ICESat-2（Ice,Cloud and Land Elevation Satellite 2）衛(wèi)星是NASA地球觀測系統(tǒng)的組成之一，其于2018年9月15號發(fā)射。主要是測量冰蓋高程、海冰的厚度以及陸地的地形。

其發(fā)布的數(shù)據(jù)產(chǎn)品有ATL02至ATL21等產(chǎn)品，平時需要根據(jù)不同的用途選擇對應的數(shù)據(jù)產(chǎn)品。主要的數(shù)據(jù)類型見下表：

下面我將讀取ATL08的數(shù)據(jù)，采用了python程序。

import icepyx as ipx
import numpy as np
import xarray as xr
import pandas as pd
import h5py
import os,json
from pprint import pprint
import dask.dataframe as dd
# put the full filepath to a data file here. You can get this in JupyterHub by navigating to the file,
# right clicking, and selecting copy path. Then you can paste the path in the quotes below.
fname = r'./dataset/ATL08_20210601183700_10531105_004_01.h5'
def read_atl08(fname, bbox=None):
? ?"""
? ?Read 1 ATL08 file and output 6 reduced files.

? ?Extract variables of interest and separate the ATL08 file
? ?into each beam (ground track) and ascending/descending orbits.
? ?"""
? ?# Each beam is a group
? ?group = ['/gt1l', '/gt1r', '/gt2l', '/gt2r', '/gt3l', '/gt3r']
? ?# Loop trough beams
? ?for k,g in enumerate(group):
? ? ? ?#-----------------------------------#
? ? ? ?# 1) Read in data for a single beam #
? ? ? ?#-----------------------------------#
? ? ? ?# Load variables into memory (more can be added!)
? ? ? ?with h5py.File(fname, 'r') as fi:
? ? ? ? ? ?lat = fi[g+'/land_segments/latitude'][:]
? ? ? ? ? ?lon = fi[g+'/land_segments/longitude'][:]
? ? ? ? ? ?canopy_h_metrics = fi[g+'/land_segments/canopy/canopy_h_metrics'][:]
? ? ? ? ? ?canopy_openness = fi[g+'/land_segments/canopy/canopy_openness'][:]
? ? ? ? ? ?h_canopy_quad = fi[g+'/land_segments/canopy/h_canopy_quad'][:]
? ? ? ?#---------------------------------------------#
? ? ? ?# 2) Filter data according region and quality #
? ? ? ?#---------------------------------------------#
? ? ? ?# Select a region of interest
? ? ? ?if bbox:
? ? ? ? ? ?lonmin, lonmax, latmin, latmax = bbox
? ? ? ? ? ?bbox_mask = (lon >= lonmin) & (lon <= lonmax) & \
? ? ? ? ? ? ? ? ? ? ? ?(lat >= latmin) & (lat <= latmax)
? ? ? ?else:
? ? ? ? ? ?bbox_mask = np.ones_like(lat, dtype=bool) ?# get all
? ? ? ?# Test for no data
? ? ? ?if len(canopy_h_metrics) == 0: continue
? ? ? ?#-----------------------#
? ? ? ?# 4) Save selected data #
? ? ? ?#-----------------------#
? ? ? ?# Define output file name
? ? ? ?ofile = fname.replace('.h5', '_'+g[1:]+'.h5')
? ? ? ?# Save variables
? ? ? ?with h5py.File(ofile, 'w') as f:
? ? ? ? ? ?f['lon'] = lon
? ? ? ? ? ?f['lat'] = lat
? ? ? ? ? ?f['canopy_h_metrics'] = canopy_h_metrics
? ? ? ? ? ?f['canopy_openness'] = canopy_openness
? ? ? ? ? ?f['h_canopy_quad'] = h_canopy_quad
? ? ? ? ? ?print('out ->', ofile)
? ? ? ?# save as csv
? ? ? ?ofilecsv = fname.replace('.h5', '_'+g[1:]+'.csv')
? ? ? ?result = pd.DataFrame()
? ? ? ?result['lon'] = lon
? ? ? ?result['lat'] = lat
? ? ? ?result['canopy_h_metrics_0'] = canopy_h_metrics[:,0]
? ? ? ?result['canopy_h_metrics_1'] = canopy_h_metrics[:,1]
? ? ? ?result['canopy_h_metrics_2'] = canopy_h_metrics[:,2]
? ? ? ?result['canopy_h_metrics_3'] = canopy_h_metrics[:,3]
? ? ? ?result['canopy_h_metrics_4'] = canopy_h_metrics[:,4]
? ? ? ?result['canopy_h_metrics_5'] = canopy_h_metrics[:,5]
? ? ? ?result['canopy_h_metrics_6'] = canopy_h_metrics[:,6]
? ? ? ?result['canopy_h_metrics_7'] = canopy_h_metrics[:,7]
? ? ? ?result['canopy_h_metrics_8'] = canopy_h_metrics[:,8]
? ? ? ?result['canopy_openness'] = canopy_openness
? ? ? ?result['h_canopy_quad'] = h_canopy_quad

? ? ? ?print('out ->', ofilecsv)
? ? ? ?result.to_csv(ofilecsv,index=None)
read_atl08(fname, None)

程序運行的結(jié)果將會以CSV的格式導出。

接著在arcgis中展示數(shù)據(jù)的空間分布:

另外分享一個網(wǎng)址，大家可以找到不同語言編寫的數(shù)據(jù)讀取代碼，可以自己下載源程序調(diào)試讀取；

https://hdfeos.org/zoo/index_openICESat2_Examples.php#ATL

下圖是使用其中的代碼繪制的效果

如有不對的地方，歡迎與我交流！