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acdc

arcticseaice

Sea ice parameters

arcticseaice2016budgets

Python data processing and plotting scripts from Petty et al., (2018), published in The Cryosphere.

arcticseaiceprediction2017

Data processing and plotting scripts from Petty et al., 2017, JGR

bachelor-discrimmination-of-sea-ice-types-with-satellite-altimetry---filtration-and-classifcation

MATLAB code which extracts AltiKa data to discriminate, filtrate and classify sea ice types within areas in the Polar Circle (North Pole).

batch_process_icesat_h5data

These scripts are used to obtain the projected shapefiles from the original ICESat .H5 files in a batch process.

bbgs_elmer

Elmer/Ice code for the Bering-Bagley Glacier System Model

beringseaice2018

Historical sea ice in the Bering Sea

brown_retracker

Brown retracker c++ implementation

caathickness

Canadian Arctic Archipelago - sea ice thickness

captoolkit

NASA’s Cryosphere Altimetry Processing Toolkit

chasingseaice

Python code to correct IceBridge flight path to account for ice drift when underflying ICESat-2

climate-science-analysis

Various climate science analysis and plots: Sea Surface Temperature, Winds, Sea Ice, NAO, Empirical Orthogonal Functions, etc

cmseaice

Colormaps for visualizing sea ice thickness (and other sea ice parameters)

cosipy

Coupled snowpack and ice surface energy and mass balance model in Python

cryo-toolbox

Public repo for Python modules which may be useful for parsing/plotting sea ice data (e.g. from NSIDC, seismic traces)

dominance-analysis

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

fine-tuning-a-pre-trained-cnn-for-first-year-sea-ice-and-multi-year-sea-ice-cp-imagery-classificatio

Mapping first-year sea ice and multi-year sea ice in the oceans is significant for many applications. For example, ship navigation and weather forecast. Accurate and robust classification methods of multi-year ice and first-year ice are in demand [2]. Hybrid-polarity SAR architecture will be included in future SAR missions such as the Canadian RADARSAT Constellation Mission (RCM). These sensors will enable the use of compact polarimetry (CP) data in wide swath imagery [1]. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, I pursue this method for the classification of image patches from compact polarimety (CP) imagery into first-year ice and multi-year ice is applicable. In this course project, my work is kind of like the first practice of the CP imagery classification by fine-tuning a pre-trained convolutional neural network (CNN). Specifically, fine-tuning the last fully-connected layer of a pre-trained convolutional networks, I extract patches from simulated CP images as my dataset, the classification accuracy of the test set achieved 91.3% by fine-tuning a pre-trained CNN, compared to 49.4% classification accuracy by training from scratch.

gbm

Repository for the global mass project, WP4, land ice

gee_tools

Google Earth Engine Tools (scripts)

glace

FORTRAN and C++ tools for processing and analysis of ICESat data

ground2float

Identifying grounding zones from ICESat-2

hosts

:statue_of_liberty:最新可用的google hosts文件。国内镜像：

icesat-2-offsea-data-processing

icesat-2-sea-ice-thickness

Generating sea ice thickness estimates from NASA's ICESat-2 freeboard data

icesat-2hackweek-seaice

ICESat-2 hack week repository for those in the sea ice team

icesat-e2settings

Enigma2 Settings for 13.0E - 19.2E - 23.5E - 28.2E

icesat2

Scripts for processing and viewing ICESat-2 data

icesat2-1

Matlab code for working with ICESat2 data

icesat2-2