Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping. Issue 6 (1st June 2020)

• Record Type: Journal Article
• Authors: Rittger, Karl; Raleigh, Mark S.; Dozier, Jeff; Hill, Alice F.; Lutz, James A.; Painter, Thomas H.
• Is Part Of: Water resources research. Volume 56:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-01
• Subjects: snow cover -- MODIS -- spectral mixture analysis -- canopy adjustment -- cloud detection -- SWE; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR024914
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Maps of snow cover serve as early indicators for hydrologic forecasts and as inputs to hydrologic models that inform water management strategies. Advances in snow cover mapping have led to increasing accuracy, but unsatisfactory treatment of vegetation's interference when mapping snow has led to maps that have limited utility for water forecasting. Vegetation affects snow mapping because ground surfaces not visible to the satellite produce uncertainty as to whether the ground is snow covered. At nadir, the forest canopy obscures the satellite view below the canopy. At oblique viewing angles, the forest floor is obscured by both the canopy and the projection of tree profiles onto the forest floor. We present a canopy correction method based on Moderate Resolution Imaging Spectroradiometer satellite imagery validated with field observations that mitigates geometric and georegistration issues associated with changing satellite acquisition angles in forested areas. The largest effect from a variable viewing zenith angle on the viewable gap fraction in forested areas occurs in moderately forested areas with 30–40% tree canopy coverage. Cloud cover frequently causes errors in snow identification, with some clouds identified as snow and some snow identified as cloud. A snow‐cloud identification method utilizes a time series of fractional vegetation and rock land‐surface data to flag snow‐cloud identification errors and improve snow‐map accuracy reducing bias by 20% over previous methods. Together, these contributions to snow‐mapping techniques could advance hydrologic forecasting in forested, snow‐dominated basins that comprise an estimated one fifth of Northern Hemisphere snow‐covered areas. Plain Language Summary: Mapping snow cover extent informs water resources stored in the winter snowpack, providing important information for planning how and when to utilize available water. Tree canopies shield the forest floor and hinder the determination of snow‐covered area in vegetated terrain from above, where satellites view the Earth's surface. Moreover, satellites that scan the surface (like the Moderate Resolution Imaging Spectroradiometer) are most often not directly overhead, and as the view from the satellite to the surface slants, trees obscure a greater portion of the land surface that a satellite can "see, " especially where trees are tall, adding uncertainty to the satellite‐based maps. This effect is greatest in forests where 30–40% of the land area is covered by tree canopy and has little effect in very dense forests where most of the forest floor is obscured already. Here we present a method to accommodate the stretched and hidden forest footprint when a satellite is not directly overhead. A new method for identifying snow‐mapping errors caused by clouds is also presented by flagging unusually large changes in the non‐snow surfaces over sequential images. These methodological advancements are important because snow‐melt water comprises a major portion of the water supply in many regions where humans live. Key Points: The canopy correction methodology improves remotely sensed snow mapping accuracy in forested environments, reducing bias by 20% A novel cloud detection algorithm that uses fractional vegetation and rock coverage can reduce snow‐cloud identification errors Improved performance over previous snow mapping approaches enable melt out date identification within days of actual snow disappearance