对GEDI雷达数据预处理的代码

由于GEDI雷达数据处理涉及到多个步骤，以下提供了一些常用的预处理代码：

1. 解压GEDI数据文件

import h5py

# 读取GEDI数据文件
with h5py.File('GEDI01_B_2019321195108_O01754_T04276_02_003_01.h5', 'r') as f:
    # 获取数据集
    dataset = f['BEAM0000']['geolocation']['latitude_bin0']
    # 读取数据
    data = dataset[:]

2. 选择感兴趣的轨道和波束

import h5py

# 读取GEDI数据文件
with h5py.File('GEDI01_B_2019321195108_O01754_T04276_02_003_01.h5', 'r') as f:
    # 获取数据集
    dataset = f['BEAM0000']['geolocation']['latitude_bin0']
    # 选择感兴趣的轨道
    track = 4276
    data = dataset[track]
    # 选择感兴趣的波束
    beam = 'BEAM0000'
    data = f[beam]['geolocation']['latitude_bin0'][track]

3. 滤波和降采样

import numpy as np
import scipy.signal

# 滤波器参数
fs = 20  # 采样频率
f_cutoff = 0.1  # 截止频率
b, a = scipy.signal.butter(4, f_cutoff/(fs/2), 'lowpass')

# 读取GEDI数据文件
with h5py.File('GEDI01_B_2019321195108_O01754_T04276_02_003_01.h5', 'r') as f:
    # 获取数据集
    dataset = f['BEAM0000']['geolocation']['latitude_bin0']
    # 选择感兴趣的轨道和波束
    track = 4276
    data = f['BEAM0000']['geolocation']['latitude_bin0'][track]
    # 滤波
    data_filtered = scipy.signal.filtfilt(b, a, data)
    # 降采样
    data_downsampled = scipy.signal.resample(data_filtered, len(data_filtered)//2)

4. 校正时间偏移

import h5py
import numpy as np

# 读取GEDI数据文件
with h5py.File('GEDI01_B_2019321195108_O01754_T04276_02_003_01.h5', 'r') as f:
    # 获取数据集
    dataset = f['BEAM0000']['geolocation']['latitude_bin0']
    # 选择感兴趣的轨道和波束
    track = 4276
    data = f['BEAM0000']['geolocation']['latitude_bin0'][track]
    # 获取时间偏移量
    t_ref = f['/ancillary_data/gps_epoch'][()]
    t_poe = f['/ancillary_data/profile_time'][:]
    t_ref = np.array([t_ref]*len(t_poe))
    t_offset = f['/ancillary_data/delta_time'][:]
    # 校正时间偏移
    t_corrected = t_ref + t_poe + t_offset