Thursday, February 27, 2020

The PO.DAAC is pleased to announce the public release of the NASA OBPG/JPL MODIS Aqua and Terra GHRSST Level-2P (L2P) and Level-3 (L3) Sea Surface Temperature v2019.0 datasets.

This release includes 2 MODIS_Aqua/Terra GHRSST L2P products and 48 L3 SST products (DOI list below). All the 50 datasets are produced by NASA’s Ocean Biology Processing Group (OBPG) with technical support from Dr. Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS). The 2 GHRSST L2P products are reformatted at JPL PO.DAAC to be in compliance with the Group for High-Resolution Sea Surface Temperature (GHRSST) Data Specification (GDS2). The 48 MODIS L3 SST datasets provide global day and night coverage at both 4.63 and 9.26 km spatial resolution in the daily, weekly, monthly and annual temporal range.

The v2019.0 is the updated version from the current v2014.0. The new datasets have incorporated several significant science enhancements, including (1) Improved day and night cloud identification using Alternating Decision Trees (ADTree) (Kilpatrick et. al 2019), (2) A night time Saharan dust index and aerosol correction for NSST product (Luo et. Al 2019), (3) Updated SST and SST4 algorithm coefficients based on reprocessed NASA C6.1 L1B Brightness Temperature, (4) The addition of unique algorithm coefficients for latitude > 60N to improve Arctic retrievals, and (5) Updated hypercube of single sensor error statistics (SSES).

The datasets are described and discoverable via the PO.DAAC dataset information pages. The dataset information pages also provide access to the technical documentation, including the SST algorithm ATBD, data reprocessing overview, 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

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,

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,

Comments/Questions? Please contact or visit the PO.DAAC Forum.