PCA Leads

Principal component analysis of Arctic Sea ice using multi-spectral Sentinel-2 and RADAR Sentinel-1 data

Jullian C.B. Williams *†, Kiran Bhaganagar ††

† Department of Earth and Planetary Sciences, Center for Advanced Measurements in Extreme Environments (CAMEE), The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, Texas 78249

†† Department of Mechanical Engineering, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, Texas 78249

corresponding author: [email protected]

Summary

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulate heat balances in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of a principal component analysis (PCA) is one such tool that is used to identify the characteristics of seasonal ice variability. The PCA not only identifies “normal” or “dominant” conditions or features, but also extract anomalous spatial features from an archive of long-term sequence of images.

Introduction

Leads are described as linear kinematic features found on sea ice surfaces. The formation of leads is akin to the process of plate tectonics, where shifting ice sheets diverge, converge and slide against each other, forming fissures in the ice. Leads are important to arctic habitats and wildlife (Stirling, 1997), polar navigation (Lasserre, 2015) and global climate (Hutter, 2018). Leads are important regulators of heat in the Arctic, with open water and thin ice leads providing the first and second largest heat fluxes into the atmosphere (approximately 600 W.m-2 and 5 W.m-2 respectively, (Maykut 1982 and Li, 2019)). Radar satellite imagery is used to observe and quantify the changes in lead fractions across the ice sheet. Radar data is particularly useful in quantifying lead fractions since the instrument can make observations in Polar regions regardless of weather conditions or time of year (Lindsay & Rothrock, 1995), (Rohrs & Kaleschke, 2012), (Brohan & Kalesche, 2014), (Willmes & Heinemann, 2015). The Sentinel-1 A/B instrument is a powerful, high resolution radar satellite imager that has been used for Polar observations since its launch in 2014 and 2016 (A and B respectively). The two-satellite constellation provides a 6-day exact repeat cycle, 12-day cross-over and a repeat frequency of less than 1 day in the Arctic (Lohse, 2020). Sentinel-1 has unprecedented nominal spatial resolution of ~40 m, which allows small scale leads on the ice surface to be captured.
Google Earth Engine (GEE) hosts a large repository of satellite data, including the entire Sentinel catalog. GEE has been used in several research arenas, from vegetation and land use mapping (Gorelick et al., 2017 and Tsai, et al., 2018) to wetland and snow melt detection (Gulacsi, et al., 2020 and Liang et., al, 2021). Indeed, GEE and Sentinel-2 are proven powerhouses in the use of remote sensing techniques and climate observations in cloud computation environments. Currently, there are no studies published focusing on the detection of leads in the Arctic using this platform which provide a solution to the common big data problem of data storage and computation power. This study provides a principal component technique to sea ice classification in the GEE cloud computation environment using Sentinel-2 optical data. The Beaufort Gyre (Figure 1), once described as an ice production area has shown a decrease in productivity with increased climate forcing over the last decade (Armitage, 2020). This motivates our investigation of sea ice lead prevalence during the lifetime of the Sentinel 1 & 2 instruments. The aim of this study is to classify sea ice open water and thin ice leads in the offshore Alaskan region in the Beaufort Gyre to assess the changes potential heat flux over the ice sheet.

Datasets

Sentinel-2 multi-spectral optical data: https://developers.google.com/earth-engine/datasets/catalog/sentinel-2
Sentinel-1 RADAR data: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD

Methods

Google Colab Python environment

Alongside the use of GEE for data retrieval, preparation and classification, we opt to use Google Colab, a cloud computation python environment. Indeed, Google Colab (GC) is an exceptional tool for the use of superior python functions in the cloud, minimizing the pressure of data storage while simultaneously enhancing processing power through parallelization. GC allows the user to mount their Google Drive storage point to the environment, allowing easy data imports and exports of results.

Python Modules

from datetime import datetime
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.cbook
from mpl_toolkits.basemap import Basemapimport random
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import stats
import matplotlib.image as mpimg
from sklearn.metrics import r2_score
from sklearn.decomposition import PCA
import warnings

Principal Component Analysis

The PCA method is such that the first component will always contain the most variation found in the original data. This is referred to as the “integrated brightness” and provides a good index of the general image characteristics. In the case of sea ice data, the image scenes may be water or ice dominant. The second component then highlights the major differences between the image channels. In fact, it is suggested that studies using multitemporal monthly data over an annual cycle display a second component that tracks the seasonal evolution well (Piwowar, et al., 1996). Principal components three and onward usually represent increasingly local variation and anomalies. Therefore, we propose that it is within these variations that the open water and thin ice leads may be characterized.

Plotting Techniques

The results of the principal component analysis yield mean and anomalous digital signals from the false RGB color composite images. From this, we can visualize these parameters graphically in Arctic map plots as well as quantitatively in scatter plots to display the orthogonal components. The mean and standard deviations will give a margin of error of the spectral boundaries of the lead types versus the ice ridges in the image scenes which can be both spectrally and geometrically similar.

References

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Bröhan, D., Kaleschke, L., & Notz, D. (2014). Analysis of Arctic sea-ice leads from Advanced Microwave Scanning Radiometer.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing Of Environment, 202, 18-27. doi: 10.1016/j.rse.2017.06.031.
Gulácsi, A., and Kovács, F. (2020). Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine. Remote Sensing, 12(10), 1614. doi: 10.3390/rs12101614.
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Stirling, I. (1997). The importance of polynyas, ice edges, and leads to marine mammals and birds. Journal Of Marine Systems, 10(1-4), 9-21. doi: 10.1016/s0924-7963(96)00054-1.
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Williams, J. C.; Ackley, S. F.; Mestas-Nuñez, A. M.; Macdonald, G. J. Lead Detection with Sentinel-1 in the Beaufort Gyre using Google Earth Engine. Preprints 2024, 2024050284. https://doi.org/10.20944/preprints202405.0284.v1
Willmes, S. and Heinemann, G. (2015). Sea-Ice Wintertime Lead Frequencies and Regional Characteristics in the Arctic, 2003–2015. Remote Sensing, 8(1), 4. doi: 10.3390/rs8010004.

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