Moderate Resolution Imaging Spectroradiometer Snow Cover Products Research Articles

Development of Snow Cover Frequency Maps from MODIS Snow Cover Products

With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from 2001–2020 using a ~5 km resolution MODIS snow cover map. For each day of the year the maps show the frequency of snow cover for the 20-year period on a per-grid cell basis. Following on from other work to develop snow frequency maps using MODIS snow cover products, we include spatial filtering to reduce errors caused by ‘false snow’ that occurs primarily due to cloud-snow confusion. On our snow frequency maps, there were no regions or time periods with a noticeable absence of snow where snow was expected. In one example, the MODIS derived frequency of snow cover on 25 December compares well with NOAA’s historical probability of snow on the same day. Though the MODIS derived snow frequency and NOAA probabilities are computed from very different data sources, they compare well. Though this preliminary research is promising, much future evaluation is needed.

Cloud–Snow Confusion with MODIS Snow Products in Boreal Forest Regions

Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.

Snow cover detection in mid-latitude mountainous and polar regions using nighttime light data

Traditional optical remote sensing data have been widely used for snow cover detection and monitoring. However, they are limited to daytime detection and often suffer from large data gaps due to frequent cloud obscuration. This is in particular a serious challenge for high-latitude and polar regions where long nights prevail during the winter. Nighttime light sensors have a strong capability of sensing the low-level reflected moonlight. They potentially provide a new way to detect snow cover. In this study, we quantitatively analyzed the moonlight intensity for snow detection and developed a Minimum Error Thresholding (MET) algorithm to detect snow cover from the data collected by Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite data. For the two case study sites, Abisko in the sub-Arctic zone and the Tibetan Plateau, our analysis results suggest that the moonlight provides sufficient illumination to map snow cover for approximately 10 days in a lunar month. Our nighttime snow cover detection method was quantitatively evaluated by comparing our S-NPP VIIRS DNB snow cover estimates with in situ station observations, Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products over Abisko region and the Tibetan Plateau during the 2017–2018 snow season. The overall accuracy of S-NPP VIIRS snow cover estimates was approximately 80.3% in Abisko region and 76.7% in the Tibetan Plateau. The data gaps in our S-NPP VIIRS DNB snow cover estimates were smaller than those of the MODIS snow cover products by 22.1% and 5.1% over Abisko region and the Tibetan Plateau, respectively. Further, we found that nearly 92.8% and 74.6% of data gaps in the MODIS snow-cover product can be filled up by incorporating our S-NPP VIIRS DNB snow cover estimates in Abisko region and the Tibetan Plateau. The total accuracy of daily MODIS snow cover products can be improved to 91.0% in the Tibetan Plateau. Our results indicate that S-NPP VIIRS DNB nighttime satellite data can provide reliable snow products over polar regions and mid-latitude mountainous areas, which is complementary to the standard MODIS snow cover products.

Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America

Abstract. The dynamic characteristics of seasonal snow cover are critical for hydrology management, the climate system, and the ecosystem functions. Optical satellite remote sensing has proven to be an effective tool for monitoring global and regional variations in snow cover. However, accurately capturing the characteristics of snow dynamics at a finer spatiotemporal resolution continues to be problematic as observations from optical satellite sensors are greatly impacted by clouds and solar illumination. Traditional methods of mapping snow cover from passive microwave data only provide binary information at a spatial resolution of 25 km. This innovative study applies the random forest regression technique to enhanced-resolution passive microwave brightness temperature data (6.25 km) to estimate fractional snow cover over North America in winter months (January and February). Many influential factors, including land cover, topography, and location information, were incorporated into the retrieval models. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products between 2008 and 2017 were used to create the reference fractional snow cover data as the “true” observations in this study. Although overestimating and underestimating around two extreme values of fractional snow cover, the proposed retrieval algorithm outperformed the other three approaches (linear regression, artificial neural networks, and multivariate adaptive regression splines) using independent test data for all land cover classes with higher accuracy and no out-of-range estimated values. The method enabled the evaluation of the estimated fractional snow cover using independent datasets, in which the root mean square error of evaluation results ranged from 0.189 to 0.221. The snow cover detection capability of the proposed algorithm was validated using meteorological station observations with more than 310 000 records. We found that binary snow cover obtained from the estimated fractional snow cover was in good agreement with ground measurements (kappa: 0.67). There was significant improvement in the accuracy of snow cover identification using our algorithm; the overall accuracy increased by 18 % (from 0.71 to 0.84), and the omission error was reduced by 71 % (from 0.48 to 0.14) when the threshold of fractional snow cover was 0.3. The experimental results show that passive microwave brightness temperature data may potentially be used to estimate fractional snow cover directly in that this retrieval strategy offers a competitive advantage in snow cover detection.

Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions

Cloud obscuration is a major problem for using Moderate Resolution Imaging Spectroradiometer (MODIS) images in different applications. This issue poses serious difficulties in monitoring the snow cover in mountainous regions due to high cloudiness in such areas. To overcome this, different cloud removal methods have been developed in the past where most of them use MODIS snow cover products and spatiotemporal dependencies of snow to estimate the undercloud coverage. In this study, a new approach is adopted that uses surface reflectance data in the cloud-free pixels and estimates the surface reflectance of a cloudy pixel as if there were no cloud. This estimation is obtained by subsequently applying the k-nearest neighbor and dynamic time compositing methods. The modified surface reflectance data are then utilized as inputs of a Normalized Difference Snow Index (NDSI)-based algorithm to map snow cover in the study area. The results indicate that the suggested approach is able to appropriately estimate undercloud surface reflectance in bands 2, 4 and 6, and can map the snow cover with 97% accuracy, which is a substantial improvement over the conventional method with an accuracy of 86%. Finally, although a clear underestimation of snow cover (about 15%) is observed by applying the proposed approach, still, it is much better than the 30% underestimation obtained by the conventional method.

Application of snowmelt runoff model for water resource management

AbstractSnow‐covered areas (SCAs) are the fundamental source of water for the hydrological cycle for some region. Accurate measurements of river discharge from snowmelt can help manage much needed water required for hydropower generation and irrigation purposes. This study aims to apply the snowmelt runoff model (SRM) in the Upper Indus basin by the Astore River in northern Pakistan for the years 2000 to 2006. The Shuttle Radar Topographic Mission (SRTM) data are used to generate the Digital Elevation Model (DEM) of the region. Various variables (snow cover depletion curves (SCDCs), temperature and precipitation) and parameters (degree‐day factor, recession coefficient, runoff coefficients, time lag, critical temperature and temperature lapse rate) are used as input in the SRM. However, snow cover data are direct and an important input to the SRM. Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate the SCA. Normalized difference snow index (NDSI) algorithm is applied for snow cover mapping and to differentiate snow from other land features. Nash–Sutcliffe coefficient of determination (R2) and volume difference (DV) are used for quality assessment of the SRM. The results of the current research show that for the study years (2000–2006), the average value of R2 is 0·87 and average volume difference DV is 1·18%. The correlation coefficient between measured and computed runoff is 0·95. The results of the study further show that a high level of accuracy can be achieved during the snowmelt season. The simulation results endorse that the SRM in conjunction with MODIS snow cover product is very useful for water resource management in the Astore River and can be used for runoff forecasts in the Indus River basin in northern Pakistan. Copyright © 2011 John Wiley & Sons, Ltd.

A Comparison of AMSR-E/Aqua Snow Products with in situ Observations and MODIS Snow Cover Products in the Mackenzie River Basin, Canada

Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB.

Development and assessment of combined Terra and Aqua snow cover products in Colorado Plateau, USA and northern Xinjiang, China

This study presents our methods to produce multi-day Terra, Aqua, and Terra-Aqua moderate resolution imaging spectroradiometer (MODIS) snow cover composite images respectively from daily Terra MODIS, Aqua MODIS, and both Terra and Aqua MODIS snow cover products, with flexible starting and ending dates and a user-defined cloud cover threshold. The methods are applied to Colorado Plateau, USA and northern Xinjiang, China. The statistical comparison gives the following results. For the 2003-2004 hydrologic year, the daily Terra-Aqua composite images exhibited ~10-15% less on annual mean cloud cover and ~1-4% more on annual mean snow cover, compared with their daily Terra or Aqua counterparts. Using 10% cloud cover as a user-defined threshold, we produced 152 (northern Xinjiang) and 162 (Colorado Plateau) multi-day Terra-Aqua composite images for the 2003-2004 hydrological year, respectively. On average, it is 2.4 and 2.2 days per composite image for northern Xinjiang and Colorado Plateau, respectively. The multi-day Terra, Aqua, and Terra-Aqua composite products result in similar annual mean snow covers (~15% for the Colorado Plateau and ~30% for the northern Xinjiang), as those from the corresponding standard NASA 8-day products, ~3 times as those from the standard NASA daily products. The lower snow cover percentage retrieved from the daily standard products is mainly due to larger cloud cover in the daily products. The Aqua products always have lower annual mean snow cover and higher annual mean cloud cover than those of the Terra products. The daily Terra-Aqua composite products have higher agreement with ground measurements than either of the standard NASA daily Terra or Aqua products. The multi-day Terra-Aqua composite products have much higher agreement with ground measurements than that of the standard daily products and have similar agreement as that of the standard 8-day products. (partial Abstract)

Cloud removal methodology from MODIS snow cover product

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90–96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover.

Extending the Utility of Modis Daily Snow-Cover Products through Snow-Cover Prediction of Cloud-Obscured Areas in Idaho's Big Lost River Basin (USA)

ABSTRACT Increasing demand for water resources in the Western United States has given rise to many conflicts and has increased the need for more accurate and timely water management decisions. Snow-Covered Area (SCA) is an important hydrologic variable for both volumetric and daily stream flow forecasting. Satellite data provide the ideal way to map snow cover in mountain regions; however, the utility of these datasets have been limited due to the large number of scenes that are contaminated with cloud cover. To improve the utility of these datasets for the snowmelt season, in particular the Moderate Resolution Imaging Spectroradiometer (MODIS) Daily Snow-Cover Product dataset, a new procedure was developed to predict snow-cover in cloud-obscured areas using a snow-occurrence map technique. Results show that this method effectively increases the usefulness of the MODIS Snow-Cover product in mapping the daily evolution of snow cover extent in the Big Lost River Basin located in southeastern Idaho.

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

AbstractRemote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.

A prototype MODIS-SSM/I snow-mapping algorithm

Data in the wavelength range 0.545-1.652 w m from the Moderate Resolution Imaging Spectroradiometer (MODIS), launched aboard the Earth Observing System (EOS) Terra in December 1999, are used to map daily global snow cover at 500 m resolution. However, during darkness, or when the satellite's view of the surface is obscured by cloud, snow cover cannot be mapped using MODIS data. We show that during these conditions, it is possible to supplement the MODIS product by mapping the snow cover using passive microwave data from the Special Sensor Microwave Imager (SSM/I), albeit with much poorer resolution. For a 7-day time period in March 1999, a prototype MODIS snow-cover product was compared with a prototype MODIS-SSM/I product for the same area in the mid-western USA. The combined MODIS-SSM/I product mapped 9% more snow cover than the MODIS-only product.