Run matched filter on EnMAP product¶
Author: Gonzalo Mateo-García
See example of using EnMAP reader in georeader.
The Window-Wide Matched Filter is proposed in: > J. Roger, L. Guanter, J. Gorroño, and I. Irakulis-Loitxate, “Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers,” Atmospheric Measurement Techniques, vol. 17, no. 4, pp. 1333–1346, Feb. 2024, doi: 10.5194/amt-17-1333-2024.
Download EnMAP image from UNEP IMEO Cloud Storage¶
This requires the adlfs package:
pip install adlfs
import os
SAS_TOKEN = ""
AZURE_STORAGE_ACCOUNT=""
CONTAINER = ""
folder_dest = "../tests/data/EnMAP"
tile = "ENMAP01-____L1B-DT0000149931_20250820T075156Z_002_V010502_20250827T172144Z"
folder_dest_tile = os.path.join(folder_dest, tile)
path_metadata_dst = os.path.join(folder_dest_tile, f"{tile}-METADATA.XML")
NEED_DOWNLOAD = not os.path.exists(path_metadata_dst)
print(f"File '{path_metadata_dst}' exists: {not NEED_DOWNLOAD}")
if NEED_DOWNLOAD:
import adlfs
fs = adlfs.AzureBlobFileSystem(account_name=AZURE_STORAGE_ACCOUNT, sas_token=SAS_TOKEN, container=CONTAINER)
PRODUCT_FOLDERS = {
"SPECTRAL_IMAGE_SWIR": "swir",
"QL_QUALITY_CLOUDSHADOW": "ql/cloudshadow",
"QL_PIXELMASK_SWIR": "ql/pixelmask_swir",
"QL_QUALITY_CIRRUS": "ql/cirrus",
"QL_QUALITY_SNOW": "ql/snow",
"QL_PIXELMASK_VNIR": "ql/pixelmask_vnir",
"QL_QUALITY_TESTFLAGS_SWIR": "ql/testflags_swir",
"QL_QUALITY_HAZE": "ql/haze",
"QL_QUALITY_TESTFLAGS_VNIR": "ql/testflags_vnir",
"QL_QUALITY_CLASSES": "ql/classes",
"QL_SWIR": "ql/swir",
"QL_QUALITY_CLOUD": "ql/cloud",
"QL_VNIR": "ql/vnir",
"SPECTRAL_IMAGE_VNIR": "vnir",
}
if NEED_DOWNLOAD:
os.makedirs(folder_dest_tile, exist_ok=True)
# Copy metadata
path_metadata = f"az://{CONTAINER}/EnMAP/metadata/{tile}.xml"
if not fs.exists(path_metadata):
raise FileNotFoundError(f"File {path_metadata} does not exist")
fs.get(path_metadata, path_metadata_dst)
# Download TIF files
for extension, folder in PRODUCT_FOLDERS.items():
path = f"az://{CONTAINER}/EnMAP/{folder}/{tile}.tif"
path_dst = os.path.join(folder_dest_tile, f"{tile}-{extension}.TIF")
if os.path.exists(path_dst):
continue
if not fs.exists(path):
print(f"File {path} not found skipping")
continue
print(f"Downloading file {path}...")
fs.get(path, path_dst)
else:
print(f"Skipping download, '{path_metadata_dst}' already exists")
!tree {folder_dest_tile}
Load image¶
from georeader.readers import enmap
rst = enmap.EnMAP(xml_file=path_metadata_dst,by_folder=False)
rst
Run WW-MF retrieval on EnMAP product¶
Run wide matched filter retrieval based on the work of Roger et al. 2024.
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
from marshsi.prismaenmap import retrieval_upv_prisma_enmap
cube = retrieval_upv_prisma_enmap.MF_sunglint_combo_enmap(rst,logger=logger)
Reproject to UTM and apply RPCS¶
import georeader
from georeader import read
import numpy as np
crs = georeader.get_utm_epsg(cube.footprint(crs="EPSG:4326").centroid)
cube_crs = read.read_rpcs(
cube.values.astype(np.float32),
rpcs=rst.rpcs_swir,
dst_crs=crs,
resolution_dst_crs=30,
fill_value_default=cube.fill_value_default,
)
cube_crs
retrieval_upv_prisma_enmap.BAND_NAMES_COMBO
rgb = rst.load_rgb(as_reflectance=True,
dst_crs=crs,
apply_rpcs=True,
resolution_dst_crs=30)
rgb
Plot results¶
from georeader import plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1,3, figsize=(15,5), sharey=True)
plot.show(rgb, ax=ax[0], mask=True, title="RGB")
mf = cube_crs.isel({"band": 1})
plot.show(mf, ax=ax[1], vmin=0, vmax=.3, title= "MF $\Delta X$CH$_4$ [ppm]", mask=True)
wmf = cube_crs.isel({"band": 2})
plot.show(wmf, ax=ax[2], vmin=0, vmax=.3, title= "WWMF $\Delta X$CH$_4$ [ppm]", mask=True)
The marshsi package and tutorials are released under a Creative Commons non-commercial Share-Alike licence. For using the codes in comercial pipelines written consent by the authors must be provided.
If you find this work useful please cite:
@Article{roger_2024,
AUTHOR = {Roger, J. and Guanter, L. and Gorroño, J. and Irakulis-Loitxate, I.},
TITLE = {Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers},
JOURNAL = {Atmospheric Measurement Techniques},
VOLUME = {17},
YEAR = {2024},
NUMBER = {4},
PAGES = {1333--1346},
URL = {https://amt.copernicus.org/articles/17/1333/2024/},
DOI = {10.5194/amt-17-1333-2024}
}