Abstract.Trichodesmium is the main nitrogen-fixing species in the South Pacific region, a hotspot for diazotrophy. Due to the paucity of in situ observations, methods for detecting Trichodesmium presence on a large scale have been investigated to assess the regional-to-global impact of these species on primary production and carbon cycling. A number of satellite-derived algorithms have been developed to identify Trichodesmium surface blooms, but determining with confidence their accuracy 20 has been difficult, chiefly because of the scarcity of sea-truth information at time of satellite overpass. Here, we use a series of new cruises as well as airborne observational surveys in the South Pacific to quantify statistically the ability of these algorithms to discern correctly Trichodesmium surface blooms in the satellite imagery. The evaluation, performed on MODIS data at 250m and 1km resolution acquired over the South West Pacific, shows limitations due to spatial resolution, clouds, and atmospheric correction. A new satellite-based algorithm is designed to alleviate some of these limitations, by exploiting 25 optimally spectral features in the atmospherically corrected reflectance at 531, 645, 678, 748, and 869 nm. This algorithm outperforms former ones near clouds, limiting false positive detection, and allowing regional scale automation. Compared with observations, 80% of the detected mats are within a 2 km range, demonstrating the good statistical skill of the new algorithm. 2 taking into account environment properties (wind, sea surface temperature, …), fluorescence, and spatial structure of filaments, and a better understanding of Trichodesmium dynamics, including aggregation processes to generate surface mats. Such submesoscales aggregation processes for Trichodesmium are yet to be understood.