-situ PI relationships to build models
 that predict the seasonal cycle of PI parameters as a function of
 satellite-observed variables. Using only surface light, temperature, and
 chlorophyll, we achieve an R2 of 58% for predicting photosynthesis rates
 at saturating light and an R2 of 78% for predicting the light
 saturation parameter. Predictability is maximized when averaging
 environmental covariates over 30-day and
 25-day timescales, respectively, indicating that environmental
 history and community turnover timescales are important for predicting
 in-situ PI relationships. These results will help improve the
 parameterization of satellite-based primary production models and quantify
 emergent environmental integration timescales in photosynthetic
 communities.
description: # Data from: Predicting photosynthesis-irradiance relationships from
 satellite remote-sensing observations Dataset DOI:
 [10.5061/dryad.w6m905r1j](10.5061/dryad.w6m905r1j) ## Description of the
 data and file structure Data are a subset of the database curated by
 Bouman et al. (2018; Photosynthesis-­irradiance parameters of
 phytoplankton: synthesis of a global data set. Earth System Science Data
 **10**: 251–266) and updated by Kulk et al. (2020; Primary production, an
 index of climate change in the ocean: Satellite-based estimates over two
 decades. Remote Sensing **12**: 1–26). All measurements analyzed here were
 taken using 14C uptake as a measure of photosynthetic rate using
 incubations between 1.5-4 hours in duration. The database reports
 parameters for fitted PI relationships. We chose subsets of PI
 relationships originating from established regional sampling programs
 wherein the same locations are sampled across time and over a range of
 environmental conditions using consistent sampling and experimental
 methodology. Analyzing these subsets acts to minimize bias when collating
 datapoints that differ in collection methodology. Samples were taken from
 the mixed layer based on an established monthly mixed layer depth
 climatology to best correspond with satellite observations. We matched PI
 relationships with satellite-derived photosynthetically available
 radiation (PAR), sea surface temperature (SST), and chlorophyll-a (Chl).
 We use 4km daily-averaged surface PAR from merged MODIS and SeaWIFS
 version R2018.0. SST at 6km resolution is from version 2.0 of the
 multi-sensor Operational Sea Surface Temperature and Ice Analysis (OSTIA)
 foundation temperature product. We use Chl from version 5.0 of the
 multi-sensor Ocean Colour Climate Change Initiative (OC-CCI) 4km product.
 We also used SST and Chl to compute an estimate of the picophytoplankton
 contribution to Chl, denoted  according to equations given in Brewin et
 al. (2010; A three-component model of phytoplankton size class for the
 Atlantic Ocean. Ecological Modelling **221**: 1472–1483). We constructed a
 series of temporal averages of the variables, with each average extending
 one day further back in time. We do this over 100 days for individual
 in-situ sampling locations, generating 100 covariate datasets to be used
 in the modeling of the PI parameters as a function of environmental
 integration timescale. ### Files and variables #### File: dat.csv
 **Description:**  ##### Variables * sst: sea surface temperature [degree
 Celsius] * chl: surface chlorophyll-a concentration [mg Chl m^-3^ ] * par:
 surface incident photosynthetically active radiation [Einstein m^-2^
 day^-1^]  * parml: mixed-layer depth averaged PAR [Einstein m^-2^ day^-1^]
 * nano_pico: concentration ratio of nano-phytoplankton to
 pico-phytoplankton [unitless] * micro_nano: concentration ratio of
 micro-phytoplankton to nano-phytoplankton [unitless] * pico: proportion of
 phytoplankton represented by pico-phytoplankton [proportion] * micro_pico:
 concentration ratio of micro-phytoplankton to
 pico-phytoplankton [unitless] * lat: latitude of the PI experiment
 [degrees] * lon: longitude of the PI experiment [degrees] * depth: depth
 of water sampled for PI experiment [m] * month: month in which the PI
 experiment was performed [number] * PBmax: measured PBmax from the PI
 experiment [mg C mg Chl^-1^ hour^-1^] * alpha: measured alpha from the PI
 experiment [mg C mg Chl-1 seconds-1 (micromol photons m^-2^
 seconds^-1^)^-1^] * Ek: ratio of measured PBmax and alpha [micromol
 photons m^-2^ seconds^-1^] * region: sub-region used in the analysis [ID]
 * date: date of the PI experiment [month/day/year] * mld: climatological
 mixed layer depth according to the latitude, longitude and month of the
 sampling location [m] ## Code/software All code used to perform analyses
 and produce figures will be publicly available at:
 [https://github.com/gregbritten/pi_parameters_public](https://github.com/gregbritten/pi_parameters_public) ## Access information All data used in this study are cited in the main text and publicly available with the following DOIs: OC-CCI chlorophyll (doi:10.5285/5011d22aae5a4671b0cbc7d05c56c4f0); OSTIA SST (doi:10.48670/moi-00168); MODIS and SeaWifs PAR (doi:10.5067/AQUA/MODIS/L3B/PAR/2022.0, doi:10.5067/ORBVIEW-2/SeaWiFS/L3B/PAR/2022.0).