Estimating photosynthetically available radiation at the ocean surface from ADEOS-II global imager data

A simple, yet efficient and fairly accurate algorithm is presented to estimate photosynthetically available radiation (PAR) at the ocean surface from Global Imager (GLI) data. The algorithm utilizes plane-parallel radiation-transfer theory and separates the effects of the clear atmosphere and clouds, i.e., the planetary atmosphere is modeled as a clear atmosphere positioned above a cloud layer. PAR is computed as the difference between the incident 400–700 nm solar flux at the top of the atmosphere (known) and the solar flux reflected back to space by the atmosphere and surface (derived from GLI radiance), taking atmospheric absorption into account. Knowledge of pixel composition is not required, eliminating the need for cloud screening and arbitrary assumptions about sub-pixel cloudiness. For each GLI pixel, clear or cloudy, a daily PAR estimate is obtained. Diurnal changes in cloudiness are taken into account statistically, using a regional diurnal albedo climatology based on 5 years of Earth Radiation Budget Satellite (ERBS) data. The algorithm results are verified against other satellite estimates of PAR, the National Centers for Environmental Prediction (NCEP) reanalysis product, and in-situ measurements from fixed buoys. Agreement is generally good between GLI and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) estimates, with root-mean-squared (rms) differences of 7.9 (22%), 4.6 (13%), and 2.7 (8%) Einstein/m2/day on daily, weekly, and monthly time scales, and a bias of only 0.8–0.9 (about 2%) Einstein/m2/day. The rms differences between GLI and Visible and Infrared Spin Scan Radiometer (VISSR) estimates and between GLI and NCEP estimates are smaller and larger, respectively, on monthly time scales, i.e., 3.0 (7%) and 5.0 (14%) Einstein/m2/day, and biases are 1.1 (2%) and −0.2 (−1%) Einstein/m2/day. The comparison with buoy data also shows good agreement, with rms inaccuracies of 10.2 (23%), 6.3 (14%), and 4.5 (10%) Einstein/m2/day on daily, weekly, and monthly time scales, and slightly higher GLI values by about 1.0 (2%) Einstein/m2/day. The good statistical performance makes the algorithm suitable for large-scale studies of aquatic photosynthesis.