Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions

Abstract

Cloud obscuration is a major problem for using Moderate Resolution Imaging Spectroradiometer (MODIS) images in different applications. This issue poses serious difficulties in monitoring the snow cover in mountainous regions due to high cloudiness in such areas. To overcome this, different cloud removal methods have been developed in the past where most of them use MODIS snow cover products and spatiotemporal dependencies of snow to estimate the undercloud coverage. In this study, a new approach is adopted that uses surface reflectance data in the cloud-free pixels and estimates the surface reflectance of a cloudy pixel as if there were no cloud. This estimation is obtained by subsequently applying the k-nearest neighbor and dynamic time compositing methods. The modified surface reflectance data are then utilized as inputs of a Normalized Difference Snow Index (NDSI)-based algorithm to map snow cover in the study area. The results indicate that the suggested approach is able to appropriately estimate undercloud surface reflectance in bands 2, 4 and 6, and can map the snow cover with 97% accuracy, which is a substantial improvement over the conventional method with an accuracy of 86%. Finally, although a clear underestimation of snow cover (about 15%) is observed by applying the proposed approach, still, it is much better than the 30% underestimation obtained by the conventional method.

Regional influence of ocean–atmosphere teleconnections on the timing and duration of MODIS-derived snow cover in British Columbia, Canada

“…Stronger more coherent events may occur when the PDO phase and ENSO phase coincide (Gershunov and Barnett, 1998). Similarly to the ONI, we define PDO events of 5 consecutive months with values greater than 0.5 • C as positive (warm) and below −0.5 • C as negative (cool) phases (Kiem et al, 2003).…”

Author Response to RC2

“…Other studies have proposed methods of removing clouds from MODIS snow cover data. Spatiotemporal filtering that uses a temporal probability of snow and a DEM (Li et al, 2017), and a spatial k-means interpolation with dynamic time warping (Khoramian and Dariane, 2017) are some of the innovative methods being developed in the rapidly evolving field. Our focus was to find a method that easily detects SDON and SDOFF, and the LOWESS does that quickly, efficiently and accurately.…”