72nd EASTERN SNOW CONFERENCE Sherbrooke, Québec, Canada, 2015

An Introduction to the new NASA Suomi-NPP VIIRS Snow Cover Data Products

GEORGE A. RIGGS1 AND DOROTHY K. HALL2

ABSTRACT

A synopsis of the soon-to-be-released NASA Suomi-NPP (S-NPP) snow cover data products produced in the Land Science Investigator-led Processing System (LSIPS) is presented. The cryospheric science of the Suomi-NPP Visible Imaging Radiometer Suite (VIIRS) instrument builds and expands on the heritage of cryospheric science from the EOS Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Suomi-NPP was launched on 28 October 2011 from Vandenberg Air Force Base in California with the first image acquired on 21 November 2011. In 2014 NASA selected the LSIPS to work along with science investigators to produce Science Data Records (ESDRs) and improve and revise the algorithms and data products produced for the science community. The NASA VIIRS snow cover algorithm and data products and the processing sequence and contents of the data products are described.

Keywords: snow cover, snow cover extent, NDSI, NPP, VIIRS.

SNOW COVER AND CLIMATE CHANGE

The importance of monitoring snow cover onset, duration and melt date relative to climate change has been made evident in several studies. Satellite and in-situ data show that Northern Hemisphere snow cover extent, and timing of snow melt are changing (Choi, et al., 2010; Brown and Robinson, 2011; Robinson and Estilow, 2013). Derksen and Brown (2012) and Brown et al. (2010) found that snow melt is occurring earlier in the spring season at some locations in the Northern Hemisphere. Spring snow cover has undergone significant reductions over the past 90 years and the rate has accelerated over the past 40 years. Results from the Rutgers Global Snow Lab snow cover climate-data record (CDR) (http://climate.rutgers.edu/snowcover) for the 47-year record, show an earlier spring snowmelt in the Northern Hemisphere (−2.12± 0.45%/decade).

SUOMI-NPP VIIRS

The Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) instrument builds and expands on the heritage of cryospheric science from the (EOS) MODIS instrument. S-NPP was launched on 28 October 2011 from Vandenberg Air Force Base in California with the first image acquired on 21 November 2011. The VIIRS is a scanning radiometer collecting data across visible and infrared wavelengths. Its design and capabilities are similar to MODIS; it is the follow on instrument to the MODIS. A series of VIIRS instruments

1 SSAI, Lanham, MD, 20706, george.a.riggs@.gov 2 Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, [email protected] 107 are planned. Information on the S-NPP mission and instruments can be found at the NASA Polar- Orbiting missions website (npp.gsfc.nasa.gov/Suomi.html).

VIIRS SNOW COVER PRODUCT

The S-NPP snow cover algorithm and data products will extend the EOS snow cover data product record. The NASA EOS MODIS snow cover data products record that began in 2000 has been used in a range of research and applications to monitor snow cover onset, duration and melt date in studies ranging from synoptic study of snow cover related to climate change to monitoring of watershed snow cover for hydrological modeling. The NASA S-NPP VIIRS snow cover data products will be used to study the occurrence and extent of snow cover across all landscapes in ways similar to those of MODIS.

MODIS snow cover data products have been used to make snow cover duration maps. Dietz, et al. (2012) made a snow cover duration map of Europe 2000-2011 using the MODIS (MOD10A1) and (MYD10A1) data products with assessed accuracy of above 90%. In follow on research Dietz, et al. (2013) made a snow cover duration map of central Asia 2000-2011 and detected a gradient in snow cover duration. MODIS snow cover products have been used to make snow depletion curves in hydrologic studies and modeling on regional scales with overall good results (e.g., Dèry and Brown, 2007; Parajka and Blöschl, 2008; Hall et al., 2012). MODIS snow-cover products have been used to study trends or interannual variability in snow cover (e.g., Pu et al., 2007). A bibliography is maintained on our MODIS snow and ice project website (http://modis-snow-ice.gsfc.nasa.gov/?c=publications) which contains more than 280 citations for the standard MODIS snow and sea ice products. The daily MODIS snow cover extent product can be used to accurately track seasonal snow cover on the landscape, as demonstrated for a region of the Appalachian Mountains in West Virginia (Riggs and Hall, 2014). The MODIS snow cover algorithm has been robust across most landscapes and conditions with accuracy > 90% under most situations and the products have been applied in a variety of research and applications. Similar accuracy and usage is expected for the NASA VIIRS snow cover product. The VIIRS snow cover products will continue the EOS snow cover data record that was begun with the Terra MODIS in February 2000.

SNOW COVER DETECTION

Snow cover can be detected based on the snow characteristic of high visible reflectance and low shortwave infrared reflectance. That snow reflectance characteristic also distinguishes snow from many other features. High visible reflectance of snow at wavelengths < 0.7 µm distinguishes it from vegetation which has low reflectance and in the shortwave near-infrared (SWIR); vegetation typically has higher reflectance than snow. The algorithm uses the normalized difference snow index (NDSI) which is effective at detecting snow cover globally over a range of nominal viewing conditions. The basic theory of the NDSI is discussed then limitations, sources of error and practical implementation of the snow detection algorithm are presented in the following sections.

Snow cover reflectance characteristics of high visible (VIS) reflectance in the 0.3 – 1.0 µm bands and low reflectance in the SWIR ~ 1.6 µm are the primary characteristics used to detect snow cover on the landscape using satellite sensors with spectral bands in the visible and infrared wavelength regions. Frei et al. (2012) discuss the reasons for monitoring snow cover with visible and NIR, and passive microwave sensors, and the techniques and algorithms used to generate global scale daily snow cover products. Under clear skies and good viewing geometry the high VIS and low SWIR characteristic of snow is readily observable but limitations increase in situations of low illumination, dense vegetation and cloud cover

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Normalized Difference Snow Index (NDSI) Use of visible and shortwave near-infrared reflectance as a ratio or as a normalized difference to detect snow cover has been done since the mid 1970s in various forms with different aircraft and satellite sensors to reliably and accurately map snow cover. A concise history of the use of VIS to SWIR ratios and the NDSI is given in Hall and Riggs (2011). The NDSI has been used in the MODIS snow cover product algorithm and is used in the S-NPP VIIRS snow cover product algorithm.

The NDSI is an index that indicates the presence of snow cover on the surface based on snow characteristics of high VIS reflectance and very low SWIR. If snow is present and viewable by a satellite then the NDSI will be > 0. The NDSI is a measure of the relative difference of reflectance between a VIS band and a SWIR band. The basic NDSI applicable to sensors with VIS and SWIR channels is NDSI = (VIS - SWIR) / (VIS + SWIR). The VIIRS NDSI is

NDSI = (I1 – I3) / (I1 + I3)

Where I1 is the VIIRS band centered at 0.64 µm and I3 is the VIIRS band centered at 1.61 µm, both at a nominal 375 m spatial resolution.

The NDSI can theoretically range from -1.0 – 1.0, with a value of 0.0 or less indicating no snow. Snow does not have SWIR reflectance greater than visible reflectance, which would result in a negative NDSI. Practically the NDSI for snow ranges from 0.0 to 1.0. To demonstrate the NDSI let us assume that snow visible reflectance increases from 0.0 to 100% in increments of 1.0 and that the SWIR reflectance remains constant at 10%. A SWIR reflectance of 10% from snow is reasonable for a snow covered surface with minimal vegetation or other surface features based on reflectance plots in the literature and extensive interpretation of MODIS imagery. As the VIS – SWIR reflectance increases the NDSI increases as shown by the black asterisk line in Figure 1. The NDSI is plotted vs. the numerator difference to show change in NDSI as the difference between VIS and SWIR increases. As the visible reflectance increases the difference between visible and near-IR increases with NDSI increasing, reaching a maximum NDSI of 0.81 (Fig. 1). An in depth discussion of the NDSI is presented in Riggs and Hall (2015a).

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Figure 1. Basic NDSI plot. Black line with asterisks is NDSI with difference of VIS – SWIR increasing from 0 to 100% in increments of 1 while SWIR reflectance is constant at 10%.

The NDSI is a robust indicator of snow on the surface that has been used in various forms by many investigators since the late 1980s primarily to generate snow cover area (SCA) maps based on setting a NDSI threshold above which the surface is considered as 100% snow cover and below which the surface is considered not snow covered. A global NDSI threshold value of 0.4 has been used. That threshold line is drawn on the NDSI plot in Figure 1, however many researchers have shown that SCA maps can be improved in specific situations if the NDSI set for that situation with methods of threshold selection based on visual inspection/interpretation, empirical relationship or automated selection (Yin et al., 2013) and that the NDSI threshold setting may possibly be set as low as 0.1 for SCA determination. Arbitrarily setting an NDSI threshold for SCA prevents information at lower values from being used; at 0.4 nearly half the NDSI data value range is ignored.

The ability to detect snow cover is related to the difference in reflectance in the VIS and SWIR. When there is a greater difference the NDSI is greater thus there is a high certainty of the presence of snow cover. As the difference decreases so does the NDSI and the range of NDSI decreases. With a lower difference the certainty of snow cover detection decreases. Mishra et al. (2009) investigated the range of NDSI value of snow in the Himalayan region relative to sub pixel snow modeling and found an NDSI range of 0.04 to 0.92 with increasing amounts of snow cover and that different snow fractions in a pixel may have the same NDSI value due to percentages of other components in reflectance from a pixel. The NDSI was found to be a more useful variable and to provide more information than a binary SCA by Kolberg and Gottschalk (2010) in modeling and study of interannual variability of snow cover depletion curves. The NDSI has been used in neural network research of snow cover mapping because of its greater information content as compared to using a binary SCA e.g. Dobreva and Klein (2011). MODIS derived NDSI was used by Hassan et al. (2012) for reconstructing spatial dynamics of SWE and snow depths in northern Alberta. Some researchers have noted the ability to correctly detect snow below the 0.4

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NDSI threshold (Jain et al., 2008). NDSI from higher spatial resolution sensors has been used with great success to delineate glaciers (Racoviteanu et al., 2008). The NDSI can be used to estimate snow cover extent or fraction in a particular region by fitting relationships e.g. Lin et al., (2012). Generating an NDSI snow cover product allows users to use the NDSI directly in their research/application, combine it with other data, or set their own threshold for making a SCA map.

Algorithm Limitations Though the NDSI is a robust technique for snow detection it does have limitations. Two classes of error associated with this technique are; 1) snow omission error, i.e., missing snow when snow is present, and 2) snow commission error which is detection of non-snow features as snow. Snow omission error is generally infrequent when the full range of NDSI is used however very low illumination or reflectance conditions may increase the probability of omission errors. Snow commission error is the most common snow detection error and most visually obvious when snow is mapped in places where snow is extremely improbable, e.g. in the Amazon jungle, or along ocean coastlines in tropical regions, or on bright surfaces such as salt flats.

The algorithm technique to alleviate or flag possible snow detection errors is to apply screens to all pixels that were detected with snow. Screens are tests for snow or non-snow reflectance or other expected snow characteristics that if failed will reverse a snow detection and set a bit flag, or set a bit flag indicating uncertainty in the snow detection. A surface temperature screen is also applied to alleviate snow commission errors. The theory of the screens is presented in Riggs and Hall (2015a) and how to read and use them is described in Riggs and Hall (2015b). Thus only a brief description is given below.

Snow Detection Screens and QA bit flags A surface temperature screen of 281 K is used to reverse a snow detection (NDSI > 0.0) based on the fact that it is too warm to be snow. This screen is linked to elevation and will reverse snow detections at ≤ 1300 m. At higher elevations a snow decision is not reversed but the bit flag is set to indicate warm snow. At a synoptic scale this surface temperature screen is effective at blocking snow commission errors on warm surfaces but is not effective on cold surface snow commission errors.

Uncertainty increases at the very low limit of snow detection, NDSI  0.1. Analysis of a great many situations with and without snow cover has resulted in setting a low limit on snow detection that decreases snow commission errors while not greatly increasing snow omission errors. A pixel with an NDSI  0.1 is set to 0 and a QA bit flag is set.

Constantly decreasing visible reflectance across the VIS to the IR is not a typical snow reflectance characteristic; snow typically exhibits increases and decreases in reflectance across the visible wavelengths. So a snow detection that has an NDSI ≥ 0.1 and constantly decreasing reflectance across the visible bands has a QA bit flag set to indicate unusual reflectance and uncertainty in the snow detection.

If visible reflectance in the red or green band is  11% then the reflectance is too low to run the algorithm and a no decision result is output and a QA bit flag is set.

Snow/ice cover is detected for inland water bodies in the NDSI snow cover data field. The inland water mask is set as a QA bit flag so that users can extract or mask the inland water bodies.

Cloud Cover The VIIRS cloud mask product (JPSS, 2014) is used to mask cloud cover.

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NASA VIIRS SNOW COVER PRODUCTS

The VIIRS snow cover products will be produced as a series starting with the Level-2 swath product, then the daily projected and gridded tile product and finally a daily global gridded product. The snow detection algorithm is applied at Level-2 then the swath products are composited, projected and gridded to make a daily snow cover product using the ‘best’ observation of the day into tiles of global projection. A daily global snow cover map will be made from the daily tiled products. The naming scheme of the products will be similar to that of the MODIS products; each product will have an Earth Science Data Type (ESDT) label. The three snow cover products are

VIIRSNP10_L2 – swath product, 375 m resolution, 6 min of coverage, snow detection algorithm, NDSI snow cover, basic QA, QA bit flags

VIIRSNP10A1 – daily tiled product, 375 m resolution, swath product projected onto sinusoidal grid of 10° x 10° tiles, using ‘best’ observation of the day (composite of overlapping VIIRSNP10_L2) NDSI snow cover, basic QA and QA bit flags

VIIRSNPCMG – daily global product, probably at 25 km resolution but could possibly be made at higher spatial resolution.

Images of the data fields of a VIIRSNP10_L2 product are shown in Figures 2-4. A detailed description of the products and data fields will be found in the user guide for the products. The NDSI snow cover (Fig. 2) shows snow cover in shades of blue ranging from no snow (0) to snow dominated (100) pixels. The ocean mask, cloud mask and other masks are mapped to make a thematic snow cover map. VIIRS is a scanning radiometer so as a scan sweeps across the field of view the scan view increases in width thus the same Earth location is imaged in successive scans towards the right and left sides of the swath, that creates overlap, bowtie overlap. Onboard and on ground processing of image data remove that overlap, bowtie trim, replacing it with bowtie trim values. The black lines on the sides of Fig. 2 are bowtie trim. In Fig. 3 the basic QA and QA bit flags data layers are shown. At this scale pixel details is very difficult to see but the QA bit flag for inland water bodies clearly maps the Great Lakes, near bottom of the swath. The QA bit flags are shown in higher resolution in Fig. 4 a subset of northwestern Ohio in the swath. Some of the QA bit flags show where a snow detection was changed to 0 and the reason for the change, while other QA bit flags indicate a lowered confidence in the snow detection i.e. looks like snow but may not be snow. An explanation of QA bit flags is in the user guide. Reprojection of the swath (Fig. 5) removes the bowtie trim and changes the format to GeoTIFF.

An example of the VIIRSNP10A1 tiled product NDSI snow cover field is shown in Fig. 6. The tile covers approximately 10° x 10° and is projected in the MODIS sinusoidal projection and converted to GeoTIFF in this example. This product is similar to the MODIS tiled product, MOD10A1, which has been the most commonly used snow cover product, and thus it is expected to be the most commonly used VIIRS snow cover product.

An example of the daily global gridded VIIRSNPCMG that was made using the NASA VIIRS algorithms is not yet available. The spatial resolution of this product as a baseline specification of 25 km however a higher resolution may be selected as suggested by many in the user community.

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Figure 2. VIIRSNP10_L2, 2014059.1845.* full swath product, NDSI snow cover map. Black lines are bowtie trim – done onboard and in on ground processing to remove pixels that were in scan line overlaps of the VIIRS instrument.

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Figure 3. VIIRSNP10_L2, 2014059.1845.* full swath product, NDSI snow cover map and corresponding basic QA data layer, upper left image, and the QA bit flags data layer, upper right image.

Figure 4. Northwestern Ohio subset from swath VIIRSNP10_L2, 2014059.1845.* (shown in Fig 2 and 3) showing snow cover landscape detail (left) and the corresponding QA bit flags (right).

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Figure 5. VIIRSNP10_L2, 2014059.1845.* swath projected to geographic projection using LSIPS reprojection tool. Bowtie trim is excluded from the reprojection.

Figure 6. VIIRSNP10A1, 2014059, 10° x 10° tile in geographic projection. Sinusoidal projection may be used in production; a final decision not yet been made on projection. Includes data fields for NDSI snow cover, basic QA and QA bit flags.

Product Generation The NASA LSIPS generates the products. LSIPS personnel collaborate with the VIIRS Cryosphere Team to code the VIIRS snow algorithms and products and to run unit and global tests of the individual products and chain tests of products. As the first step the LSIPS has adapted the MODIS snow cover MOD10_L2 algorithm (C5) to create the NASA VIIRS snow cover algorithm and data product VIIRSNP10_L2 and we have evaluated unit and a day global run test outputs.

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The VIIRS Cryosphere Team collaborates with LSIPS to revise algorithms and data products. There will be revisions and reprocessing of the NASA VIIRS EDRs.

Data Availability The NSIDC will be the Distributed Active Archive Center (DA AC) archiving, distributing and providing use services for the NASA VIIRS snow cover products, nsidc.org/daac/. Documentation of the products will be available at NSIDC. The NASA S-NPP VIIRS land website (npp.gsfc.nasa.gov/virrs.html) provides information on the mission, instruments, science and data, including the Algorithm Theoretical Basis Document (ATBD) and user guide.

REFERENCES:

Brown RD, Derksen C, Wang L. 2010. A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967-2008. Journal of Geophysical Research 115: D16111. DOI: 10.1029/2010JD013975. Brown RD, Robinson DA. 2011. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. The Cryosphere 5: 219-229. Choi G, Robinson DA, Kang S. 2010. Changing Northern Hemisphere Snow Seasons. Journal of Climate 23:5305-5310. DOI: 10.1175/2010JCLI3644.1. Derksen C, Brown R. 2012. Spring snow cover extent reductions in the 2008-2012 period exceeding climate model projections. Geophysical Research Letters 39: L19504. DOI: 10.1029/2012GL053387. Déry SJ, Brown RD. 2007. Recent Northern Hemisphere snow cover extent trends and implications for the snow--feedback. Geophysical Research Letters 34:L22504. DOI: 10.1029/2007GL031474. Dietz AJ, Wohner C, Kuenzer C. 2012. European snow cover characteristics between 2000 and 2011 derived from improved MODIS daily snow cover products. Remote Sensing. DOI:10.3390/rs4082432. Dietz AJ, Kuenzer C, Conrad C. 2013. Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34(11), 3879-3902. DOI: 10.1080/01431161.2013.767480. Dobreva I, Klein AG. 2011. Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance. Remote Sensing of Environment 115:3355-3366. DOI: 10.1016/j.rse_2011.07.018. Frei A, Tedsco M, Lee S, Foster J, Hall DK, Kelly R, Robinson D. 2012. A review of global satellite-derived snow products. Advances in Space Research 50(2012) 1007-1029. DOI: 10.1016/j.asr.2011.12.021. Hall DK, Riggs GA. 2011. Normalized-Difference Snow Index (NDSI). In Encyclopedia of Earth Sciences, Encyclopedia of Snow, Ice and Glaciers, Singh VP, Singh P, Haritashya UK (eds). Springer Science+Business Media B.V. DOI: 10.1007/978-90-481-2642-2_376. Hall DK, Foster JL, DiGirolamo NE, Riggs GA. 2012. Snow cover, snowmelt timing and stream power in the Wind River Range, Wyoming. Geomorphology 137:87-93. DOI: 10.1016/j.geomorph.2010.11.011 Hassan QK, Sekhon NS, Magai R, McEachern P. 2012. Reconstruction of snow water equivalent and snow depth using remote sensing data. Journal of Environmental Infomatics 20(2): 67-74. DOI: 10.3808/jei.201200221. Jain SK, Goswami A, Saraf AK. 2008. Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions, International Journal of Remote Sensing. DOI: 10.1080/01431160801908129. JPPS. 2014. Joint Polar Satellite System (JPSS) VIIRS cloud mask (VCM) Algorithm Theoretical Basis Document (ATBD). URL npp.gsfc.nasa.gov/documents.html.

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Kolberg S, Gottschalk L. 2010. Interannual stability of grid cell snow depletion curves as estimated from MODIS images. Water Resources Research 46:W11555. DOI: 10.1029/2008WR007617. Lin J, Feng X, Xiao P, Li H, Wang J, Li Y. 2012. Comparison of snow indexes in estimating snow cover fraction in a mountainous area in northwestern China. IEEE Geoscience and Remote Sensing Letters 9(4): 725-729. DOI: 10.1109/LGRS.2011.2179634. Mishra VD, Neghi HS, Rawat AK, Chaturvedi A, Singh RP. 2009. Retrieval of sub-pixel snow cover information in the Himalayan region using medium and coarse resolution remote sensing data. International Journal of Remote Sensing 30(18): 4707-4731. DOI: 10.1080/01431160802651959. Parajka J, Blöschl G. 2008. Spatio-temporal combination of MODIS images – potential for snow cover mapping. Water Resources Research 44:W03406. DOI: 10.1029/2007/WR006204. Pu Z, Xu L, Salomonson V. 2007. MODIS/Terra observed seasonal variations of snow cover over the Tibetan Plateau. Geophysical Research Letters 34:L06706. DOI: 10.1029/2007GL029262. Racoviteanu AE, Arnaud Y. Williams MW, Ordoñez J. 2008. Decadal changes in glacier parameters in the Cordillera Blanca, Peru, derived from remote sensing. Journal of Glaciology 54(186): 499-510. DOI:10.3189/002214308785836922. Riggs G, Hall DK. 2014. Tracking seasonal Appalachian snow cover with MODIS daily snow cover product. Proceedings of the 71st Annual Eastern Snow Conference, 3-5 June 2014, Boone, NC. www.easternsnow.org. Riggs G, Hall DK. 2015a. S-NPP VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD), In preparation. Available at npp.gsfc.nasa.gov/Suomi.html. Riggs G, Hall DK. 2015b. NASA VIIRS Snow Cover Products Users Guide, In preparation. Availabile at modis-snow-ice.gsfc.nasa.gov or npp.gsfc.nasa.gov/Suomi.html. Robinson D, Estilow T. 2013. 2013 Arctic report card: spring snow cover below average again. Climate.gov URL www.climate.gov/news-features/featured-images/2013-arctic-report-card- spring-snow-cover-below-average-again. Yin D, Cao X, Chen X, Shao Y, Chen J. 2013. Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery International Journal of Remote Sensing 34(19): 6529-6538. DOI: 10.1080/01431161.2013.803631.

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