Abstract

Abstract. 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 spacecraft and the Suomi National Polar-orbiting Partnership (S-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; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) 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 m native resolution compared to MODIS 500 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 at the National Snow and Ice Data Center in Boulder, Colorado.

Highlights

  • NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover data products have been available since early 2000 following the 18 December 1999 launch of the Terra satellite

  • In this paper we describe the MODIS Collection 6 (C6) and NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products

  • Consistent cloud masking is a significant challenge as the cloud masking algorithms can be affected by changes in sensor performance and changes in satellite orbit characteristics and changes in the algorithm, all of which contribute to changes in cloud detection observed between the MODIS Collection 5 (C5) and C6 and VIIRS cloud mask products

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Summary

Introduction

NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover data products have been available since early 2000 following the 18 December 1999 launch of the Terra satellite. The overarching objective of mapping snow cover with the MODIS and VIIRS instruments is to develop a consistent long-term snow-cover dataset based on a globally applicable algorithm that is able to detect snow cover under a wide variety of solar viewing angles in different land covers and seasonal conditions. The specific objective of the algorithms used to generate the MODIS snow-cover products in C6 and the NASA VIIRS C1 is to optimize the accuracy of mapping SCE on the global scale and to minimize snow-cover detection errors of omission and commission. Notable revisions have been made in the Terra and Aqua MODIS snow-cover algorithms and data products in C6 compared to C5 (Riggs et al, 2016a, b, c) to correct known errors, improve accuracy and increase data content. Description of the NASA VIIRS C1 snow-cover products, produced with the same snow-cover detection algorithm used for MODIS, is given to introduce potential users to the products

Background
Surface temperature screen
Solar illumination
Shadowed landscape
Clouds
Data products in MODIS Collection 6
NDSI snow-cover dataset
Findings
Conclusions
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