一、使用ArcMap提取出第一期数据
1.使用工具箱中的“Make NetCDF Raster Layer”工具，提取出一个数据
可以发现该数据有正确的像元大小、坐标系等
2.导出该数据作为标准数据
二、使用python批量提取所有数据
1. 查看数据属性
from netCDF4 import Dataset,num2date infile = "../01Data/Runoff1992-2014/GRUN_v1_GSWP3_WGS84_05_1902_2014.nc" data_set = Dataset(infile) # 读取nc文件信息 print(data_set)
输出为
<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_CLASSIC data model, file format NETCDF3):
title: GRUN
version: GRUN 1.0
meteorological_forcing: GSWP3
temporal_resolution: monthly
spatial_resolution: 0.5x0.5
crs: WGS84
proj4: +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
EPSG: 4326
references: Ghiggi et al.,2019. GRUN: An observation-based global gridded runoff dataset from 1902 to 2014. ESSD, doi: https://doi.org/10.5194/essd-2019-32
authors: Gionata Ghiggi; Lukas Gudmundsson
contacts: gionata.ghiggi@gmail.com; lukas.gudmundsson@env.ethz.ch
institution: Land-Climate Dynamics, Institute for Atmospheric and Climate Science, ETH Zrich
institution_id: IAC ETHZ
dimensions(sizes): X(720), Y(360), time(1356)
variables(dimensions): float64 X(X), float64 Y(Y), float64 time(time), float32 Runoff(time, Y, X)
groups:
可以看到variables变量X、Y为经纬度，time为时间，Runoff为需要的结果
2.批量导出结果
from osgeo import gdal
from netCDF4 import Dataset,num2date
import numpy as np
def WriteTiff(im_data,inputdir, path):
raster = gdal.Open(inputdir)
im_width = raster.RasterXSize #栅格矩阵的列数
im_height = raster.RasterYSize #栅格矩阵的行数
im_bands = raster.RasterCount #波段数
im_geotrans = raster.GetGeoTransform()#获取仿射矩阵信息
im_proj = raster.GetProjection()#获取投影信息
if 'int8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
elif 'int16' in im_data.dtype.name:
datatype = gdal.GDT_UInt16
else:
datatype = gdal.GDT_Float32
if len(im_data.shape) == 3:
im_bands, im_height, im_width = im_data.shape
elif len(im_data.shape) == 2:
im_data = np.array([im_data])
im_bands, (im_height, im_width) = 1, im_data.shape
# 创建文件
driver = gdal.GetDriverByName("GTiff")
dataset = driver.Create(path, im_width, im_height, im_bands, datatype)
if (dataset != None):
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
dataset.SetProjection(im_proj) # 写入投影
for i in range(im_bands):
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
del dataset
infile = "../01Data/Runoff1992-2014/GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"
data_set = Dataset(infile) # 读取nc文件信息
time = data_set.variables["time"][:] # 获取时间一列
units = data_set.variables["time"].units # 获取第一期时间
#读取样本tif文件的地理信息
intif = "../03ProcessData/runoff_example.tif"
for i in range(0,len(time)):
yr = num2date(time[i],units).year # 提取年份
mon = num2date(time[i],units).month # 提取月份
value_data = data_set.variables['Runoff'][i]
# 将缺失值改为0
data = value_data.data
mask = value_data.mask
data[np.where(mask == True)] = 0
outputname = "../01Data/Runoff1992-2014/tif/" + str(yr) + str(mon).zfill(2) + ".tif"
WriteTiff(data,intif , outputname)
print(outputname)
！注意事项
1.使用时候请自行修改修改输入输出文件路径与变量名称
2.根据需要处理缺失值
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原文链接：https://www.cnblogs.com/skypanxh/p/16034602.html
