Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2

Abstract

During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance.

Evaluation of the MODIS (C6) Daily Albedo Products for Livingston Island, Antarctic

“…This robust statistical method, which is usually applied to high-dispersion datasets, in addition to showing the trend in the long-term data, allows the effects of outliers to be minimized. LOWESS has multiple applications, including noise reduction in satellite-derived measurements [68]. The method is based on the adjustment of points from a polynomial regression in which the closest points have the greatest weight in the estimation of the regression.…”

“…As mentioned in Section 2.1, the study area is characterized by abundant cloud cover. It is known that the physical properties of clouds, e.g., water content, are factors that can also influence cloud albedo [68]. In these cases, when the MODIS cloud mask fails, the products could be assuming that cloud albedo values are snow albedo.…”

Variability of Basal Meltwater Generation During Winter, Western Greenland Ice Sheet

“…The velocity products have intrinsic errors associated with the measurements and methodologies, as well as inconsistences in availability of the primary satellite data. The errors tend to be more problematic when considering finer detail and shorter time intervals (Derkacheva et al, 2020). Limitations of the velocity product thus dictate that our MEaSUREs data analysis is restricted to the 2017-2018 winter, deemed to have the best quality (Joughin et al, 2018b).…”

Spatiotemporal Variability of Dune Velocities and Corresponding Uncertainties, Detected from Optical Image Matching in the North Sinai Sand Sea, Egypt

“…The combination of individual velocities (hereafter called fusion) gained importance due to the availability of the free archives (e.g., Landsat 4/5/7/8, and Sentinel-2) with high temporal resolutions. The fusion of individual velocities is uncommon in dune studies; however, it was previously employed extensively in glacier studies (e.g., [37,[42][43][44][45]). Dehecq et al [42] introduced large-scale velocities for glaciers in the Pamir-Karakoram-Himalayas for the period 1999-2001, where they processed 1536 pairs generated from 1382 images belonging to 68 Landsat 5/7 frames and employed a spatiotemporal median to introduce the final velocity.…”