Overview of NASA's MODIS and VIIRS Snow-Cover Earth System Data Records

George A. Riggs, Dorothy K. Hall, Miguel O. Román
2017 Earth System Dynamics Discussions  
Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua
more » ... raft and the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally-applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375&amp;thinsp;m native resolution compared to MODIS 500&amp;thinsp;m), the snow-detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. <br><br> DOIs of the referenced datasets: <br> MODIS Collection 5<br> doi: <a href="http://dx.doi.org/10.5067/ACYTYZB9BEOS">http://dx.doi.org/10.5067/ACYTYZB9BEOS</a><br> doi: <a href="http://dx.doi.org/10.5067/R90VAMI75N22">http://dx.doi.org/10.5067/R90VAMI75N22</a><br> doi: <a href="http://dx.doi.org/10.5067/63NQASRDPDB0">http://dx.doi.org/10.5067/63NQASRDPDB0</a><br> doi: <a href="http://dx.doi.org/10.5067/ZFAEMQGSR4XD">http://dx.doi.org/10.5067/ZFAEMQGSR4XD</a><br> doi: <a href="http://dx.doi.org/10.5067/EI5HGLM2NNHN">http://dx.doi.org/10.5067/EI5HGLM2NNHN</a><br> doi: <a href="http://dx.doi.org/10.5067/EW53FPU9NAS6">http://dx.doi.org/10.5067/EW53FPU9NAS6 </a> <br><br> MODIS Collection 6<br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MOD10_L2.006">http://dx.doi.org/10.5067/MODIS/MOD10_L2.006</a><br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MYD10_L2.006">http://dx.doi.org/10.5067/MODIS/MYD10_L2.006</a><br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MOD10A1.006">http://dx.doi.org/10.5067/MODIS/MOD10A1.006</a><br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MYD10A1.006">http://dx.doi.org/10.5067/MODIS/MYD10A1.006</a><br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MOD10C1.006">http://dx.doi.org/10.5067/MODIS/MOD10C1.006</a><br> doi: <a href="http://dx.doi.org/10.5067/MODIS/MYD10C1.006">http://dx.doi.org/10.5067/MODIS/MYD10C1.006</a> <br><br> VIIRS Collection 1<br> <a href="http://dx.doi.org/10.5067/VIIRS/VNP10.001">doi:10.5067/VIIRS/VNP10.001</a>
doi:10.5194/essd-2017-25 fatcat:y3iiw7voizgk3awgehlroknima