Data for: The Annual Cycle of Landfast Ice in the Eastern Bering Sea

Large quantities of data were used for this study. Due to the 10gb file limit of Mendely Data, we can only include instructions on how to acquire the SAR imagery, with R and python scripts to complete the remaining steps. The data is uploaded as a compressed file that unzips into a folder. Within the folder there are two subdirectories: “SAR_Imagery” and “Gradient_Analysis”.

The SAR_Imagery subdirectory includes python scripts to initiate downloads of Radarsat-1 imagery covering geographic regions within our study area. The download is accomplished via the Alaska Satellite Facility Data Portal; the creation of an account will be required to download this imagery.

The Gradient_Analysis subdirectory includes two folders: “Dates” and “Scripts”. The “Dates” folder includes CSV files listing the three dates used in each “triplet”. These CSV files are needed for the execution of the scripts contained in the “Scripts” folder. To run these scripts, you need R-Studio, a free and open-sourced IDE for the R programming language. Step1_Subfolders.R loops through the dates contained within the CSV files in the “Dates Folders”, and deposits the three SAR images in a new subdirectory. Step2_Gradient.R then loops through these subdirectories and performs the net-gradient-difference analysis needed to procure imagery for identifying landfast ice.

本研究采用了大量数据。鉴于Mendely数据平台的10GB文件限制，我们仅能提供获取合成孔径雷达（SAR）影像的指导，以及使用R和Python脚本完成后续步骤的方法。数据以压缩文件形式上传，解压后生成一个文件夹。文件夹内包含两个子目录：“SAR_Imagery”和“Gradient_Analysis”。在“SAR_Imagery”子目录中，包含用于启动覆盖研究区域内地理区域的Radarsat-1影像下载的Python脚本；下载通过阿拉斯加卫星设施数据门户完成；下载此影像需要创建账户。在“Gradient_Analysis”子目录中，包含两个文件夹：“Dates”和“Scripts”。“Dates”文件夹中包含CSV文件，列出了每个“三元组”中使用的三个日期。这些CSV文件是执行“Scripts”文件夹中脚本所必需的。要运行这些脚本，需要R-Studio，这是为R编程语言提供的一个免费且开源的集成开发环境。Step1_Subfolders.R脚本将遍历“Dates Folders”中的CSV文件中的日期，并将三个SAR影像存放在一个新的子目录中。随后，Step2_Gradient.R脚本将遍历这些子目录，执行获取用于识别固定冰的影像所需的净梯度差分析。