The recent R2019.0 processing of the Aqua/Terra Moderate-resolution Imaging Spectroradiometer (MODIS) Sea Surface Temperature (SST) data by NASA's Ocean Biology Processing Group (OBPG) has made significant improvements from the previous R2014.0 SST version. This new version has incorporated several enhancements, including a revised cloud classification scheme based on the theory of Alternating Decision Trees (ADtree) developed by Freund and Mason 1999 and modified by Pfahringer et. al. 2000, improved day and night cloud identification using ADTrees (Kilpatrick et. al 2019), and a night time Saharan dust index and aerosol correction for the NSST product (Luo et. Al 2019).
The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging; OBPG, also functioning as the NASA ground data system, is responsible for the production of daily MODIS ocean products. The PO.DAAC at JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P Data specification (GDS2).
MODIS Level-3 products provide the day and night gridded global skin SSTs from the MODIS instrument onboard the Aqua/Terra satellites, sampled temporally at averages of daily, weekly (8 day), monthly and annual time scales, and spatially at 4.63 and 9.26 km, adding up to a total of 48 datasets. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR (11 and 12 micron) wavelength channels. The second SST product is generated using the mid-infrared (3.95 and 4.05 micron) wavelength channels which are unique to MODIS and only applies to the night image.
Both L2P and L3 SST datasets can be discovered at the PO.DAAC dataset information pages. The dataset information pages also provide access to the technical documentation, MODIS SST algorithms (ATBD), R2019 reprocessing and guidance on how to cite the data.
Freund,Y. and Mason L., (1999) "The alternating decision tree learning algorithm", Proceedings of the 16th International Conference on Machine Learning, Bled, Slovenia, pp. 124-133
Pfahringer B., Holmes G., Kirkby R. (2001) "Optimizing the Induction of Alternating Decision Trees", In: Cheung D., Williams G.J., Li Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science, vol 2035. Springer, Berlin, Heidelberg doi:10.1007/3-540-45357-1_50
Luo, B., Minnett, P. J., Gentemann, C. and Szczodrak, G., (2019) "Improving satellite retrieved night-time infrared sea surface temperatures in aerosol contaminated regions", Remote Sensing of Environment,Vol. 223, doi:10.1016/j.rse.2019.01.009
Kilpatrick, K.A., G. Podestá, E. Williams, S. Walsh, and P.J. Minnett, 2019: Alternating Decision Trees for Cloud Masking in MODIS and VIIRS NASA Sea Surface Temperature Products. J. Atmos. Oceanic Technol., 36, 387–407, doi:10.1175/JTECH-D-18-0103.1