Optical satellite-derived waterline time series at 8 beaches using various indexes and thresholding methods

Satellite-derived waterlines, each file is associated to a site and a thresholding method and contains the waterline time series derived from 5 different indexes (refer to the associated paper) Example of code : import pickle import matplotlib.pyplot as plt index = 'NDWI' #NDWI can be switched to SCoWI, MNDWI, AWEIsh, AWEIns indexes. data = pickle.load(open('path/to/file.p','rb')) key = 'TRANSECT NAME' #data.keys() to see transects names waterline_dict = data[key][index]['raw'] #raw refers to raw waterline, IQR refers to raw data without outliers, and tcorr the tide corrected data. t,X = waterline_dict['sat_dates'], waterline_dict['SDW_'+index] plt.plot(t,X) plt.ylabel('Cross-shore waterline position [m]') plt.xlabel('Time')卫星反演水边线数据集：每个文件对应一个观测站点与一种阈值提取方法，包含基于5种不同水体指数提取的水边线时间序列（详见关联论文）。 代码示例： import pickle import matplotlib.pyplot as plt index = 'NDWI' # 归一化差分水体指数（NDWI）可替换为SCoWI、MNDWI、AWEIsh、AWEIns等指数。 data = pickle.load(open('path/to/file.p','rb')) # 利用pickle模块加载指定路径的二进制数据文件 key = 'TRANSECT NAME' # 可通过data.keys()查看所有断面名称 waterline_dict = data[key][index]['raw'] # raw指代原始水边线数据，IQR指代剔除异常值后的原始数据集，tcorr指代经潮汐校正后的数据集。 t,X = waterline_dict['sat_dates'], waterline_dict['SDW_'+index] plt.plot(t,X) plt.ylabel('Cross-shore waterline position [m]') # 设置纵轴标签为'跨岸水边线位置 [m]' plt.xlabel('Time') # 设置横轴标签为'时间'