将 HDF 数据读取到 3d 数组并在 python 中另存为数据帧

Question

I am currently working on the NASA aerosol optical depth data (MCD19A2), which is a NASA satellite level three product. I have uploaded the data . I want to save the data as a dataframe including all the information of longitude and latitude, and values. I have successfully converted the 0.47um band file into a three-dimensional array. I want to ask how to convert this array into a correct dataframe includes X, Y and the value.

Below are the codes I have tried:

from osgeo import gdal
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

rds = gdal.Open("MCD19A2.A2006001.h26v04.006.2018036214627.hdf")
names=rds.GetSubDatasets()
names[0][0]
*'HDF4_EOS:EOS_GRID:"MCD19A2.A2006001.h26v04.006.2018036214627.hdf":grid1km:Optical_Depth_047'*
aod_047 = gdal.Open(names[0][0])
a47=aod_047.ReadAsArray()
a47[1].shape
(1200,1200)

I would like the result to be like

X (n=1200)	Y (n=1200)	AOD_047
8896067	5559289	0.0123

I know that in R this can be done by

require('gdalUtils')
require('raster')
require('rgdal')


file.name<-"MCD19A2.A2006001.h26v04.006.2018036214627.hdf"
sds <- get_subdatasets(file.name)
gdal_translate(sds[1], dst_dataset = paste0('tmp047', basename(file.name), '.tiff'), b = nband) 
r.047 <- raster(paste0('tmp047', basename(file.name), '.tiff'))
df.047 <- raster::as.data.frame(r.047, xy = T)
names(df.047)[3] <- 'AOD_047'

But, R really relies on memory and saving to 'tif' and reading 'tif' is using a lot of memory. So I want to do this task in python. Thanks a lot for your help.

Answer 1

You can use pandas :

import pandas as pd
df=pd.read_hdf('filename.hdf')