Abstract
We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI -based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth < 30 cm) the mean NDSI -0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.
Highlights
A notable issue in optical remote sensing of snow cover is the problem of snow detection in spatially heterogeneous landscapes at the end of the melting season when a signal can be affected by snow-covered ground, snow-free ground and tree canopy (Dietz et al, 2012; Frei et al, 2012; Vikhamar and Solberg, 2003b; Xin et al, 2012)
AISA –derived quantities under direct illumination from various land cover types at Sodankylä. Their related indices, and other quantities described in Section 2.4 of snow-covered and snow-free ground for Sodankylä site characterized by patchy thin]0, 30] cm snow layer under direct illumination
We found that standard deviation for Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) are only slightly sensitive to Canopy Cover (CC) when the ground was covered by a dry thick snow layer, while for thin melting snow the variance is in general clearly higher and in addition, more sensitive to CC (Fig. 5)
Summary
A notable issue in optical remote sensing of snow cover is the problem of snow detection in spatially heterogeneous landscapes at the end of the melting season when a signal can be affected by snow-covered ground, snow-free ground and tree canopy (Dietz et al, 2012; Frei et al, 2012; Vikhamar and Solberg, 2003b; Xin et al, 2012). In some investigations the scene reflectance from boreal landscape during full snow cover has been successfully modelled, e.g. by Vikhamar and Solberg (2003a), Niemi et al (2012) and Pulliainen et al (2014). Both the modelling experiments and experimental data analysis lack the consideration of thin and dirty snow layers as well as the high variability of snow depth from snow-free ground patches to thicker snow packs, which is typical to boreal forests. In this work we tackle these questions by investigating the variability of the melting snow and snow-free ground reflectance with focus on the boreal forest zone with detailed information on the Dataset/ Measurement Cases
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