Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Normalized-Difference Snow Index (NDSI)

The Normalized-Difference Snow Index (NDSI) has a long history. 'The use of ratioing visible (VIS) and near-infrared (NIR) or short-wave infrared (SWIR) channels to separate snow and clouds was documented in the literature beginning in the mid-1970s. A considerable amount of work on this subject was conducted at, and published by, the Air Force Geophysics Laboratory (AFGL). The objective of the AFGL work was to discriminate snow cover from cloud cover using an automated algorithm to improve global cloud analyses. Later, automated methods that relied on the VIS/NIR ratio were refined substantially using satellite data In this section we provide a brief history of the use of the NDSI for mapping snow cover

Comparison of MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

The VIIRS (Visible Infrared Imaging Radiometer Suite) on board the Suomi-NPP (National Polar-orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument carried aboard Aqua and Terra satellites. There are speculations concerning differences between MODIS and VIIRS snow cover products because of different spatial resolution and spectral coverage. However, the quantitative comparisons between VIIRS and MODIS snow products are currently limited. Consequently, this study intercompares MODIS and VIIRS snow products during the 2016 hydrological year. To accomplish its research objectives, 244 swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (VSCMO/binary) were intercompared for the 2016 hydrological year from October 1, 2015 to May 31, 2016 using confusion matrices, comparison maps and false color imagery. The current VIIRS snow product is binary, therefore to produce MODIS binary snow maps, the MODIS snow cover fraction threshold value of 30% was determined by examining snow cover area at four different thresholds (20%, 30%, 40% and 50%) and comparing them with the VIIRS binary snow map. Overall VIIRS appears to map more snow and less clouds than MODIS. On average, MODIS snow maps mapped snow but VIIRS in 1% of cloud free pixels, whereas 2% of the time VIIRS mapped snow but MODIS did not. The average agreement between MODIS and VIIRS was approximately 98% indicating good agreement between them. Agreement between MODIS and VIIRS was high during the winter but lower during late fall and spring, mostly over dense forest. Both MODIS and VIIRS often mapped snow/no-snow transition zones as cloud. The visual comparison depicts good qualitative agreement between snow cover area visible in MODIS and VIIRS false color imagery and mapped in their respective snow cover products

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent

Fractional snow cover in the Colorado and Rio Grande basins, 1995-2002

A cloud-masked fractional snow-covered area (SCA) product gridded at 1 km was developed from the advanced very high resolution radiometer for the Colorado River and upper Rio Grande basins for 1995-2002. Cloud cover limited SCA retrievals on any given 1-km2 pixel to on average once per week. There were sufficient cloud-free scenes to map SCA over at least part of the basins up to 21 days per month, with 3 months having only two scenes sufficiently cloud free to process. In the upper Colorado and upper Grande, SCA peaked in February-March. Maxima were 1-2 months earlier in the lower Colorado. Averaged over a month, as much as 32% of the upper Colorado and 5.5% of the lower Colorado were snow covered. Snow cover persisted longest at higher elevations for both wet and dry years. Interannual variability in snow cover persistence reflected wet-dry year differences. Compared with an operational (binary) SCA product produced by the National Operational Hydrologic Remote Sensing Center, the current products classify a lower fraction of pixels as having detectable snow and being cloud covered (5.5% for SCA and 6% for cloud), with greatest differences in January and June in complex, forested terrain. This satellite-derived subpixel determination of snow cover provides the potential for enhanced hydrologic forecast abilities in areas of complex, snow-dominated terrain. As an example, we merged the SCA product with interpolated ground-based snow water equivalent (SWE) to develop a SWE time series. This interpolated, masked SWE peaked in April, after SCA peaked and after some of the lower-elevation snow cover had melted. Copyright 2008 by the American Geophysical Union

Snow Accumulation and Melt Timing at High Elevations in Northwest Montana

The sensitivity of snowmelt driven water supply to climate variability and change is difficult to assess in the mountain west, where strong climatic gradients coupled with complex topography are sampled by sparse ground measurements. We developed a snowmelt model, which ingests daily satellite imagery and meteorological data and is suitable for application to areas greater than 1000 km2, yet captures important spatial variability in steep mountain terrain. We applied the model to the Middle Fork of the Flathead Basin, a 2900 km2 snowmelt-dominated watershed in northwest Montana. Time integration of the melt model yielded a history of snow water equivalent distribution for the years 2000-2008. We found that over 25% of the total annual snow falls above the highest measurement station in the basin, and over 70% falls above the mean elevation of the nine nearest SNOTEL stations. Furthermore, elevation lapse rates in snow water equivalent are variable from year-to-year and are not described by the poorly distributed ground measurements. Consequently, scaling point measurements of snow water equivalent to describe basin conditions leads to significant misrepresentation. Numerical melt simulations performed on the basin’s peak snow accumulation elucidated the control of temperature variability on snowmelt timing under modern climate and future climate projected by downscaled GCMs. Natural temperature variability affects snowmelt timing on the order of 4 weeks, and plays an even larger role in a warmer climate. Timing of melt in a large snowpack year was found to be more susceptible to natural temperature variability than in a small snowpack year. On average, snowmelt timing occurs 24 days earlier in our projected future climate, but the range of variability is such that an overlap of today’s conditions occurs as often as 50% of the time