Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

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

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

“…The temperature differences are larger at higher temperatures. Finally, the cloud mask algorithms are improved in C6 (Riggs et al, 2017), resulting in a less strict cloud mask over Greenland.…”

“…Making this threshold decision may depend on the level of error that is acceptable given the analysis at hand. The ideal improvement would not be merely to change the threshold value, but to continue to improve cloud detection algorithms, which is continually done with each MODIS collection iteration (e.g., Riggs et al, 2017).…”

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

“…With 36 channels, of which seven are dedicated to land remote sensing, automated global snow‐mapping algorithms were developed (Hall et al, ), based on heritage work using Landsat (e.g., Dozier, ; Dozier & Marks, ) and MODIS Airborne Simulator (MAS) data (Hall et al, ). A suite of MODIS standard snow‐cover products was produced that continues today, serving hundreds of users internationally (Hall et al, ; Riggs et al, , ). And thanks to the Earth Observing System Data and Information System (EOSDIS), anyone in the world can download and use the snow maps for free (Wolfe & Ramapriyan, ).…”

“…VIIRS, launched in 2011, has added another tool for mapping global snow cover from space. With its 375‐m spatial resolution and 22 bands in the visible, near thermal infrared and infrared parts of the spectrum, automated algorithms are being developed by NASA to extend the snow cover data record of MODIS (Justice et al, ; Riggs et al, , ).…”

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

“…Therefore, in recent years, the study of snow snow cover (FSC) products [55]. In 2016, MODIS version 6 was released, which no longer provides binary snow cover products but instead offers NDSI products, which can be regarded as continuous numerical products [52,56]. Compared with binary snow cover products, NDSI products can provide more information, which is more conducive to hydrological and ecological process simulations.…”