Sanggyun Lee

Landfast sea ice monitoring using multisensor fusion in the Antarctic

Landfast sea ice (fast ice) means sea ice that is attached to the shoreline with little or no motion in contrast to pack ice which drifts on the sea. As fast ice plays an important role in the environmental and biological systems of the Antarctic, it is crucial to accurately monitor the spatiotemporal distribution of fast ice. Previous studies on fast ice using satellite remote sensing were mostly focused on the Arctic and near-Arctic areas, whereas few studies were conducted over the Antarctic, especially the West Antarctic region. This research mapped fast ice using multisensor data from 2003 to 2008 based on machine learning approaches – decision trees (DTs) and random forest (RF). A total of seven satellite-derived products, including Advanced Microwave Scanning Radiometer for the Earth observing system brightness temperatures and sea ice concentration, Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) and Special Sensor Microwave/Imager ice velocity, were used as input variables for identifying fast ice. RF resulted in better performance than that of DT for fast ice classification. Visual comparison of the fast ice classification results with 250-m MODIS images for selected areas also revealed that RF outperformed DT. Ice velocity and IST were identified as the most contributing variables to classify fast ice. Spatiotemporal variations of fast ice in the East and West Antarctic were also examined using the time series of the fast ice maps produced by RF. The residence time of fast ice was much shorter in the West Antarctic than in the East.

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches—decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15–75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30–45 min in advance using COMS MI data.

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014.

CO2 concentration and its spatiotemporal variation in the troposphere using multi-sensor satellite data, carbon tracker, and aircraft observations

Satellite-based atmospheric CO2 observations have provided a great opportunity to improve our understanding of the global carbon cycle. However, thermal infrared (TIR)-based satellite observations, which are useful for the investigation of vertical distribution and the transport of CO2, have not yet been studied as much as the column amount products derived from shortwave infrared data. In this study, TIR-based satellite CO2 products – from Atmospheric Infrared Sounder, Tropospheric Emission Spectrometer (TES), and Thermal And Near infrared Sensor for carbon Observation – and carbon tracker mole fraction data were compared with in situ Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) data for different locations. The TES CO2 product showed the best agreement with CONTRAIL CO2 data resulting in R2 ~ 0.87 and root-mean-square error ~0.9. The vertical distribution of CO2 derived by TES strongly depends on the geophysical characteristics of an area. Two different climate regions (i.e., southeastern Japan and southeastern Australia) were examined in terms of the vertical distribution and transport of CO2. Results show that while vertical distribution of CO2 around southeastern Japan was mainly controlled by horizontal and vertical winds, horizontal wind might be a major factor to control the CO2 transport around southeastern Australia. In addition, the vertical transport of CO2 also varies by region, which is mainly controlled by anthropogenic CO2, and horizontal and omega winds. This study improves our understanding of vertical distribution and the transport of CO2, both of which vary by region, using TIR-based satellite CO2 observations and meteorological variables.

The spatiotemporal variations of CO2 in the troposphere using multi-sensor satellite data and aircraft observation

This study investigated the ability of thermal infrared satellite-based products to document the vertical distribution of atmospheric CO2 concentration. CO2 concentration products derived from three satellite sensors including Atmospheric Infrared Sounder (AIRS), Tropospheric Emission Spectrometer (TES) and Thermal And Near infrared Sensor for carbon Observation (TANSO) were validated with aircraft observation from Comprehensive Observation Network for Trace gases by AIrLiners (CONTRAIL) over three different areas (i.e., around Japan, Indochina peninsula, and southeastern Australia). CONTRAIL has measured vertical profiles of CO2 concentration with air flask measurements by recording CO2 concentration every 10 seconds when an aircraft ascends or descends. Results showed that TES CO2 vertical concentration had the best agreement with the CONTRAIL measurements, followed by AIRS and TANSO. Spatiotemporal patterns of TES CO2 vertical concentration were also examined over the three areas. The patterns varied by region, implying that the different environmental characteristics such as land phenology, distribution of geographical features, atmospheric conditions, and wind patterns can affect the distinct patterns.