This article is a part of the NASA DEVELOP’s Spring 2017 Article Session. For more articles like these, click here
Using NASA Earth observations and Google Earth Engine, NASA DEVELOP helps to provide National Park Service with a new tool to assess snow cover in Saguaro National Park.
Authors:
Tyler Lynn
Elaina Gonsoroski
Charles Barrow III
Saranee Dutta
Farnaz Bayat
Katie Harville

Image Caption A: A bedrock pool in Saguaro National Park with a lowland leopard frog inset. Image Credit: National Park Service
The bedrock pools and streams in the park and the surrounding sky islands are reliant on the accumulation of snow at higher elevations to help supply water during the dry season. Recently, park managers suspected there may be decreases in the amount of snow present compared to historic data and decreases in the amount of water present for supplying bedrock pools and park streams later into the dry season. However, a lack of synoptic, geospatial historical data has left park managers without a way to synoptically quantify the changes they are observing at specific field locations. Additionally, many of the locations where snow occurs are either remote or in rugged terrain, leaving park managers unable to field visit such locations in most instances. To better manage the water resources during the dry season, park managers look to fill a critical data gap on snow occurrence patterns by using satellite imagery to help determine the amounts of seasonal snow cover on a per area basis across the calendar year. With information on the amounts of snow cover for individual watersheds, park managers will be better equipped to manage water resources during the dry season.
Recently, NASA DEVELOP’s Mobile County Health Department team partnered with the National Park Service (NPS) in the Intermountain Region and the Saguaro National Park to help develop needed tools and data sets to assist park managers in assessing snow cover trends for individual watersheds. For the purposes of this study, the Upper Rincon Creek Watershed was chosen as it directly feeds Rincon Creek, which runs through Saguaro National Park (Image B). Landsat 5, 7, and 8 were chosen to observe snow cover due to their increased spatial resolution relative to coarser satellite imagery such as those from MODIS sensors. Landsat data was also chosen as the main data source for snow monitoring because it is available as early as 1984, allowing for historical snow cover trends to be observed and compared to current snow cover trends. With this set of satellites available, the NPS will be able to observe the historical seasonal trends of snow cover.

Image Caption B: Study area depicting boundaries of the Upper Rincon Creek watershed within Saguaro National Park. Image Credit: NASA DEVELOP
To assess the amount of snow cover, a combined snow index was used that utilized the Normalized Difference Snow Index (NDSI) [1] and the S3 [2] snow index. NDSI utilizes the green and shortwave infrared bands whereas the S3 index utilizes the red, near-infrared, and short-wave infrared bands. Both indices have particular snow classification errors. such as errors due to topographical shadow, cloud shadow, and vegetation.
The S3 index was particularly useful for classifying snow in topographic and cloud shadow as well as in vegetated areas such as those on the higher elevations of the mountains. Using both snow detection indices, thresholds for each index were identified by the team based on a literature review [3] to only classify snow that met the pre-determined thresholds in both the NDSI and S3 indices (Image C). The combined use of the two snow indices allowed lower snow detection thresholds compared to mapping snow with a singular index. This combined index approach improved the snow identification accuracy. After classifying snow cover in the region, the percentage of an entire watershed that was classified as snow was calculated and then graphed.
When comparing the seasonal snow cover to the seasonal stream discharge, the peaks of both appeared to have a relationship. The highest amount of stream discharge occurred at nearly the same time as the highest amount of snow cover in the watershed. The lag between when the peak stream discharge was observed and when the peak snow cover was observed may be due to the difference between when the Landsat image was taken. The peak amount of snow cover may have occurred before the Landsat image was taken and thus resulted in the peak observed snow cover occurring after the peak stream discharge was observed.

Image Caption C: Landsat image of the Rincon Mountains showing the calculated snow index (center) compared to a True Color RGB image (left) and a False Color RGB image (right). Image Credit: NASA DEVELOP
References
[1] D.K. Hall et al., “Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms,” in NASA GSFC September 2001.
[2] Y. Asaoka and Y. Kominami, “Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of gridded degree-day factor,” in Annals of Glaciology vol. 54(62), 2013, 205-213.
[3] H.S. Negi et al., “Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements,” in Journal of Earth System Science vol. 118(5), 2009, 525-538.
Author Biographies
Tyler Lynn is a recent graduate from Auburn University working with DEVELOP at the Mobile County Health Department node as an independent research consultant on the Southeastern Arizona Water Resources II project.
Elaina Gonsoroski is a recent graduate from the Ohio State University working with DEVELOP at the Mobile County Health Department node as an independent research consultant on the Southeastern Arizona Water Resources II project.
Charles Barrow III is a recent graduate from the University of South Alabama working with DEVELOP at the Mobile County Health Department node as an independent research consultant on the Southeastern Arizona Water Resources II project.
Saranee Dutta is a recent graduate from Mississippi State University working with DEVELOP at the Mobile County Health Department node as an independent research consultant on the Southeastern Arizona Water Resources II project.
Farnaz Bayat is a recent graduate from the University of South Alabama working with DEVELOP at the Mobile County Health Department node as an independent research consultant and worked on the Southeastern Arizona Water Resources project.
Katie Harville is a graduate from the University of South Alabama. Since working with DEVELOP at the Mobile County Health Department on the Southeastern Arizona Water Resources project, Katie has returned to her work with FEMA as an Environmental and Historic Preservation Specialist.