def main(ifile, geo_file, path_out):
'''
@description: AQUA/MODIS SeaDAS大气校正
@ifile {str} L1B文件
@geo_file {str} 地理定位文件
@path_out {str} 输出路径
@return:
'''
date_str = os.path.split(ifile)[1].split('.')[1]
date_str = date_teanslator.jd_to_cale(date_str[1:])
time_str = os.path.split(ifile)[1].split('.')[2]
year = int(date_str.split('.')[0])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
hour = int(time_str[0:2])
minute = int(time_str[2:])
date_str = '%d%02d%02d%02d%02d%02d' % (year, month, day, hour + 8, minute,
0)
nrow = os.path.split(ifile)[1].split('.')[3]
out_name = 'TERRA_MODIS_1000_L2_%s_%s_00.hdf' % (date_str, nrow)
raster_fn_out = os.path.join(path_out, out_name)
cmd = 'l2gen ifile=%s geofile=%s cloud_thresh=0.05 ofile=%s' % \
(ifile, geo_file, raster_fn_out)
os.system(cmd)
return (raster_fn_out)
def main(ifile,
shp_file,
center_lonlat,
salz,
sala,
satellite,
aerotype=1,
altitude=0.01,
visibility=15,
band_need=['all'],
path_out_radi=None,
path_out_6s=None):
'''
@description: 主程序
@ifile {str}: L1B swath文件
@shp_file {str}: 研究区域矢量文件
@center_lonlat {[lon, lat]}: 中心经纬度
@aerotype {int}: 气溶胶类型(默认大陆型)
@altitude {float}: 海拔(km)
@visibility {float}: 能见度(km)
@band_need {list}: 所选波段, all表示全部处理
@path_6s {str}: 大气校正结果输出路径
@return:
'''
# 设置环境变量
os.environ['MRTDATADIR'] = global_config['MRTDATADIR']
os.environ['PGSHOME'] = global_config['PGSHOME']
os.environ['MRTBINDIR'] = global_config['MRTBINDIR']
run_path = os.path.split(ifile)[0]
# step 1: 获取hdf信息
SD_file = SD(ifile)
sds_obj = SD_file.select('EV_250_Aggr500_RefSB')
sds_info = sds_obj.attributes()
scales = sds_info['radiance_scales']
offsets = sds_info['radiance_offsets']
sds_obj = SD_file.select('EV_500_RefSB')
sds_info = sds_obj.attributes()
scales_t = sds_info['radiance_scales']
offsets_t = sds_info['radiance_offsets']
for i in range(len(scales_t)):
scales.append(scales_t[i])
offsets.append(offsets_t[i])
SD_file.end()
nbands = len(scales)
date_str = os.path.split(ifile)[1].split('.')[1]
time_str = os.path.split(ifile)[1].split('.')[2]
date_str = date_teanslator.jd_to_cale(date_str[1:])
year = int(date_str.split('.')[0])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
hour = int(time_str[0:2])
minute = int(time_str[2:])
date = '%d/%d/%d %d:%d:00' % (year, month, day, hour, minute)
sola_position = calc_sola_position.main(center_lonlat[0], center_lonlat[1],
date)
solz = sola_position[0]
sola = sola_position[1]
heg_bin = os.path.join(global_config['MRTBINDIR'], 'hegtool')
os.system('cd %s && %s -h %s > heg.log' % (run_path, heg_bin, ifile))
info_file = os.path.join(run_path, 'HegHdr.hdr')
if not (os.path.exists(info_file)):
print('获取文件信息出错：%s' % ifile)
return ('')
else:
lat_min = None
lat_max = None
lon_min = None
lon_max = None
pixel_x = None
pixel_y = None
with open(info_file, 'r') as fp:
lines = fp.readlines()
for line in lines:
if 'SWATH_LAT_MIN' in line:
lat_min = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LAT_MAX' in line:
lat_max = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LON_MIN' in line:
lon_min = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LON_MAX' in line:
lon_max = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_X_PIXEL_RES_DEGREES' in line:
pixel_x = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_Y_PIXEL_RES_DEGREES' in line:
pixel_y = float(re.findall(r'[\d.]+', line)[0])
if lat_min and lat_max and lon_min and lon_max and pixel_x and pixel_y:
prm_file = os.path.join(run_path, 'HegSwath.prm')
# 重投影
for i_band in range(nbands):
modis_band = 'B%d' % (i_band + 1)
if (modis_band in band_need) or ('all' in band_need):
print('reprojection %s ...' % modis_band)
out_file = ifile.replace('.hdf',
'_reproj_' + modis_band + '.tif')
if '.hdf' in ifile:
if os.path.exists(out_file):
os.remove(out_file)
else:
print('无法识别的文件格式：%s' % os.path.split(ifile)[1])
with open(prm_file, 'wb') as fp:
fp.write(b'\nNUM_RUNS = 1\n\n')
fp.write(b'BEGIN\n')
fp.write(
bytes('INPUT_FILENAME = %s\n' % ifile, 'utf-8'))
fp.write(b'OBJECT_NAME = MODIS_SWATH_Type_L1B\n')
if i_band >= 0 and i_band <= 1:
fp.write(b'FIELD_NAME = EV_250_Aggr500_RefSB|\n')
fp.write(
bytes('BAND_NUMBER = %d\n' % (i_band + 1),
'utf-8'))
else:
fp.write(b'FIELD_NAME = EV_500_RefSB|\n')
fp.write(
bytes('BAND_NUMBER = %d\n' % (i_band - 1),
'utf-8'))
fp.write(
bytes('OUTPUT_PIXEL_SIZE_X = %f\n' % pixel_x,
'utf-8'))
fp.write(
bytes('OUTPUT_PIXEL_SIZE_Y = %f\n' % pixel_y,
'utf-8'))
fp.write(
bytes(
'SPATIAL_SUBSET_UL_CORNER = ( %f %f )\n' %
(lat_max, lon_min), 'utf-8'))
fp.write(
bytes(
'SPATIAL_SUBSET_LR_CORNER = ( %f %f )\n' %
(lat_min, lon_max), 'utf-8'))
fp.write(b'OUTPUT_PROJECTION_TYPE = GEO\n')
fp.write(
bytes('OUTPUT_FILENAME = %s\n' % out_file,
'utf-8'))
fp.write(b'OUTPUT_TYPE = GEO\n')
fp.write(b'END\n\n')
swtif_bin = os.path.join(global_config['MRTBINDIR'],
'swtif')
os.system('cd %s && %s -P HegSwath.prm > heg.log' %
(run_path, swtif_bin))
else:
print('[Error] 未获得全部的所需信息')
return (0)
# 裁切与合并
if 'all' in band_need:
raster_file = ifile.replace('.hdf', '_reproj_B1.tif')
else:
raster_file = ifile.replace('.hdf',
'_reproj_' + band_need[0] + '.tif')
if not (os.path.exists(raster_file)):
return ('')
out_file = raster_file.replace('.tif', '_cut.tif')
cut_range = img_cut.main(raster_file,
shp_file=shp_file,
out_file=out_file)
xsize = cut_range[1] - cut_range[0]
ysize = cut_range[3] - cut_range[2]
# 影像裁切
print('裁切...')
for i_band in range(nbands):
modis_band = 'B%d' % (i_band + 1)
if (modis_band in band_need) or ('all' in band_need):
raster_file = ifile.replace('.hdf',
'_reproj_' + modis_band + '.tif')
out_file = raster_file.replace('.tif', '_cut.tif')
img_cut.main(raster_file, sub_lim=cut_range, out_file=out_file)
# 辐射定标
print('辐射定标...')
raster = gdal.Open(raster_file)
date_str = '%d%02d%02d%02d%02d%02d' % (year, month, day, hour + 8,
minute, 0)
nrow = os.path.split(ifile)[1].split('.')[3]
if satellite == 'AQUA':
out_name = 'AQUA_MODIS_500_L2_%s_%s_00.tif' % (date_str, nrow)
elif satellite == 'TERRA':
out_name = 'TERRA_MODIS_500_L2_%s_%s_00.tif' % (date_str, nrow)
else:
print('[Error] 无法识别卫星类型')
return (0)
if os.path.exists(path_out_radi):
raster_fn_out_radi = os.path.join(path_out_radi, out_name)
driver = gdal.GetDriverByName('GTiff')
target_ds = driver.Create(raster_fn_out_radi, xsize, ysize, nbands,
gdal.GDT_Int16)
target_ds.SetGeoTransform(raster.GetGeoTransform())
target_ds.SetProjection(raster.GetProjectionRef())
nan_mask = None
for i_band in range(nbands):
modis_band = 'B%d' % (i_band + 1)
if (modis_band in band_need) or ('all' in band_need):
raster_file = ifile.replace(
'.hdf', '_reproj_' + modis_band + '.tif')
out_file = raster_file.replace('.tif', '_cut.tif')
raster_cut = gdal.Open(out_file)
raster_data = (
raster_cut.GetRasterBand(1).ReadAsArray()).astype(
np.int)
if nan_mask is None:
nan_mask = raster_data > 65530
raster_data[nan_mask] = -9999
target_ds.GetRasterBand(i_band + 1).WriteArray(raster_data)
target_ds.GetRasterBand(i_band + 1).SetNoDataValue(-9999)
raster_cut = None
target_ds = None
else:
print('[Warning] without radiation-correction output!')
# 大气校正
print('大气校正 ...')
nan_mask = None
driver = gdal.GetDriverByName('GTiff')
raster_fn_out_6s = os.path.join(path_out_6s, out_name)
target_ds = driver.Create(raster_fn_out_6s, xsize, ysize, nbands,
gdal.GDT_Int16)
target_ds.SetGeoTransform(raster.GetGeoTransform())
target_ds.SetProjection(raster.GetProjectionRef())
for i_band in range(nbands):
modis_band = 'B%d' % (i_band + 1)
if (modis_band in band_need) or ('all' in band_need):
raster_file = ifile.replace('.hdf',
'_reproj_' + modis_band + '.tif')
out_file = raster_file.replace('.tif', '_cut.tif')
raster_cut = gdal.Open(out_file)
raster_data = raster_cut.GetRasterBand(1).ReadAsArray()
if nan_mask is None:
nan_mask = raster_data > 65530
raster_data = raster_data.astype(float)
raster_data = scales[i_band] * (raster_data - offsets[i_band])
mtl_coef = {
'altitude': altitude,
'visibility': visibility,
'aero_type': aerotype,
'location': center_lonlat,
'month': month,
'day': day,
'solz': solz,
'sola': sola,
'salz': salz,
'sala': sala
}
wave_index = i_band + 42
data_tmp = (arms_corr(raster_data, mtl_coef, wave_index) *
10000).astype(np.int)
data_tmp[nan_mask] = -9999
target_ds.GetRasterBand(i_band + 1).WriteArray(data_tmp)
target_ds.GetRasterBand(i_band + 1).SetNoDataValue(-9999)
raster_cut = None
target_ds = None
raster = None
# 删除过程文件
for item in modis_band_list:
file_name = ifile.replace('.hdf', '_reproj_' + item + '.tif')
file_name_met = file_name.replace('.tif', '.tif.met')
file_name_cut = ifile.replace('.hdf',
'_reproj_' + item + '_cut.tif')
if os.path.exists(file_name) and os.path.exists(file_name_met):
os.remove(file_name)
os.remove(file_name_met)
if os.path.exists(file_name_cut):
os.remove(file_name_cut)
log_list = [
'heg.log', 'swtif.log', 'HegSwath.prm', 'HegHdr.hdr', 'hegtool.log'
]
for item in log_list:
file_name = os.path.join(run_path, item)
if os.path.exists(file_name):
os.remove(file_name)
return (raster_fn_out_6s)
def main0(file_ref,
file_info,
subrange,
aerotype=1,
altitude=0.01,
visibility=15,
path_out=None):
'''
@description: 主程序
@file_ref {str}: DN数据文件
@file_info {str}: 影像信息数据文件(包含经纬度、传感器方位信息等)
@subrange {lon_min, lon_max, lat_min, lat_max}
@return: None
'''
# 文件信息获取
file_sd = SD(file_info)
obj = file_sd.select('Longitude')
lon = obj.get()
obj = file_sd.select('Latitude')
lat = obj.get()
# 构造目标经纬度
if subrange is None:
griddata_key = False # 不使用griddata插值(考虑效率)
lon_min = np.min(lon)
lon_max = np.max(lon)
lat_min = np.min(lat)
lat_max = np.max(lat)
subrange = [lon_min, lon_max, lat_min, lat_max]
xsize = lon.shape[1]
ysize = lon.shape[0]
xstep = (lon_max - lon_min) / xsize
ystep = (lat_min - lat_max) / ysize
x1d = np.linspace(subrange[0], subrange[1], xsize)
y1d = np.linspace(subrange[3], subrange[2], ysize)
[xx, yy] = np.meshgrid(x1d, y1d)
cut_index = [0, ysize, 0, xsize]
xsize0 = xsize
ysize0 = ysize
# 重构
lon_1d = np.reshape(lon, lon.shape[0] * lon.shape[1])
lat_1d = np.reshape(lat, lat.shape[0] * lat.shape[1])
lonlat = np.vstack(([lon_1d], [lat_1d])).T
lon_1d = None
lat_1d = None
else:
griddata_key = True
xstep = 0.375 / 111
ystep = -0.375 / 111
xsize = (subrange[1] - subrange[0]) * 111 / 0.375
ysize = (subrange[3] - subrange[2]) * 111 / 0.375
x1d = np.linspace(subrange[0], subrange[1], xsize)
y1d = np.linspace(subrange[3], subrange[2], ysize)
[xx, yy] = np.meshgrid(x1d, y1d)
cut_index = cut_data(subrange, lon, lat)
xsize0 = cut_index[3] - cut_index[2] # 裁切后的大小
ysize0 = cut_index[1] - cut_index[0]
lon_cut = lon[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
lat_cut = lat[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
lon_1d = np.reshape(lon_cut, lon_cut.shape[0] * lon_cut.shape[1])
lat_1d = np.reshape(lat_cut, lat_cut.shape[0] * lat_cut.shape[1])
lonlat = np.vstack(([lon_1d], [lat_1d])).T
lon_cut = None
lat_cut = None
lon_1d = None
lat_1d = None
# 卫星方位角
obj = file_sd.select('SatelliteAzimuthAngle')
sala_all = obj.get()
sala_cut = sala_all[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
sala = np.mean(sala_cut)
if sala < 0:
sala = sala + 360
sala_all = None
sala_cut = None
# 卫星天顶角
obj = file_sd.select('SatelliteZenithAngle')
salz_all = obj.get()
salz_cut = salz_all[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
salz = np.mean(salz_cut)
salz_all = None
salz_cut = None
# 太阳天顶角
obj = file_sd.select('SolarZenithAngle')
solz_all = obj.get()
solz_cut = solz_all[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
solz = np.mean(solz_cut)
solz_all = None
solz_cut = None
# 太阳方位角
obj = file_sd.select('SolarAzimuthAngle')
sola_all = obj.get()
sola_cut = sola_all[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
sola = np.mean(sola_cut)
sola_all = None
sola_cut = None
file_sd.end()
file_sd = SD(file_ref)
atms_corr_resample = np.zeros((xx.shape[0], xx.shape[1], 4))
center_lonlat = [(subrange[0] + subrange[1]) / 2,
(subrange[2] + subrange[3]) / 2]
for i in range(4):
obj_name = 'Radiance_I' + str(i + 1)
obj = file_sd.select(obj_name)
data = obj.get()
data_cut = data[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
info = obj.attributes()
scale = info['Scale']
offset = info['Offset']
radi_cali = data_cut * scale + offset
date_str = os.path.split(file_ref)[1].split('.')[1]
date_str = date_teanslator.jd_to_cale(date_str[1:])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
mtl_coef = {
'altitude': altitude,
'visibility': visibility,
'aero_type': aerotype,
'location': center_lonlat,
'month': month,
'day': day,
'solz': solz,
'sola': sola,
'salz': salz,
'sala': sala
}
atms_corr = arms_corr(radi_cali, mtl_coef, i + 161)
# 重采样
print('I%d 大气校正 ...' % (i + 1))
atms_corr_1d = np.reshape(atms_corr, xsize0 * ysize0)
print('I%d 重采样 ...' % (i + 1))
if griddata_key:
atms_corr_resample[:, :, i] = griddata(lonlat,
atms_corr_1d, (xx, yy),
method='nearest')
else:
# 采用最邻近填充法，效率较高，但有部分损失
data_resize = np.zeros(xx.shape) + np.nan
x_index = np.round((lon - lon_min) / xstep)
y_index = ysize - 1 - np.round((lat - lat_min) / abs(ystep))
usefall_key = np.logical_and(x_index < xsize, x_index >= 0)
usefall_key = np.logical_and(usefall_key, y_index < ysize)
usefall_key = np.logical_and(usefall_key, y_index >= 0)
x_index_1d = x_index[usefall_key]
y_index_1d = y_index[usefall_key]
data_index_1d = atms_corr[usefall_key]
for j in range(len(y_index[usefall_key])):
data_resize[int(y_index_1d[j]),
int(x_index_1d[j])] = data_index_1d[j]
driver = gdal.GetDriverByName('GTiff')
xsize = np.shape(atms_corr_resample)[1]
ysize = np.shape(atms_corr_resample)[0]
nbands = 4
if '.hdf' in file_ref:
# 输出文件名
date_str = os.path.split(file_ref)[1].split('.')[1]
time_str = os.path.split(file_ref)[1].split('.')[2]
date_str = date_teanslator.jd_to_cale(date_str[1:])
year = int(date_str.split('.')[0])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
hour = int(time_str[0:2])
minute = int(time_str[2:])
date_str = '%d%02d%02d%02d%02d%02d' % (year, month, day, hour + 8,
minute, 0)
nrow = os.path.split(file_ref)[1].split('.')[3]
out_name = 'NPP_VIIRS_375_L2_%s_%s_00.tif' % (date_str, nrow)
raster_fn_out = os.path.join(path_out, out_name)
else:
print('无法识别的文件类型: %s' % os.path.split(file_ref)[1])
return (0)
target_ds = driver.Create(raster_fn_out, xsize, ysize, nbands,
gdal.GDT_UInt16)
raster_srs = osr.SpatialReference()
raster_srs.ImportFromEPSG(4326)
geo_trans = (subrange[0], xstep, 0, subrange[3], 0, ystep)
target_ds.SetGeoTransform(geo_trans)
target_ds.SetProjection(raster_srs.ExportToWkt())
for i in range(nbands):
data_tmp = atms_corr_resample[:, :, i]
mask = np.logical_or(data_tmp >= 65530, np.isnan(data_tmp))
data_tmp = (data_tmp * 10000).astype(np.int)
data_tmp[mask] = 65530
target_ds.GetRasterBand(i + 1).WriteArray(data_tmp)
band = target_ds.GetRasterBand(1 + 1)
band.SetNoDataValue(65530)
target_ds = None
return (raster_fn_out)
def main(file_ref,
file_info,
subrange,
aerotype=1,
altitude=0.01,
visibility=15,
path_out=None):
'''
@description: 主程序
@file_ref {str}: DN数据文件
@file_info {str}: 影像信息数据文件(包含经纬度、传感器方位信息等)
@subrange {lon_min, lon_max, lat_min, lat_max}
@return: None
'''
nbands = 4
mask_value = 65533
# hdf转geotif
print('reconstruction...')
hdf_merge = file_ref.replace('.hdf', '_merge.hdf')
if os.path.exists(hdf_merge):
os.system('rm %s' % hdf_merge)
file_sd_out = SD(hdf_merge, SDC.CREATE | SDC.WRITE)
file_sd1 = SD(file_ref)
file_sd2 = SD(file_info)
obj = file_sd2.select('Longitude')
lon = obj.get()
obj = file_sd2.select('Latitude')
lat = obj.get()
fields = ['Radiance_I%d' % (i + 1) for i in range(nbands)]
size = None
for field in fields:
obji = file_sd1.select(field)
if size is None:
size = obji[:].shape
objo = file_sd_out.create(field, SDC.UINT16, size)
objo.set(obji[:])
obji = file_sd2.select('Longitude')
objo = file_sd_out.create('Longitude', SDC.FLOAT32, size)
objo.set(obji[:])
obji = file_sd2.select('Latitude')
objo = file_sd_out.create('Latitude', SDC.FLOAT32, size)
objo.set(obji[:])
obji.endaccess()
objo.endaccess()
file_sd_out.end()
for i in range(nbands):
ofile_tif = file_ref.replace('.hdf', '_band%s_reproj.tif' % str(i + 1))
cmd = '%s -geoloc -t_srs EPSG:4326 -srcnodata %s HDF4_SDS:UNKNOWN:"%s":%s %s' % (
global_config['path_gdalwarp'], mask_value, hdf_merge, str(i),
ofile_tif)
os.system(cmd)
# 卫星方位信息
cut_index = cut_data(subrange, lon, lat)
# 太阳天顶角
obj = file_sd2.select('SolarZenithAngle')
data = obj.get()
data_cut = data[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
solz = np.mean(data_cut)
# 太阳方位角
obj = file_sd2.select('SolarAzimuthAngle')
data = obj.get()
data_cut = data[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
sola = np.mean(data_cut)
# 卫星天顶角
obj = file_sd2.select('SatelliteZenithAngle')
data = obj.get()
data_cut = data[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
salz = np.mean(data_cut)
# 卫星方位角
obj = file_sd2.select('SatelliteAzimuthAngle')
data = obj.get()
data_cut = data[cut_index[0]:cut_index[1], cut_index[2]:cut_index[3]]
sala = np.mean(data_cut)
center_lonlat = [(subrange[0] + subrange[1]) / 2,
(subrange[2] + subrange[3]) / 2]
raster = gdal.Open(file_ref.replace('.hdf', '_band1_reproj.tif'))
xsize = raster.RasterXSize
ysize = raster.RasterYSize
geo_trans = raster.GetGeoTransform()
proj_ref = raster.GetProjectionRef()
# 计算裁切范围
target_lon_min = 116.28
target_lon_max = 125.0
target_lat_min = 30.0
target_lat_max = 37.83
colm_s = int(round((target_lon_min - geo_trans[0]) / geo_trans[1]))
colm_e = int(round((target_lon_max - geo_trans[0]) / geo_trans[1]))
line_s = int(round((target_lat_max - geo_trans[3]) / geo_trans[5]))
line_e = int(round((target_lat_min - geo_trans[3]) / geo_trans[5]))
if colm_s < 0:
colm_s = 0
if line_s < 0:
line_s = 0
if colm_e >= xsize:
colm_e = xsize - 1
if line_e >= ysize:
line_e = ysize - 1
x_1d = np.array([geo_trans[0] + i * geo_trans[1] for i in range(xsize)])
y_1d = np.array([geo_trans[3] + i * geo_trans[5] for i in range(ysize)])
xx, yy = np.meshgrid(x_1d, y_1d)
xx_sub = xx[line_s:line_e, colm_s:colm_e]
yy_sub = yy[line_s:line_e, colm_s:colm_e]
# 文件保存所需信息
date_str = os.path.split(file_ref)[1].split('.')[1]
time_str = os.path.split(file_ref)[1].split('.')[2]
date_str = date_teanslator.jd_to_cale(date_str[1:])
year = int(date_str.split('.')[0])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
hour = int(time_str[0:2])
minute = int(time_str[2:])
date_str = '%d%02d%02d%02d%02d%02d' % (year, month, day, hour + 8, minute,
0)
nrow = os.path.split(file_ref)[1].split('.')[3]
out_name = 'NPP_VIIRS_375_L2_%s_%s_00.tif' % (date_str, nrow)
raster_fn_out = os.path.join(path_out, out_name)
driver = gdal.GetDriverByName('GTiff')
target_ds = driver.Create(raster_fn_out, xsize, ysize, nbands,
gdal.GDT_UInt16)
target_ds.SetGeoTransform(geo_trans)
target_ds.SetProjection(proj_ref)
# 大气校正
for i in range(nbands):
obj_name = 'Radiance_I' + str(i + 1)
obj = file_sd1.select(obj_name)
raster = gdal.Open(
file_ref.replace('.hdf', '_band%s_reproj.tif' % str(i + 1)))
data = raster.GetRasterBand(1).ReadAsArray()
print('重采样:Band %s' % (i + 1))
data_sub = data[line_s:line_e, colm_s:colm_e]
blank_key = data_sub == mask_value
# OpenCV形态学处理
blank_key[:, 0] = 0
blank_key[:, -1] = 0
blank_key[0, :] = 0
blank_key[-1, :] = 0
labels_struct = cv2.connectedComponentsWithStats(blank_key.astype(
np.uint8),
connectivity=4)
for i_label in range(1, labels_struct[0]):
if labels_struct[2][i_label][4] > 1e5:
blank_key[labels_struct[1] == i_label] = 0
lon_blank = xx_sub[blank_key]
lat_blank = yy_sub[blank_key]
valid_key = np.logical_not(blank_key)
lon_valid = xx_sub[valid_key]
lat_valid = yy_sub[valid_key]
lonlat = np.vstack((lon_valid, lat_valid)).T
data_valid = data_sub[valid_key]
data_blank = griddata(lonlat,
data_valid, (lon_blank, lat_blank),
method='nearest')
data_sub[blank_key] = data_blank
data[line_s:line_e, colm_s:colm_e] = data_sub
mask = data == mask_value
print('I%d 辐射定标和大气校正 ...' % (i + 1))
info = obj.attributes()
scale = info['Scale']
offset = info['Offset']
radi_cali = data.astype(float) * scale + offset
date_str = os.path.split(file_ref)[1].split('.')[1]
date_str = date_teanslator.jd_to_cale(date_str[1:])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
mtl_coef = {
'altitude': altitude,
'visibility': visibility,
'aero_type': aerotype,
'location': center_lonlat,
'month': month,
'day': day,
'solz': solz,
'sola': sola,
'salz': salz,
'sala': sala
}
atms_corr = arms_corr(radi_cali, mtl_coef, i + 161)
# save
data_tmp = (atms_corr * 10000).astype(np.int)
data_tmp[mask] = mask_value
target_ds.GetRasterBand(i + 1).WriteArray(data_tmp)
band = target_ds.GetRasterBand(i + 1)
band.SetNoDataValue(mask_value)
target_ds = None
file_sd1.end()
file_sd2.end()
# 删除过程文件
os.system('rm %s' % hdf_merge)
for i in range(nbands):
os.system(
'rm %s' %
(file_ref.replace('.hdf', '_band%s_reproj.tif' % str(i + 1))))
return (raster_fn_out)
def main(ifile,
shp_file,
center_lonlat,
salz,
sala,
cut_range=None,
aerotype=1,
altitude=0.01,
visibility=15,
band_need=['all'],
path_out=None):
'''
@description:
@ifile {str}: L1B swath文件
@shp_file {str}: 研究区域矢量文件
@center_lonlat {[lon, lat]}: 中心经纬度
@aerotype {int}: 气溶胶类型(默认大陆型)
@altitude {float}: 海拔(km)
@visibility {float}: 能见度(km)
@return:
'''
# 设置环境变量
os.environ['MRTDATADIR'] = global_config['MRTDATADIR']
os.environ['PGSHOME'] = global_config['PGSHOME']
os.environ['MRTBINDIR'] = global_config['MRTBINDIR']
run_path = os.path.split(ifile)[0]
# step 1: 获取hdf信息
nbands = 16
SD_file = SD(ifile)
scales = []
offsets = []
for i_band in range(nbands):
if i_band + 1 == 9:
sds_obj = SD_file.select('Reflectance_M%d' % (i_band + 1))
elif i_band + 1 >= 12:
sds_obj = SD_file.select('BrightnessTemperature_M%d' %
(i_band + 1))
else:
sds_obj = SD_file.select('Radiance_M%d' % (i_band + 1))
sds_info = sds_obj.attributes()
if 'Scale' in sds_info:
scales.append(sds_info['Scale'])
else:
scales.append(1)
if 'Offset' in sds_info:
offsets.append(sds_info['Offset'])
else:
offsets.append(0)
SD_file.end()
date_str = os.path.split(ifile)[1].split('.')[1]
time_str = os.path.split(ifile)[1].split('.')[2]
date_str = date_teanslator.jd_to_cale(date_str[1:])
year = int(date_str.split('.')[0])
month = int(date_str.split('.')[1])
day = int(date_str.split('.')[2])
hour = int(time_str[0:2])
minute = int(time_str[2:])
date = '%d/%d/%d %d:%d:00' % (year, month, day, hour, minute)
sola_position = calc_sola_position.main(center_lonlat[0], center_lonlat[1],
date)
solz = sola_position[0]
sola = sola_position[1]
heg_bin = os.path.join(global_config['MRTBINDIR'], 'hegtool')
os.system('cd %s && %s -h %s > heg.log' % (run_path, heg_bin, ifile))
info_file = os.path.join(run_path, 'HegHdr.hdr')
if not (os.path.exists(info_file)):
print('获取文件信息出错：%s' % ifile)
return (0)
else:
lat_min = None
lat_max = None
lon_min = None
lon_max = None
pixel_x = None
pixel_y = None
with open(info_file, 'r') as fp:
lines = fp.readlines()
for line in lines:
if 'SWATH_LAT_MIN' in line:
lat_min = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LAT_MAX' in line:
lat_max = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LON_MIN' in line:
lon_min = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_LON_MAX' in line:
lon_max = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_X_PIXEL_RES_DEGREES' in line:
pixel_x = float(re.findall(r'[\d.]+', line)[0])
elif 'SWATH_Y_PIXEL_RES_DEGREES' in line:
pixel_y = float(re.findall(r'[\d.]+', line)[0])
if not (cut_range is None):
lat_min = cut_range[2]
lat_max = cut_range[3]
lon_min = cut_range[0]
lon_max = cut_range[1]
if lat_min and lat_max and lon_min and lon_max and pixel_x and pixel_y:
prm_file = os.path.join(run_path, 'HegSwath.prm')
# 反射波段值重投影
for i_band in range(nbands):
viirs_band = 'M%d' % (i_band + 1)
if (viirs_band in band_need) or ('all' in band_need):
print('reprojection %s ...' % viirs_band)
out_file = ifile.replace('.hdf',
'_reproj_' + viirs_band + '.tif')
if '.hdf' in ifile:
if os.path.exists(out_file):
os.remove(out_file)
else:
print('无法识别的文件格式：%s' % os.path.split(ifile)[1])
with open(prm_file, 'wb') as fp:
fp.write(b'\nNUM_RUNS = 1\n\n')
fp.write(b'BEGIN\n')
fp.write(
bytes('INPUT_FILENAME = %s\n' % ifile, 'utf-8'))
fp.write(b'OBJECT_NAME = VIIRS_EV_750M_SDR\n')
if viirs_band == 'M9':
fp.write(
bytes(
'FIELD_NAME = Reflectance_M%d|\n' %
(i_band + 1), 'utf-8'))
elif viirs_band == 'M15' or viirs_band == 'M16':
fp.write(
bytes(
'FIELD_NAME = BrightnessTemperature_M%d|\n'
% (i_band + 1), 'utf-8'))
else:
fp.write(
bytes(
'FIELD_NAME = Radiance_M%d|\n' %
(i_band + 1), 'utf-8'))
fp.write(b'BAND_NUMBER = 1\n')
fp.write(
bytes('OUTPUT_PIXEL_SIZE_X = %f\n' % pixel_x,
'utf-8'))
fp.write(
bytes('OUTPUT_PIXEL_SIZE_Y = %f\n' % pixel_y,
'utf-8'))
fp.write(
bytes(
'SPATIAL_SUBSET_UL_CORNER = ( %f %f )\n' %
(lat_max, lon_min), 'utf-8'))
fp.write(
bytes(
'SPATIAL_SUBSET_LR_CORNER = ( %f %f )\n' %
(lat_min, lon_max), 'utf-8'))
fp.write(b'OUTPUT_PROJECTION_TYPE = GEO\n')
fp.write(
bytes('OUTPUT_FILENAME = %s\n' % out_file,
'utf-8'))
fp.write(b'OUTPUT_TYPE = GEO\n')
fp.write(b'END\n\n')
swtif_bin = os.path.join(global_config['MRTBINDIR'],
'swtif')
os.system('cd %s && %s -P HegSwath.prm > heg.log' %
(run_path, swtif_bin))
else:
print('[Error] 未获得全部的所需信息')
return (0)
# 裁切与合并
if 'all' in band_need:
raster_file = ifile.replace('.hdf', '_reproj_M1.tif')
else:
raster_file = ifile.replace('.hdf',
'_reproj_' + band_need[0] + '.tif')
out_file = raster_file.replace('.tif', '_cut.tif')
cut_range = img_cut.main(raster_file,
shp_file=shp_file,
out_file=out_file)
xsize = cut_range[1] - cut_range[0]
ysize = cut_range[3] - cut_range[2]
data_join = np.zeros([ysize, xsize, nbands])
print('大气校正 ...')
for i_band in range(nbands):
viirs_band = 'M%d' % (i_band + 1)
if (viirs_band in band_need) or ('all' in band_need):
raster_file = ifile.replace('.hdf',
'_reproj_' + viirs_band + '.tif')
out_file = raster_file.replace('.tif', '_cut.tif')
img_cut.main(raster_file, sub_lim=cut_range, out_file=out_file)
# 辐射定标和大气校正
raster = gdal.Open(out_file)
raster_data = raster.GetRasterBand(1).ReadAsArray()
raster_data = raster_data.astype(float)
mask = raster_data >= 65530
raster_data = scales[i_band] * raster_data + offsets[i_band]
mtl_coef = {
'altitude': altitude,
'visibility': visibility,
'aero_type': aerotype,
'location': center_lonlat,
'month': month,
'day': day,
'solz': solz,
'sola': sola,
'salz': salz,
'sala': sala
}
if i_band <= 11:
wave_index = i_band + 149
else:
wave_index = None
if not (wave_index is None):
if viirs_band == 'M9':
tmp = raster_data
else:
tmp = arms_corr(raster_data, mtl_coef, wave_index)
# tmp = raster_data
tmp[mask] = 65535
data_join[:, :, i_band] = tmp
else:
tmp = raster_data
tmp[mask] = 65535
data_join[:, :, i_band] = tmp
driver = gdal.GetDriverByName('GTiff')
if 'all' in band_need:
raster_file = ifile.replace('.hdf', '_reproj_M1_cut.tif')
else:
raster_file = ifile.replace('.hdf',
'_reproj_' + band_need[0] + '_cut.tif')
raster = gdal.Open(raster_file)
# 输出文件名
date_str = '%d%02d%02d%02d%02d%02d' % (year, month, day, hour + 8,
minute, 0)
nrow = os.path.split(ifile)[1].split('.')[3]
out_name = 'NPP_VIIRS_750_L2_%s_%s_00.tif' % (date_str, nrow)
if path_out is None:
raster_fn_out = os.path.join(os.path.split(ifile)[0], out_name)
else:
raster_fn_out = os.path.join(path_out, out_name)
target_ds = driver.Create(raster_fn_out, xsize, ysize, nbands,
gdal.GDT_UInt16)
target_ds.SetGeoTransform(raster.GetGeoTransform())
target_ds.SetProjection(raster.GetProjectionRef())
for i in range(nbands):
if i < 11:
target_ds.GetRasterBand(i + 1).WriteArray(
(data_join[:, :, i] * 10000).astype(np.int))
else:
target_ds.GetRasterBand(i + 1).WriteArray(
(data_join[:, :, i] * 100).astype(np.int))
band = target_ds.GetRasterBand(i + 1)
band.SetNoDataValue(65535)
target_ds = None
raster = None
# 删除过程文件
for item in viirs_band_list:
file_name = ifile.replace('.hdf', '_reproj_' + item + '.tif')
file_name_met = file_name.replace('.tif', '.tif.met')
file_name_cut = ifile.replace('.hdf',
'_reproj_' + item + '_cut.tif')
if os.path.exists(file_name) and os.path.exists(file_name_met):
os.remove(file_name)
os.remove(file_name_met)
if os.path.exists(file_name_cut):
os.remove(file_name_cut)
log_list = [
'heg.log', 'swtif.log', 'HegSwath.prm', 'HegHdr.hdr', 'hegtool.log'
]
for item in log_list:
file_name = os.path.join(run_path, item)
if os.path.exists(file_name):
os.remove(file_name)
return (raster_fn_out)