and Terra products as Aqua MODIS band 6 detectors are not functioning, while band 6 is used for the Terra product to compute the NDVI (Hall and Riggs, 2007). As a con- sequence, the NDVI test for forested areas is not activated for Aqua products. The successive versions of the MODISsnowproducts were compared with snow maps obtained from other sources at similar or lower resolution (i.e., rel- ative validation, Hall et al., 2002; Klein and Barnett, 2003; Maurer et al., 2003; Simic et al., 2004; Rittger et al., 2013) and validated using ground snow measurements (i.e., ab- solute validation) (Klein and Barnett, 2003; Maurer et al., 2003; Simic et al., 2004; Ault et al., 2006; Parajka and Blöschl, 2006; Arsenault et al., 2014). Most of these stud- ies were done using the Terra MODIS daily snow product (MOD10A1). More comprehensive reviews can be found in Hall and Riggs (2007) and Parajka et al. (2012). Despite the variety of methods used among these studies, the results led to the same conclusion that Terra MODISsnow cover prod- ucts have a higher overall accuracy than snow maps derived from VEGETATION or AVHRR. The typical absolute accu- racy of MOD10A1 is 93 % but depends on the land cover (Hall and Riggs, 2007; Arsenault et al., 2014). The accu- racy is lower in forested areas (Simic et al., 2004; Parajka et al., 2012). Other important sources of misdetection are cloud/snow confusion (Rittger et al., 2013), the variation of the sensor viewing angle and the reprojection from the orig- inal swath data to the sinusoid grid (Arsenault et al., 2014). The accuracy of Aqua snow product is similar although less documented. A comparison with Terra snow maps indicated
Compared to other work on the SCF temporal variations in places such as the Tibetan Plateau (Pu et al., 2007) and Up- per Rio Grande River Basin (Zhou et al., 2005), our research reduces the cloud coverage in the MODISsnowproducts to improve their quality. Even though some results of snow sim- ulations and observations have discussed the heterogeneity of snow distributions, they have mainly focused on small or lo- cal areas of about 150 km 2 or less (e.g., Bl¨oschl et al., 1991; D´ery et al., 2004). However, those methods are not necessar- ily suitable for a larger domain such as the QRB. Although Baral and Gupta (1997) have used some remotely sensed data to explore the relationships between topography and snow distribution, they only compare SCE with differences in slope and aspect and not SCF and SCD as was done in the present study. However, SCE is not the best method to eval- uate the snow distribution owing to the different percentages of various slopes and aspects. Our research takes advantage of the MODISsnowproducts for assessing SCF and SCD across larger areas. The temporal and spatial distributions of SCF and SCD instead of SCE in different elevations, slopes and aspects in the QRB reveal with accuracy the topographic controls of snow distribution in complex mountainous ter- rain. Future work may provide further details on the rela- tionships between the distribution and persistence of snow cover, hydrometeorology and topography of alpine basins as the resolution of snowproducts and DEMs improves. As an example, Tong et al. (2009) show that there exits signif- icant anticorrelations between MODIS normalized SCE and normalized streamflow based on gauge observations during snow melt seasons in the QRB, with more significant rela- tions when the SF method (rather than the original MODIS product) is used.
Abstract. A spatial filter (SF) method is adopted to re- duce the cloud coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snowproducts (MOD10A2) between 2000–2007 in the Quesnel River Basin (QRB) of British Columbia, Canada. A threshold of k = 2 cm of snow depth measurements at four in-situ observation sta- tions in the QRB are used to evaluate the accuracy of MODISsnowproducts MOD10A1, MOD10A2, and SF. Using the MOD10A2 and the SF, the relationships between snow ab- lation, snow cover extent (SCE), snow cover fraction (SCF), streamflow and climate variability are assessed. Based on our results we are able to draw several interesting conclu- sions. Firstly, the SF method reduces the average cloud cov- erage in the QRB from 15% for MOD10A2 to 9%. Sec- ondly, the SF increases the overall accuracy (OA) based on the threshold k = 2 cm by about 2% compared to MOD10A2 and by about 10% compared to MOD10A1 at higher eleva- tions. The OA for the four in-situ stations decreases with elevation with 93.1%, 87.9%, 84.0%, and 76.5% at 777 m, 1265 m, 1460 m, and 1670 m, respectively. Thirdly, an aggre- gated 1 ◦ C rise in average air temperature during spring leads to a 10-day advance in reaching 50% SCF (SCF 50% ) in the
In this perspective, remote sensing can be useful for re- constructing recent changes of snow cover extent, distribu- tion and duration in wide regions such as northern Italy. Moreover, the use of snow cover maps for hydrological pur- poses is an effective tool, since the combination of large- scale information with local estimations or measurements of snow features makes it possible to estimate the snow wa- ter equivalent (SWE) stored within a river basin (Molotch and Margulis, 2008; Bavera and De Michele, 2009). Mod- erate Resolution Imaging Spectroradiometers (MODIS) em- ployed by Terra and Aqua satellites provide a Snow Cov- ered Area product (SCA) with 500 m and daily resolutions, which consists of binary maps whereby snow is detected at the pixel scale. The accuracy of MODISSnow Cover Prod- ucts depends on region, season, snow condition and land cover type (Klein and Barnett, 2003; Maurer et al., 2003; Simic et al., 2004; Zhou et al., 2005; Tekeli et al., 2005; Ault et al., 2006; Parajka et al., 2006; Hall and Riggs, 2007; Liang et al., 2008). In Europe, Parajka et al. (2006) compared daily MODISsnow maps with in situ data of 754 climate stations over the whole of Austria, reporting an average classification accuracy of 95 % on cloud-free days. The wide and hetero- geneous Austrian territory presents similarities with our case study. Accordingly, we expect a similar quality of MODIS SCA product for northern Italy. However, the primary limi- tation in using MODISsnowproducts is that no information on ground conditions is available in areas hidden by cloud. During a year, clouds may obscure most of the study area restricting the potential of using such snow cover images. For example Parajka et al. (2006) indicated that, on average, clouds obscured 63 % of Austria in daily snow maps from February 2000 to December 2005. The possibility of benefit- ing from a reliable product with daily temporal resolution is therefore conditioned by the ability to estimate the presence of snow in overcast conditions. With this aim, several proce- dures for MODISproducts have been developed and tested in different regions (Parajka and Blöschl, 2008; Gafurov and Bárdossy, 2009; Wang et al., 2009; Hall et al., 2010; Parajka et al., 2010; Paudel and Andersen, 2011). Such methods are based on a spatio-temporal combination of MODIS data and they can generate cloudless images having accuracy compa- rable to that of the source product. Contrary to the case stud- ies in Gafurov and Bárdossy (2009) (Kokcha basin: eleva- tions range from 416 m a.s.l. up to 6383 m a.s.l., about 75 % of the basin area lies above 2000 m a.s.l.) and Paudel and An- dersen (2011) (Trans-Himalayan region: 96 % of the area lies in the elevation zone above 3000 m a.s.l., with 43 % of the area above 5000 m a.s.l.), many European rivers drain basins which cover altitudes from the lowlands up to 4000 m a.s.l. in
of the MODIS-EURAC overestimation, differs between re- gions. Lower values of the parameters, which imply higher overestimations, are found in the drier and warmer areas (Pérez-Palazón et al., 2015), 0.700 in R1 – Adra and 0.645 in R2 – Andarax; located in the south face and with lower mean elevation. On the contrary, wetter and colder regions have higher values and consequently less overestimation coming from MODIS-EURAC. Although, the general accuracy from MODISsnowproducts is estimated approximately at 93 % (Hall and Riggs, 2007) and similar studies has found an accuracy of 94.6 % comparing MODISproducts with sur- face observations over northern China, (Huang et al., 2016), the heterogeneity of the snow distribution due to the abrupt terrain and climate conditions, make the overestimations of MODIS-ERURAC over this are slightly bigger.
To document snow cover patterns and their changes over time from 2000 through 2016, we calculated the annual SP for each 500 × 500 m pixel in the study area as the fraction of the images in a year with snow cover (Saavedra et al., 2017). We masked areas with mean annual SP < 7 % to avoid potential misclassifications in MODISsnowproducts (Fig. 2a) (Hall et al., 2002). This threshold excludes the little to no snow zone (SP < 7 %) as defined in Saavedra et al. (2017). For the re- maining pixels we conducted all geostatistical analyses using the statistical computing R software (RCoreTeam, 2013). We used the mean annual SP time series 2000–2016 to capture the spatial–temporal variability of snow presence (Fig. S1a in the Supplement). We used the non-parametric Mann–Kendall analysis to test for trend significance in annual SP (Khaled and Ramachandra, 1998) and quantified the rate of change using the linear Theil–Sen slope, which determines the slope as the median of all possible slopes between data pairs (Theil, 1950; Sen, 1968). We ran the Mann–Kendall analysis using the “Kendall” package for R (McLeod, 2011). Trends were considered significant at a p value ≤ 0.05. We also calculated a standardized rate of change by dividing the original Theil– Sen slope by the mean annual SP. This allowed us to compare the rate of SP change (slope) in different snow zones. For each year, we used the annual SP to calculate the line with SP = 20 % (our definition of snowline). Then we applied a buffer function of 100 m over this line to create a band of 200 m width and calculated its mean elevation from the DEM (Saavedra et al., 2017). We evaluated the trends in snowline elevation for each 0.5 ◦ latitude band on the west and east sides using Mann–Kendall analysis and calculated rates of change by the Theil–Sen slope. Finally, we calculated trends in SP for individual months and elevation bands and exam- ined how the magnitude of trends varied seasonally and with elevation across the study area.
In Switzerland, snow variables such as total snow depth and new snow depth are measured mainly by the Federal Office of Meteorology and Climatology MeteoSwiss and the Swiss Federal Institute for Snow and Avalanche Research (SLF) in Davos. The observations consist of either manually or automatically recording stations. The amount of new snow depth and total snow depth at conventional stations is measured twice daily (morning and evening) by an observer on a representative plot in horizontal terrain (Bezzola, 2004). This network of conventional observations covers the entire region of Switzerland. The advantage of manual observations is the longer time series of up to more than 50 yr providing valuable information for climatological studies. Over the past few years, efforts have been made to identify, digitize and explore snow measurements from historical data sources dating back to the second half of the 19th century (W¨uthrich, 2008). A recently published report by MeteoSwiss (W¨uthrich et al., 2010) defined a potential basic climatological network for snow, based on the analysis of 160 historical snow measurement series. In our study, three stations have been selected from this so-called National Basic Climatological Network for Snow (NBCN-S) representing main climatological regimes in Switzerland and varying altitudes (Table 1). All in situ observation data have been well quality controlled and verified through various standard quality processing steps (Bezzola, 2004). In our study, we defined snow days with a snow depth of at least 1 cm (SCD in situ ) as used at MeteoSwiss.
Secondly, the cloud removal algorithms of MODISsnow cover products will be a benefit of the platforms with higher spatial resolution more easily and frequently. With the de- velopment of the technology, the spatial resolution of the sensor becomes higher and higher. In the framework of the multi-source fusion, the microwave-based observation with a higher spatial resolution than AMSR-E should make a dif- ference, especially with the Sentinel series. For example, Sentinel-1 SAR has the spatial resolution of 20 m (Snapir et al., 2019; Nagler et al., 2016), which will significantly im- prove the fusion accuracy of the MODISsnow cover product. Additionally, the optical observations of Sentinel series, e.g., the Sentinel-2 Multispectral Instrument (MSI) and Sentinel-3 Sea Land Surface Temperature Radiometer (SLSTR; Nagler et al., 2018; Zhu et al., 2015), also have the potential to pro- vide a snow cover product with higher spatial resolution in the future. However, the spatial resolution of Sentinel series is higher than MODIS, which results in the problem with a smaller image swath and a longer revisit period. In addition, the high-spatial-resolution data will not only contribute to snow mapping, e.g., unmanned aerial vehicles (UAVs) act- ing as an effective supplement for snow mapping (Liang et al., 2017), but they will also play a significant part in the ac- curacy validation of the cloud-removed MODISsnow cover products in the near future.
Abstract. This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November un- til mid January. In this study MODISsnow cover data were validated over Iceland with comparison to manned in situ ob- servations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R 2 values 0.96, 0.92 and 0.95, respectively, and in agree- ment with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassi- fied pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggre- gation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study pe- riod a trend of increasing snow cover duration was observed for all months except October and November. However, sta- tistical testing of the trends indicated that there was only a significant trend in June.
Using proposed methodology it was possible to estimate the snow cover dynamics of mountainous regions. The original MODISsnow cover product that contains cloud covered pixels can be processed using this methodology, meaning that snow cover data with 500 m spatial and daily resolution can be prepared. MODIS also offers eight day composite snow cover information with little or no cloud cover, but this is the maximum snow cover extent for eight composite days. Yet, a considerable fraction of snow could melt or fall within eight days time period, which may not be enough information for higher temporal resolution modelling purposes. This is why the daily snow cover information is very valuable also for model calibration and for validation purposes. Such information can be extremely helpful when modeling available water resources in moun- tainous areas where snowmelt in spring or summer becomes a valuable raw material for energy production, agriculture and for drinking purposes in lowland areas.
Abstract. The shortwave cryosphere radiative effect (CrRE) is the instantaneous influence of snow and ice cover on Earth’s top-of-atmosphere (TOA) solar energy budget. Here, we apply measurements from the MODerate resolution Imaging Spectroradiometer (MODIS), combined with mi- crowave retrievals of snow presence and radiative kernels produced from four different models, to derive CrRE over global land during 2001–2013. We estimate global annual- mean land CrRE during this period of − 2.6 W m −2 , with variations from − 2.2 to − 3.0 W m −2 resulting from use of different kernels and variations of − 2.4 to − 2.6 W m −2 re- sulting from different algorithmic determinations of snow presence and surface albedo. Slightly more than half of the global land CrRE originates from perennial snow on Antarc- tica, whereas the majority of the northern hemispheric ef- fect originates from seasonal snow. Consequently, the north- ern hemispheric land CrRE peaks at − 6.0 W m −2 in April, whereas the southern hemispheric effect more closely fol- lows the austral insolation cycle, peaking at − 9.0 W m −2 in December. Mountain glaciers resolved in 0.05 ◦ MODIS data contribute about − 0.037 W m −2 (1.4 %) of the global effect, with the majority (94 %) of this contribution originating from the Himalayas. Interannual trends in the global annual-mean land CrRE are not statistically significant during the MODIS era, but trends are positive (less negative) over large areas of northern Asia, especially during spring, and slightly negative over Antarctica, possibly due to increased snowfall. During a common overlap period of 2001–2008, our MODIS esti- mates of the northern hemispheric land CrRE are about 18 % smaller (less negative) than previous estimates derived from coarse-resolution AVHRR data, though interannual varia- tions are well correlated (r = 0.78), indicating that these data are useful in determining longer-term trends in land CrRE.
These SCA products were generated from Sentinel-2 images based on the MAJA/LIS processor. The MAJA processor computes the surface reflectance and the cloud and cloud shadow mask (). The output of MAJA is a level-2A product, that is read by the LIS processor to determine the snow cover area at 20 m resolution . The snow detection is performed using the "flat surface reflectances", i.e. surface reflectances that were corrected to remove the first order effect of the topography. The snow classification in LIS uses the Normalized Difference Snow Index :
marshlands in southern Iraq (Fig. 1). Nearly all of the flow ( ∼ 90 %) of the Euphrates river originates in the highlands of eastern Turkey, with modest contributions from the Syr- ian highlands but only minimal additions from Iraq (Gruen, 2000). The mountains in eastern Turkey contribute a smaller amount ( ∼ 40 %) to the Tigris river flow and the remainder comes from numerous tributaries that originate in the Zagros Mountains between Iraq and Iran (Gruen, 2000). A typical continental climate prevails in the basin, with a distinct north to south temperature and precipitation gradient. Most of the precipitation in the Turkish highlands in the north and in the Zagros mountains in the east occurs between November and April, mostly in the form of snow over higher elevations. At the height of the cold season (February–March), snow cover flanks the northern and eastern edges of the basin, in parallel to the shape of the Fertile Crescent (Fig. 1). Precipitation re- mains in the snowpack until the spring melt season (March– June). Mountain snowpack plays a critical role in the hy- drological cycle of the basin, with spring snow-melt being the dominant source for the flow of the two rivers. Hence, annual peak flows in both rivers exhibit a unimodal distri- bution in which the peak flow coincides with seasonal melt- ing of snow in May independent of in-season precipitation (Fig. 2). Flow steadily decreases throughout the summer and early fall, reaching its lowest value in September-October be- fore the rainy period commences again in November. Given the importance of water for energy production, flood control, agricultural productivity with irrigation, and reservoir opera- tions in the E-T basin, information on water available in the snowpack and the potential changes in the extent, timing, and magnitude of snowpack under a changing climate are crucial.
Figure 9 presents CALIOP surface reflectance compar- isons over Antarctica for clear sky (solid curves) and cloudy sky (dashed curves) conditions for all of 2009. The solid black line shows the reflectance distribution estimated from the surface tail with the constant total-to-tail signal ratio of 19.6 used in Eq. (5), while the solid red line shows the reflectance directly obtained from the saturated signals by Eqs. (2) and (4). The dashed red and black are surface re- flectances before and after correcting for the two-way trans- mittances of overlying clouds. The cloudy cases are chosen with an optical depth of about 1 to make sure that the sur- face return under the cloud is not saturated and still exhibits a reasonably robust signal-to-noise ratio. When clouds are present, the apparent mean surface reflectance before cor- recting for the cloud transmittance (red dashed curve) is about 0.26 ± 0.05. The apparent mean surface reflectance from saturated signals (solid red) is about 0.58 ± 0.05. The red dashed distribution is totally separated from the solid red distribution, which indicates that the apparent surface re- flectance at 532 nm over polar snow/ice sheet regions could be used for cloud screening when cloud optical depth is greater than 1. The mean surface reflectance estimated from the surface tail (solid black) is about 0.90 ± 0.10, while the mean surface reflectance estimated by correcting for the cloud transmittance (dashed black) from the cloudy region is about 0.84 ± 0.13. The surface reflectance under cloudy con- ditions will be more accurate if the cloud effective two-way transmittance can be obtained more accurately. The magenta line in Fig. 9 shows the MODIS reflectance distribution at
The high losses in some of the years are the result of the limited profit margin between the cost of planting and the selling price of the products. To illustrate further the effect of the profit margin in the decision and the value of information, we have run a series of additional simulations where the costs of planting are reduced by 50 %, 75 % and 100 % (which is the same as zero cost). The relative value of information for these simulations (shown in Fig. 8) indicates there is a grad- ual increase in the relative value of the informed decisions as the ratio of the benefits from the crop yield to the cost of planting increases. The fully detailed gains and losses for these simulations can be found in the Supplement (Fig. S1). Relative values are still low, however, even when there is no cost for planting. This is because the uninformed decision used as a reference also improves with the reduction in the cost of planting. The course of action that performs better on average, in which the uninformed decision is based, is path 3 for the full reported cost, path 4 for the reduced costs and path 5 when no cost is considered. This means that with lower or no investment costs for planting it is better on aver- age to plant the more water-demanding crops. These results also show that as the ratio between the profit made from the crop yield and the costs of planting increases, the relative value of the informed decisions for the years in which the optimal path is followed is also reduced.
The range of the basin and the snow line were determined using base data and topographic maps with a scale of 1:50,000 in digital format. The DEM data were generated from advanced space borne thermal emission and reflection radiometer (ASTER) images provided by the Iranian Space Agency. Images were acquired near the nadir of the 3N band (0.78 to 0.86 μ m), and stereo images were acquired for the same area after examining the 3B band (0.78 to 0.86 μ m). The ASTER 3N and 3B images were designed for the DEM creation with a Baseline/Height (B/H) from the nadir and aft-telescope of B ∕ H ¼ 0.6. One hundred tie points were used to form an epipolar image, which had a Y parallax ¼ 0.9645. Y parallax comes from a correlation analysis between the 3N band image of the left-hand side and the 3B image of the right-hand side. Therefore, the final DEM is extracted from visible and near-infrared (VNIR ¼ 0.52 to 0.86 μ m) in ASTER with a spatial resolution of 15 m. The root of the mean square error (RMSE) of the DEM was 9.80 m. The DEM was generated using the bilinear interpolation method. Figure 2 shows the central Zab basin DEM extracted from the ASTER image.
Only a limited number of studies on snow-cover recon- struction have been conducted in the past that use long-term station observations and recent remote sensing data (Robin- son, 1991; Brown, 2000; Frei et al., 1999; Brown and Robin- son, 2011). These studies are, however, conducted at the continental scale under conditions of dense station network availability and neglecting the effect of topography. Robin- son (1991) and Frei et al. (1999) conducted reconstruction of snow cover based on regression analysis between snow char- acteristics and snow-cover area (SCA) derived from AVHRR satellite observations. As snow characteristics both studies used snow-cover duration derived from interpolated station records. Another study by Brown (2000) conducted recon- struction of snow cover for “pre-satellite era” interpolat- ing snow-depth data from station network. For grid cells of nearly 200 km, the interpolation of snow cover was done us- ing different thresholds for snow depth and compared against NOAA snow-cover extent during “satellite era”. The cali- bration showed 2 cm to be most appropriate snow depth for accurate snow-cover reconstruction based on station data. Brown and Robinson (2011) updated and extended the snow-
reduce its dimensionality the ICS transformation is applied (Nordhausen et al., 2008; Tyler et al., 2009). It utilises the principal component analyses (PCA) (Mardia et al., 1979) and two scatter matrices in order to construct independent components which do not rely on a distribution mean. The first scatter matrix is a regular covariance matrix used to stan- dardise data, while the second one is a matrix of the fourth moment (kurtosis) which describes data rotation within the PCA. The eigenvalue decomposition is performed on one matrix in a relation to the other one which results in the affine invariant co-ordinate system for multivariate observa- tions. The matrices are derived on the basis of a randomly se- lected winter satellite scene with vast snow cover and utilised throughout the rest of transformations. To save computation time the ICS technique is performed only for the daytime data to combine reflectances with the thermal contrast be- tween SKT and the 10.8 µm BT. It is applied selectively to pixels with probable cloud contamination (high thermal contrast) which fulfil specific criteria. These restriction are meant to improve ice cloud detection over snow and broken cloud discrimination where spectral information is ambigu- ous, but thermal contrast with surface is significant. In this way spectral signatures of areas with small thermal contrast, which may be related to climate model inaccuracy (and not to presence of clouds), remain unchanged. In this respect ICS transformation over water bodies (areas further than 8 km from the shoreline) is performed for pixels with the SKT– 10.8 µm greater than 8 K to account for warm ocean currents not included in the SKT data. For the shoreline zones, it is applied to pixels with the 0.6 µm reflectance higher than 0.3 to account for mixed land/water pixels. Furthermore, regions below 1200 m are only considered if the SKT–10.8 µm is greater than 8 K. For higher altitudes this threshold is set to 16 K to account for the local thermal variations which can- not be resolved by a coarse-resolution climate model. Over land, pixels which are unlikely to be overcast with the re- flectance lower than 0.15 at 0.6 µm or with the 10.8 µm BT greater than 290 K are not considered. After the ICS transfor- mation the size of the LUT is reduced by the SKT dimension. Moreover, if it is not available, the PCM algorithm can still proceed without enhanced reflectances. A short overview on the spectral features employed in the PCM is presented in the next subsections.
Abstract. MODerate resolution Imaging Spectroradiome- ter (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼ 5 km spatial resolutions. These products are used extensively in hydrological modeling and climate stud- ies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collec- tion 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S- NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial res- olution have been available since 2011 and are currently be- ing reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud- gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud- persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in