UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo

S. Skiles, Christopher P. Donahue, A. Hunsaker, J. Jacobs
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引用次数: 2

Abstract

Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discrete in situ observations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).
Landsat 9积雪粒度和反照率多尺度评估的无人机高光谱成像
雪反照率是衡量雪表面反射的太阳辐射量的一种指标,它在地球气候和区域水文中起着至关重要的作用,因为它是雪融化时间的主要驱动因素。卫星多光谱遥感提供了几十年的陆地表面反射率记录,从中可以检索到积雪反照率。然而,评估这一观测记录具有挑战性,因为离散的原位观测不太适合在卫星空间分辨率(几十到几百米)下验证雪的特性。例如,雪颗粒大小是雪反照率的主要驱动因素,在亚米尺度上由于坡向、海拔和植被的变化而变化。在这里,我们提出了一种新的无人驾驶飞行器高光谱成像(UAV-HSI)方法,用于绘制高分辨率(20厘米)的雪表面特性。在科罗拉多州参议员贝克盆地沼泽天使研究地块周围的草地上,一架近红外HSI在大疆matrix 600 Pro上飞行。利用辐射传输正演模拟方法,根据测量的地表反射率生成有效的雪粒度和反照率图。使用一致的地面观测资料进行验证;相对于野外光谱仪反演的平均晶粒尺寸差为2 μm, RMSE为12 μm,平均宽带反照率与飞行区中心附近测量值的差值在1%以内。尽管雪表面在视觉上是同质的,但地图显示了新降雪的空间变异性和连贯模式。为了证明无人机- hsi在改进卫星检索验证方面的潜力,在飞行两天后,在类似条件下,使用高分辨率地图评估来自卫星立交桥的17个Landsat 9 OLI像素的粒度和反照率检索以及亚像素变异性。虽然Landsat 9没有捕获与UAV-HSI相同的值范围和空间变异性,但平均而言,比较结果显示出良好的一致性,平均晶粒尺寸差异为9 μm,宽带反照率相同(86%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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