5 research outputs found

    Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

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    Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring

    Evaluation of Machine Learning Algorithms for Lake Ice Classification from Optical Remote Sensing Data

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    The topic of lake ice cover mapping from satellite remote sensing data has gained interest in recent years since it allows the extent of lake ice and the dynamics of ice phenology over large areas to be monitored. Mapping lake ice extent can record the loss of the perennial ice cover for lakes located in the High Arctic. Moreover, ice phenology dates, retrieved from lake ice maps, are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. However, existing knowledge-driven (threshold-based) retrieval algorithms for lake ice-water classification that use top-of-the-atmosphere (TOA) reflectance products do not perform well under the condition of large solar zenith angles, resulting in low TOA reflectance. Machine learning (ML) techniques have received considerable attention in the remote sensing field for the past several decades, but they have not yet been applied in lake ice classification from optical remote sensing imagery. Therefore, this research has evaluated the capability of ML classifiers to enhance lake ice mapping using multispectral optical remote sensing data (MODIS L1B (TOA) product). Chapter 3, the main manuscript of this thesis, presents an investigation of four ML classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) in lake ice classification. Results are reported using 17 lakes located in the Northern Hemisphere, which represent different characteristics regarding area, altitude, freezing frequency, and ice cover duration. According to the overall accuracy assessment using a random k-fold cross-validation (k = 100), all ML classifiers were able to produce classification accuracies above 94%, and RF and GBT provided above 98% classification accuracies. Moreover, the RF and GBT algorithms provided a more visually accurate depiction of lake ice cover under challenging conditions (i.e., high solar zenith angles, black ice, and thin cloud cover). The two tree-based classifiers were found to provide the most robust spatial transferability over the 17 lakes and performed consistently well across three ice seasons, better than the other classifiers. Moreover, RF was insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate that RF and GBT provide a great potential to map accurately lake ice cover globally over a long time-series. Additionally, a case study applying a convolution neural network (CNN) model for ice classification in Great Slave Lake, Canada is presented in Appendix A. Eighteen images acquired during the the ice season of 2009-2010 were used in this study. The proposed CNN produced a 98.03% accuracy with the testing dataset; however, the accuracy dropped to 90.13% using an independent (out-of-sample) validation dataset. Results show the powerful learning performance of the proposed CNN with the testing data accuracy obtained. At the same time, the accuracy reduction of the validation dataset indicates the overfitting behavior of the proposed model. A follow-up investigation would be needed to improve its performance. This thesis investigated the capability of ML algorithms (both pixel-based and spatial-based) in lake ice classification from the MODIS L1B product. Overall, ML techniques showed promising performances for lake ice cover mapping from the optical remote sensing data. The tree-based classifiers (pixel-based) exhibited the potential to produce accurate lake ice classification at a large-scale over long time-series. In addition, more work would be of benefit for improving the application of CNN in lake ice cover mapping from optical remote sensing imagery

    Lower Atmosphere Meteorology

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    The Atmosphere Special Issue “Lower Atmosphere Meteorology” deals with the meteorological processes that occur in the layer of the atmosphere close to the surface. The interaction between the biosphere and the atmosphere is made through the lower layer and can greatly influence living beings and materials. The analysis of the meteorological parameters provides a better understanding of processes within the lower atmosphere and involved in air pollution, climate, and weather. The mixed layer height, the wind speed, and the air parcel trajectory have a relevant interest due to their marked impact on population and energy production. The research also comprises aerosols, clouds, and precipitation, analysing their spatiotemporal variations. This issue addresses features of gases in the atmosphere and anthropogenic greenhouse emission estimates, which are also conditioned by the lower atmosphere meteorology

    The First Global Integrated Marine Assessment: World Ocean Assessment I

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    We used satellite-derived sea-surface-temperature (SST) data along with in-situ data collected along a meridional transect between 18.85 and 20.25°N along 69.2°E to describe the evolution of an SST filament and front during 25 November to 1 December in the northeastern Arabian Sea (NEAS). Both features were &#8764; 100 km long, lasted about a week and were associated with weak temperature gradients (&#8764; 0.07°C km<sup>−1</sup>). The in-situ data were collected first using a suite of surface sensors during a north–south mapping of this transect and showed the existence of a chlorophyll maximum within the filament. This surface data acquisition was followed by a high-resolution south–north CTD (conductivity–temperature–depth) sampling along the transect. In the two days that elapsed between the two in-situ measurements, the filament had shrunk in size and moved northward. In general, the current direction was northwestward and advected these mesoscale features. The CTD data also showed an SST front towards the northern end of the transect. In both these features, the chlorophyll concentration was higher than in the surrounding waters. The temperature and salinity data from the CTD suggest upward mixing or pumping of water from the base of the mixed layer, where a chlorophyll maximum was present, into the mixed layer that was about 60 m thick. A striking diurnal cycle was evident in the chlorophyll concentration, with higher values tending to occur closer to the surface during the night. The in-situ data from both surface sensors and CTD, and so also satellite-derived chlorophyll data, showed higher chlorophyll concentration, particularly at sub-surface levels, between the filament and the front, but there was no corresponding signature in the temperature and salinity data. Analysis of the SST fronts in the satellite data shows that fronts weaker than those associated with the filament and the front had crossed the transect in this region a day or two preceding the sampling of the front
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