Remote sensing of primary production—II. A semi-analytical algorithm based on pigments, temperature and light

Abstract

A semi-analytical algorithm for estimating integrated primary production is described which uses pigment, temperature and light data. The algorithm was designed using 648 stations of data from the California Cooperative Fisheries Investigations and the Southern California Bight Study. Pigment and temperature values were used to describe maximum photosynthesis in the surface waters. A model for the vertical distribution of chlorophyll was devised which simplifies the estimation of those pigments too deep for the satellite to detect. Quantum yield, light utilization efficiency, and chlorophyll-specific light utilization efficiency were described and parameterized for inclusion in the algorithm. Variance in the photosynthetic yield term was typically the largest. Some of the variance could be partitioned as nutrient effects and inter-cruise variability. Algorithm performance could be increased considerably by using one or two stations as “calibration” stations for each area of about 300,000 km 2 (and deleting such stations from subsequent analysis). Using ship data as input, the uncalibrated algorithm explained about 35% of the variance in primary production whereas the calibrated algorithm accounted for 58% of the variance. Using satellite data as input, the uncalibrated and calibrated algorithm accounted for 35 and 48% of the variance in primary production, respectively. Of the algorithm examined in Parts I and II of this series, the semi-analytical algorithm described here explains the most variance and comes the closest to a 1:1 ratio of predicted to observed production.