将GTiff栅格的时间序列堆栈转换为单个NetCDF

12

从gdal-dev邮件列表移出：

在2013年9月2日星期一晚上7：09，David Shean写道：

嗨，大家好，我正在尝试将具有相同投影/范围/分辨率的GTiff栅格时间序列打包为单个NetCDF文件进行分发。我花了一个小时来咨询在线文档，并且使用gdal_translate，gdalbuildvrt和gdalwarp都没有成功。

有使用现有的gdal命令行实用程序执行此操作的简单方法吗？在使用NetCDF Python API求助于自定义解决方案之前，我想过要问一下。

谢谢。-大卫

在2013年9月3日，星期二，上午10：15，Etienne Tourigny写道：

您想要的可能超出了gdal的范围。它将需要一些聪明的元数据管理，以便gdal_translate将它们放在一个文件中...

我建议您使用gdal_translate将它们全部转换为netcdf，然后使用python-netcdf4（而不是numpy / scipy中的那个）将它们堆叠在时间维度上。

2013年9月3日，星期二，上午7:55，“ Signell，Richard”写道：

大卫，如果您将问题发布到GIS stackexchange组 /gis//上， 我将提供示例代码，该代码应该会有所帮助。

-丰富

====================

PDT更新9/3/13 17:04

这是我的输入数据集之一的gdalinfo输出：


gdalinfo 20120901T2024_align_x+22.19_y+3.68_z+14.97_warp.tif

Driver: GTiff/GeoTIFF
Files: 20120901T2024_align_x+22.19_y+3.68_z+14.97_warp.tif
Size is 10666, 13387
Coordinate System is:
PROJCS["unnamed",
    GEOGCS["WGS 84",
        DATUM["WGS_1984",
            SPHEROID["WGS 84",6378137,298.257223563,
                AUTHORITY["EPSG","7030"]],
            AUTHORITY["EPSG","6326"]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433],
        AUTHORITY["EPSG","4326"]],
    PROJECTION["Polar_Stereographic"],
    PARAMETER["latitude_of_origin",70],
    PARAMETER["central_meridian",-45],
    PARAMETER["scale_factor",1],
    PARAMETER["false_easting",0],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]]]
Origin = (-211346.063781524338992,-2245136.291794800199568)
Pixel Size = (5.000000000000000,-5.000000000000000)
Metadata:
  AREA_OR_POINT=Area
Image Structure Metadata:
  COMPRESSION=LZW
  INTERLEAVE=BAND
Corner Coordinates:
Upper Left  ( -211346.064,-2245136.292) ( 50d22'39.70"W, 69d23'55.59"N)
Lower Left  ( -211346.064,-2312071.292) ( 50d13'22.38"W, 68d48'10.75"N)
Upper Right ( -158016.064,-2245136.292) ( 49d 1'33.33"W, 69d26'16.42"N)
Lower Right ( -158016.064,-2312071.292) ( 48d54'35.06"W, 68d50'27.28"N)
Center      ( -184681.064,-2278603.792) ( 49d38' 1.32"W, 69d 7'17.04"N)
Band 1 Block=256x256 Type=Float32, ColorInterp=Gray
  NoData Value=-32767

遵循卢克的建议方法。

vrt生成可以正常工作：

gdalbuildvrt -separate newtest.vrt *warp.tif

<VRTDataset rasterXSize="10666" rasterYSize="13387">
  <SRS>PROJCS["unnamed",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]]]</SRS>
  <GeoTransform> -2.1134606378152434e+05,  5.0000000000000000e+00,  0.0000000000000000e+00, -2.2451362917948002e+06,  0.0000000000000000e+00, -5.0000000000000000e+00</GeoTransform>
  <VRTRasterBand dataType="Float32" band="1">
    <NoDataValue>-3.27670000000000E+04</NoDataValue>
    <ComplexSource>
      <SourceFilename relativeToVRT="1">20110619T2024_align_x+15.51_y+1.15_z+12.10_warp.tif</SourceFilename>
      <SourceBand>1</SourceBand>
      <SourceProperties RasterXSize="10666" RasterYSize="13387" DataType="Float32" BlockXSize="256" BlockYSize="256" />
      <SrcRect xOff="0" yOff="0" xSize="10666" ySize="13387" />
      <DstRect xOff="0" yOff="0" xSize="10666" ySize="13387" />
      <NODATA>-32767</NODATA>
    </ComplexSource>
  </VRTRasterBand>
  <VRTRasterBand dataType="Float32" band="2">
    <NoDataValue>-3.27670000000000E+04</NoDataValue>
    <ComplexSource>
      <SourceFilename relativeToVRT="1">20110802T2024_align_x+16.33_y+2.14_z+12.02_warp.tif</SourceFilename>
      <SourceBand>1</SourceBand>
      <SourceProperties RasterXSize="10666" RasterYSize="13387" DataType="Float32" BlockXSize="256" BlockYSize="256" />
      <SrcRect xOff="0" yOff="0" xSize="10666" ySize="13387" />
      <DstRect xOff="0" yOff="0" xSize="10666" ySize="13387" />
      <NODATA>-32767</NODATA>
    </ComplexSource>
  </VRTRasterBand>
...

但是，当我尝试转换为nc时，出现以下错误：


gdal_translate -of netcdf newtest.vrt newtest.nc

Input file size is 10666, 13387
Warning 1: Variable has 0 dimension(s) - not supported.
0...10...20...30...40...50ERROR 1: netcdf error #-62 : NetCDF: One or more variable sizes violate format constraints .
at (netcdfdataset.cpp,SetDefineMode,1574)

ERROR 1: netcdf error #-39 : NetCDF: Operation not allowed in define mode .
at (netcdfdataset.cpp,IWriteBlock,1435)

ERROR 1: netCDF scanline write failed: NetCDF: Operation not allowed in define mode
ERROR 1: An error occured while writing a dirty block
...ERROR 1: netcdf error #-39 : NetCDF: Operation not allowed in define mode .
at (netcdfdataset.cpp,IWriteBlock,1435)

ERROR 1: netCDF scanline write failed: NetCDF: Operation not allowed in define mode
ERROR 1: netcdf error #-62 : NetCDF: One or more variable sizes violate format constraints .
at (netcdfdataset.cpp,~netCDFDataset,1548)

因此，仔细检查后，看来gdal对我使用的极地立体投影不满意（EPSG：3413）。请参阅netcdfdataset.cpp的第1570-1582行：

https://code.vpac.org/gitorious/gdal-netcdf-testing/gdal-netcdf-driver/blobs/8fa3582669969ad4d55e461f5846b3ed33727f63/gdal/frmts/netcdf/netcdfdataset.cpp

我的投影指定了latitude_of_origin，但没有netcdf驱动程序期望的标准并行。

python  gdal  netcdf 
— 大卫·谢恩
source
1
什么GDAL版本？在GDAL> = 1.9.0中，NetCDF驱动程序进行了许多更改。该页面专门提到对极地立体投影处理的更改。您可以通过使用gdal_translate -a_srs参数覆盖投影并指定有效但等效的投影字符串来解决此问题。另请参见（trac.osgeo.org/gdal/wiki/NetCDF_ProjectionTestingStatus）
— user2856
gdalinfo --version GDAL 1.11dev，2013
— David Shean
1
感谢Rich和Luke的有用帮助。我需要更新到最新的GDAL版本，评估最新的netcdf驱动程序Polar立体图像功能，并就任何挥之不去的问题跟进gdal-dev。虽然这两个答案都可以使用，但我喜欢Rich的食谱，并且会按照自己的目的采用。我知道其他人会发现此讨论很有用-很高兴将其存档在SE中。
— David Shean 2013年

Answers:

22

这是一些python代码，可以满足您的需要，读取表示特定时间数据的GDAL文件，并写入符合CF的单个NetCDF文件

#!/usr/bin/env python
'''
Convert a bunch of GDAL readable grids to a NetCDF Time Series.
Here we read a bunch of files that have names like:
/usgs/data0/prism/1890-1899/us_tmin_1895.01
/usgs/data0/prism/1890-1899/us_tmin_1895.02
...
/usgs/data0/prism/1890-1899/us_tmin_1895.12
'''

import numpy as np
import datetime as dt
import os
import gdal
import netCDF4
import re

ds = gdal.Open('/usgs/data0/prism/1890-1899/us_tmin_1895.01')
a = ds.ReadAsArray()
nlat,nlon = np.shape(a)

b = ds.GetGeoTransform() #bbox, interval
lon = np.arange(nlon)*b[1]+b[0]
lat = np.arange(nlat)*b[5]+b[3]


basedate = dt.datetime(1858,11,17,0,0,0)

# create NetCDF file
nco = netCDF4.Dataset('time_series.nc','w',clobber=True)

# chunking is optional, but can improve access a lot: 
# (see: http://www.unidata.ucar.edu/blogs/developer/entry/chunking_data_choosing_shapes)
chunk_lon=16
chunk_lat=16
chunk_time=12

# create dimensions, variables and attributes:
nco.createDimension('lon',nlon)
nco.createDimension('lat',nlat)
nco.createDimension('time',None)
timeo = nco.createVariable('time','f4',('time'))
timeo.units = 'days since 1858-11-17 00:00:00'
timeo.standard_name = 'time'

lono = nco.createVariable('lon','f4',('lon'))
lono.units = 'degrees_east'
lono.standard_name = 'longitude'

lato = nco.createVariable('lat','f4',('lat'))
lato.units = 'degrees_north'
lato.standard_name = 'latitude'

# create container variable for CRS: lon/lat WGS84 datum
crso = nco.createVariable('crs','i4')
csro.long_name = 'Lon/Lat Coords in WGS84'
crso.grid_mapping_name='latitude_longitude'
crso.longitude_of_prime_meridian = 0.0
crso.semi_major_axis = 6378137.0
crso.inverse_flattening = 298.257223563

# create short integer variable for temperature data, with chunking
tmno = nco.createVariable('tmn', 'i2',  ('time', 'lat', 'lon'), 
   zlib=True,chunksizes=[chunk_time,chunk_lat,chunk_lon],fill_value=-9999)
tmno.units = 'degC'
tmno.scale_factor = 0.01
tmno.add_offset = 0.00
tmno.long_name = 'minimum monthly temperature'
tmno.standard_name = 'air_temperature'
tmno.grid_mapping = 'crs'
tmno.set_auto_maskandscale(False)

nco.Conventions='CF-1.6'

#write lon,lat
lono[:]=lon
lato[:]=lat

pat = re.compile('us_tmin_[0-9]{4}\.[0-9]{2}')
itime=0

#step through data, writing time and data to NetCDF
for root, dirs, files in os.walk('/usgs/data0/prism/1890-1899/'):
    dirs.sort()
    files.sort()
    for f in files:
        if re.match(pat,f):
            # read the time values by parsing the filename
            year=int(f[8:12])
            mon=int(f[13:15])
            date=dt.datetime(year,mon,1,0,0,0)
            print(date)
            dtime=(date-basedate).total_seconds()/86400.
            timeo[itime]=dtime
           # min temp
            tmn_path = os.path.join(root,f)
            print(tmn_path)
            tmn=gdal.Open(tmn_path)
            a=tmn.ReadAsArray()  #data
            tmno[itime,:,:]=a
            itime=itime+1

nco.close()

GDAL和NetCDF4 Python可能很难构建，但好消息是它们是大多数科学python发行版（Python（x，y），Enthought Python发行版，Anaconda等的一部分）。

更新： 我尚未在兼容CF的NetCDF中完成极地立体摄影，但我应该看起来像这样。在这里，我认为central_meridian和latitude_of_origin在GDAL是相同的straight_vertical_longitude_from_pole，并latitude_of_projection_origin在CF：

#!/usr/bin/env python
'''
Convert a bunch of GDAL readable grids to a NetCDF Time Series.
Here we read a bunch of files that have names like:
/usgs/data0/prism/1890-1899/us_tmin_1895.01
/usgs/data0/prism/1890-1899/us_tmin_1895.02
...
/usgs/data0/prism/1890-1899/us_tmin_1895.12
'''

import numpy as np
import datetime as dt
import os
import gdal
import netCDF4
import re

ds = gdal.Open('/usgs/data0/prism/1890-1899/us_tmin_1895.01')
a = ds.ReadAsArray()
ny,nx = np.shape(a)

b = ds.GetGeoTransform() #bbox, interval
x = np.arange(nx)*b[1]+b[0]
y = np.arange(ny)*b[5]+b[3]


basedate = dt.datetime(1858,11,17,0,0,0)

# create NetCDF file
nco = netCDF4.Dataset('time_series.nc','w',clobber=True)

# chunking is optional, but can improve access a lot: 
# (see: http://www.unidata.ucar.edu/blogs/developer/entry/chunking_data_choosing_shapes)
chunk_x=16
chunk_y=16
chunk_time=12

# create dimensions, variables and attributes:
nco.createDimension('x',nx)
nco.createDimension('y',ny)
nco.createDimension('time',None)
timeo = nco.createVariable('time','f4',('time'))
timeo.units = 'days since 1858-11-17 00:00:00'
timeo.standard_name = 'time'

xo = nco.createVariable('x','f4',('x'))
xo.units = 'm'
xo.standard_name = 'projection_x_coordinate'

yo = nco.createVariable('y','f4',('y'))
yo.units = 'm'
yo.standard_name = 'projection_y_coordinate'

# create container variable for CRS: x/y WGS84 datum
crso = nco.createVariable('crs','i4')
crso.grid_mapping_name='polar_stereographic'
crso.straight_vertical_longitude_from_pole = -45.
crso.latitude_of_projection_origin = 70.
crso.scale_factor_at_projection_origin = 1.0
crso.false_easting = 0.0
crso.false_northing = 0.0
crso.semi_major_axis = 6378137.0
crso.inverse_flattening = 298.257223563

# create short integer variable for temperature data, with chunking
tmno = nco.createVariable('tmn', 'i2',  ('time', 'y', 'x'), 
   zlib=True,chunksizes=[chunk_time,chunk_y,chunk_x],fill_value=-9999)
tmno.units = 'degC'
tmno.scale_factor = 0.01
tmno.add_offset = 0.00
tmno.long_name = 'minimum monthly temperature'
tmno.standard_name = 'air_temperature'
tmno.grid_mapping = 'crs'
tmno.set_auto_maskandscale(False)

nco.Conventions='CF-1.6'

#write x,y
xo[:]=x
yo[:]=y

pat = re.compile('us_tmin_[0-9]{4}\.[0-9]{2}')
itime=0

#step through data, writing time and data to NetCDF
for root, dirs, files in os.walk('/usgs/data0/prism/1890-1899/'):
    dirs.sort()
    files.sort()
    for f in files:
        if re.match(pat,f):
            # read the time values by parsing the filename
            year=int(f[8:12])
            mon=int(f[13:15])
            date=dt.datetime(year,mon,1,0,0,0)
            print(date)
            dtime=(date-basedate).total_seconds()/86400.
            timeo[itime]=dtime
           # min temp
            tmn_path = os.path.join(root,f)
            print(tmn_path)
            tmn=gdal.Open(tmn_path)
            a=tmn.ReadAsArray()  #data
            tmno[itime,:,:]=a
            itime=itime+1

nco.close()
— 里奇·西涅尔
source
伟大的代码丰富！这是非常有用的，以后我会用到。看起来您的输入投影被假设为纬度/经度单位（EPSG：4326）。我正在使用极地纬度上的高分辨率数据，因此这不是理想的选择，但是我将尝试转换为WGS84。
— David Shean 2013年
lat / lon只是一个例子。您可以随意使用。您定位什么应用程序？ArcGIS，仅用于归档还是什么？
— Rich Signell
好吧，我有许多这样的时间序列，我正在评估有效存储和分析的选项。但此刻，我正在打包数据以供流模型提取。建模社区（至少是冰流建模）似乎喜欢netcdf。
— 大卫·谢恩
有没有可以找到这些数据样本的URL？
— Rich Signell 2013年
不幸的是，我目前无法分发，但是将来有计划存档。
— David Shean 2013年
2

可以使用GDAL实用程序将它们放在单个NetCDF中，如下所示。但是您没有得到@RichSignell的答案的时间维度/其他元数据。tiff只是被转储到子数据集中。

C:\remotesensing\testdata>dir /b ndvi*.tif
ndvi1.tif
ndvi2.tif
ndvi3.tif

C:\remotesensing\testdata>gdalbuildvrt -separate ndvi.vrt ndvi*.tif
0...10...20...30...40...50...60...70...80...90...100 - done.

C:\remotesensing\testdata>gdal_translate -of netcdf ndvi.vrt ndvi.nc
Input file size is 96, 88
0...10...20...30...40...50...60...70...80...90...100 - done.

C:\remotesensing\testdata>gdalinfo ndvi.nc
Driver: netCDF/Network Common Data Format
Files: ndvi.nc
Size is 512, 512
Coordinate System is `'
Metadata:
  NC_GLOBAL#Conventions=CF-1.5
  NC_GLOBAL#GDAL=GDAL 1.10.0, released 2013/04/24
  NC_GLOBAL#history=Wed Sep 04 09:49:11 2013: GDAL CreateCopy( ndvi.nc, ... )
Subdatasets:
  SUBDATASET_1_NAME=NETCDF:"ndvi.nc":Band1
  SUBDATASET_1_DESC=[88x96] Band1 (32-bit floating-point)
  SUBDATASET_2_NAME=NETCDF:"ndvi.nc":Band2
  SUBDATASET_2_DESC=[88x96] Band2 (32-bit floating-point)
  SUBDATASET_3_NAME=NETCDF:"ndvi.nc":Band3
  SUBDATASET_3_DESC=[88x96] Band3 (32-bit floating-point)
Corner Coordinates:
Upper Left  (    0.0,    0.0)
Lower Left  (    0.0,  512.0)
Upper Right (  512.0,    0.0)
Lower Right (  512.0,  512.0)
Center      (  256.0,  256.0)

C:\remotesensing\testdata>gdalinfo NETCDF:"ndvi.nc":Band1
Driver: netCDF/Network Common Data Format
Files: ndvi.nc
Size is 96, 88
Coordinate System is:
GEOGCS["GCS_GDA_1994",
    DATUM["Geocentric_Datum_of_Australia_1994",
        SPHEROID["GRS 1980",6378137,298.2572221010002,
            AUTHORITY["EPSG","7019"]],
        AUTHORITY["EPSG","6283"]],
    PRIMEM["Greenwich",0],
    UNIT["degree",0.0174532925199433]]
Origin = (115.810500000000000,-32.260249999999999)
Pixel Size = (0.000250000000000,-0.000250000000000)
Metadata:
  Band1#_FillValue=0
  Band1#grid_mapping=crs
  Band1#long_name=GDAL Band Number 1
  crs#GeoTransform=115.8105 0.00025 0 -32.26025 0 -0.00025
  crs#grid_mapping_name=latitude_longitude
  crs#inverse_flattening=298.2572221010002
  crs#longitude_of_prime_meridian=0
  crs#semi_major_axis=6378137
  crs#spatial_ref=GEOGCS["GCS_GDA_1994",DATUM["Geocentric_Datum_of_Australia_1994",SPHEROID["GRS 1980",6378137,298.2572221010002,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6283"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]
  lat#long_name=latitude
  lat#standard_name=latitude
  lat#units=degrees_north
  lon#long_name=longitude
  lon#standard_name=longitude
  lon#units=degrees_east
  NC_GLOBAL#Conventions=CF-1.5
  NC_GLOBAL#GDAL=GDAL 1.10.0, released 2013/04/24
  NC_GLOBAL#history=Wed Sep 04 09:49:11 2013: GDAL CreateCopy( ndvi.nc, ... )
Corner Coordinates:
Upper Left  ( 115.8105000, -32.2602500) (115d48'37.80"E, 32d15'36.90"S)
Lower Left  ( 115.8105000, -32.2822500) (115d48'37.80"E, 32d16'56.10"S)
Upper Right ( 115.8345000, -32.2602500) (115d50' 4.20"E, 32d15'36.90"S)
Lower Right ( 115.8345000, -32.2822500) (115d50' 4.20"E, 32d16'56.10"S)
Center      ( 115.8225000, -32.2712500) (115d49'21.00"E, 32d16'16.50"S)
Band 1 Block=96x1 Type=Float32, ColorInterp=Undefined
  NoData Value=0
  Metadata:
    _FillValue=0
    grid_mapping=crs
    long_name=GDAL Band Number 1
    NETCDF_VARNAME=Band1
— 用户名
source
我尝试了这种方法，但是对于我的输入数据却失败了-我将在上面发布输出。
— David Shean
作为测试，我使用gdalwarp将EPSG：3413多频带vrt重新投影到EPSG：4326，然后使用gdal_translate转换为netcdf4。正如卢克所言，这毫无问题。正如Etienne在原始gdal-dev线程中所建议的那样，对于这种方法对元数据的控制有限。
— David Shean 2013年
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