Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

• Published in: International Journal of Applied Earth Observation and Geoinformation
• Main Authors: Heinilä, Anna Maaria Kirsikka, Salminen, Miia, Metsämäki, Sari, Pellikka, Petri Kauko Emil, Koponen, Sampsa, Pulliainen, Jouni
• Other Authors: Helsinki Institute of Sustainability Science (HELSUS), Department of Geosciences and Geography, Earth Change Observation Laboratory (ECHOLAB)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Scientific Publ. Co 2019
• Subjects: 1171 Geosciences; 114 Physical sciences; Reflectance; AISA; Spectroscopy; Scene reflectance; Snow melt; NDSI; NDVI; Boreal forest; Land cover classification; Fell; Snow mapping; FSC; SCE; MODIS; GRAIN-SIZE; FOREST CANOPY; COVERED AREA; SPECTRAL ALBEDO; SPECTROMETER; VEGETATION; ACCURACY; PRODUCTS; FRACTION; Northern Finland
• Online Access: http://hdl.handle.net/10138/298713

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. Peer reviewed