def query(self,
start_date: str,
end_date: str,
product: str,
provider: str = 'LPDAAC_ECS',
bbox: List = []) -> List[Dict]:
"""
Search CMR database for spectral MODIS tiles matching a temporal range,
defined by a start date and end date. Returns metadata containing the URL of
each image.
Parameters
----------
start_date: string
Start date yyyy-mm-dd
end_date: string
End date yyyy-mm-dd
product: string
Product name
provider: string
Provider (default is 'LPDAAC_ECS')
bbox: List[float]
Bounding box [lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat]
Returns
----------
List[Dict]
List of granules
"""
q = GranuleQuery()
prod, ver = product['products'][0].split('.')
q.short_name(prod).version(ver)
q.temporal(f"{start_date}T00:00:00Z", f"{end_date}T23:59:59Z")
if (len(bbox) >= 4):
q.bounding_box(*bbox[:4])
_granules = q.get_all()
# filter dates
if 'day_offset' in product.keys():
day_offset = products[product]['day_offset']
else:
day_offset = 0
granules = []
for gran in _granules:
# CMR uses day 1 of window - correct this to be middle of window
date = (dateparser(gran['time_start'].split('T')[0]) +
datetime.timedelta(days=day_offset)).date()
if (dateparser(start_date).date() <= date <=
dateparser(end_date).date()):
granules.append(gran)
logger.info("%s granules found within %s - %s" %
(len(granules), start_date, end_date))
return granules
def query(start_date, end_date, product='MCD43A4.006', provider='LPDAAC_ECS'):
""" Search CMR database for spectral MODIS tiles matching a temporal range,
defined by a start date and end date. Returns metadata containing the URL of
each image.
"""
q = GranuleQuery()
prod, ver = product.split('.')
q.short_name(prod).version(ver)
q.temporal('%sT00:00:00Z' % str(start_date),
'%sT23:59:00Z' % str(end_date))
_granules = q.get_all()
# filter dates
day_offset = products[product]['day_offset']
granules = []
for gran in _granules:
# CMR uses day 1 of window - correct this to be middle of window
date = (dateparser(gran['time_start'].split('T')[0]) +
datetime.timedelta(days=day_offset)).date()
if (start_date <= date <= end_date):
granules.append(gran)
logger.info("%s granules found within %s - %s" %
(len(granules), start_date, end_date))
return granules
def get_modis(destination, date, search_window, footprint, choose, clip,
reproject):
"""
Retrieves MODIS scenes.
Scenes must entirely contain <footprint> within date +/- search_window.
"""
workdir = path.join(destination, "modis")
makedirs(workdir, exist_ok=True)
footprint_path = path.join(destination, footprint)
searchFootprint = shape(fiona.open(footprint_path)[0]["geometry"])
date_window = timedelta(days=search_window)
date_range = (date - date_window, date + date_window)
cmrAPI = GranuleQuery()
results = (cmrAPI.short_name(MODIS_SNOW_CMR_SHORT_NAME).bounding_box(
*searchFootprint.bounds).temporal(*date_range).get())
results = json_normalize(results)
print(results["time_start"].iloc[0])
print(date)
results["time_start"] = to_datetime(
results["time_start"]).dt.tz_localize(None)
results["timedeltas"] = (date - results.time_start).abs()
results = results.sort_values(by="timedeltas", ascending=True)
results["browse"] = [[
_i["href"] for _i in image_links if _i.get("title", "") == "(BROWSE)"
][0] for image_links in results.links]
print(results.iloc[0].links)
image_iloc = 0
if choose:
print(
tabulate(
results[[
"cloud_cover", "day_night_flag", "timedeltas", "browse"
]].reset_index(drop=True),
headers="keys",
))
image_iloc = int(
input("Choose image ID [0-{}]: ".format(len(results) - 1)))
image_url = results.iloc[image_iloc].links[0]["href"]
MODIS_DL_COMMAND = ("wget "
"--http-user=$EARTHDATA_USERNAME "
"--http-password=$EARTHDATA_PASSWORD "
"--no-check-certificate --auth-no-challenge "
'-r --reject "index.html*" -np -e robots=off -nH -nd '
"--directory-prefix={destination} "
"{image_url}".format(destination=workdir,
image_url=image_url))
_sh = Popen(MODIS_DL_COMMAND, shell=True).communicate()
## have to do some gdal magic with MODIS data.
## 1) turn it into a TIFF with the right projection.
MODISfile = glob(path.join(workdir, "MOD10A1*.hdf*"))[0]
output_file = path.join(workdir, "MODIS_reproj.tif")
MODIS_CONVERT_COMMAND = (
"gdalwarp "
"HDF4_EOS:EOS_GRID:{path}:MOD_Grid_Snow_500m:NDSI_Snow_Cover "
"-cutline {footprint} -crop_to_cutline -dstnodata 9999 "
"-t_srs {projection} -r cubic "
"-s_srs '+proj=sinu +R=6371007.181 +nadgrids=@null +wktext' "
"{output_tif} ".format(
path=MODISfile,
footprint=footprint_path,
projection=reproject,
output_tif=output_file,
))
_sh = Popen(MODIS_CONVERT_COMMAND, shell=True).communicate()
print(MODISfile, output_file)