nisar-pytools 0.3.1

Open source Python tools for working with NISAR datasets

nisar_pytools

Open source Python tools for working with NISAR datasets.

About

nisar_pytools provides utilities for searching, downloading, reading, and processing data products from NASA's NISAR (NASA-ISRO Synthetic Aperture Radar) mission.

Supported Products

• GSLC - Geocoded Single Look Complex
• GUNW - Geocoded Unwrapped Interferogram

Additional NISAR product types will be added over time.

Getting Started

Prerequisites

• Python 3.10+
• Miniforge (recommended)
• NASA Earthdata login (for downloading from ASF)

Installation

From PyPI:

pip install nisar-pytools

With optional extras:

pip install nisar-pytools[dem]       # DEM fetching (dem_stitcher)
pip install nisar-pytools[dolphin]   # dolphin InSAR time-series prep
pip install nisar-pytools[viz]       # Visualization (matplotlib)
pip install nisar-pytools[isce3]     # PyPI bits of the isce3 RSLC->GUNW stack (will need mamba install of isce3 libgal-hdf5)
pip install nisar-pytools[all]       # Everything

From source (for development):

git clone https://github.com/zmhoppinen/nisar_pytools.git
cd nisar_pytools
mamba env create -f environment.yml
conda activate nisar_pytools

The bundled environment.yml installs the full ISCE3 RSLC → GUNW stack (isce3, libgdal-hdf5, snaphu, pygrib, pyaps3, raider, pysolid, netcdf4, dem_stitcher) from conda-forge. Both isce3 and libgdal-hdf5 are conda-forge-only, so a pure-pip install can't reach them — if you're not using the conda env, install them yourself:

mamba install -c conda-forge isce3 libgdal-hdf5
pip install 'nisar-pytools[isce3]'

Usage - Command Line

Geotiff export

Command line utility for quick export of commonly used bands from a NISAR HDF5 as GeoTIFFshandy for pulling into QGIS.

Installed with the package as the nisar_pytools console script; run nisar_pytools to-geotiff --help for the full band catalog and flag reference.

# Default-all: write every default band for the product, next to the .h5
# GUNW -> unwrapped_phase, wrapped_phase, coherence, ionosphere
# GSLC -> amplitude (10*log10(|SLC|^2) in dB)
nisar_pytools to-geotiff NISAR_L2_PR_GUNW_...h5

# One band, explicit polarization, custom output directory
nisar_pytools to-geotiff NISAR_L2_PR_GUNW_...h5 \
    --band unwrapped_phase --pol HH --output-dir /tmp/gunw_tifs

# Subset a multi-GB GSLC to a lat/lon AOI -- streamed, low memory
nisar_pytools to-geotiff NISAR_L2_PR_GSLC_...h5 \
    --bbox-wgs84 -118.5 41.0 -118.3 41.2

# Same crop in the file's native CRS (UTM meters here)
nisar_pytools to-geotiff NISAR_L2_PR_GSLC_...h5 \
    --bbox 380000 4540000 400000 4565000

# GSLC amplitude on frequency B
nisar_pytools to-geotiff NISAR_L2_PR_GSLC_...h5 --freq B

Outputs are tiled GeoTIFFs named <h5_stem>_<band>_<pol>.tif. Writes stream chunk-by-chunk via dask + rioxarray, so a full-resolution 41 GB GSLC processes with ~330 MB peak memory.

h5 info

Quick summary of a NISAR HDF5 (GSLC or GUNW): product type/version, file size, acquisition time(s), track/frame/direction, polarizations, per-grid shape and resolution, native + WGS84 extent, and (for GUNW) coherence / unwrapped-phase stats, connected-component summary, and pre-computed perpendicular + parallel baselines from the radarGrid cube.

# Formatted text summary
nisar_pytools info NISAR_L2_PR_GUNW_...h5

# Same fields, machine-readable
nisar_pytools info NISAR_L2_PR_GSLC_...h5 --json

RSLC → GUNW (production InSAR pipeline)

Wrapper around nisar.workflows.insar (the production isce3 InSAR workflow) that takes an RSLC pair and produces a GUNW. Requires the isce3 stack from environment.yml (see Installation above).

# Minimal: auto-fetch DEM, auto-detect UTM zone + bbox, production-spec runconfig
nisar_pytools rslc-to-gunw ref.h5 sec.h5 --output-dir gunw_run/

# Bring your own DEM + crop output to a smaller AOI in UTM 11N
nisar_pytools rslc-to-gunw ref.h5 sec.h5 \
    --output-dir gunw_run/ \
    --dem dem.tif \
    --bbox 464000 4884080 544000 4964080 \
    --epsg 32611

# Use a custom runconfig (overrides the bundled default)
nisar_pytools rslc-to-gunw ref.h5 sec.h5 --output-dir gunw_run/ --runconfig my.yaml

# Force a full re-run, ignoring previously-cached step outputs
nisar_pytools rslc-to-gunw ref.h5 sec.h5 --output-dir gunw_run/ --restart

Outputs gunw_run/product.h5 (the GUNW), with intermediates and the log under gunw_run/scratch/. Multi-hour wall time on CPU.

Usage - Python

Search and Download

from nisar_pytools import find_nisar, download_urls

# find_nisar() searches ASF for NISAR products. Parameters:
#   aoi          - area of interest: [xmin, ymin, xmax, ymax], shapely geometry,
#                  or dict like {"west": -115, "south": 43, "east": -114, "north": 44}
#   start_date   - start of temporal search window (ISO string or datetime)
#   end_date     - end of temporal search window

#   product_type - "GSLC", "GUNW", "RSLC", "GCOV", "RIFG", "RUNW", "ROFF", "GOFF"
#   path_number  - relative orbit / track number (optional)
#   frame        - frame number (optional)
#   direction    - "ASCENDING" or "DESCENDING" (optional)
#   include_qa   - if True, include QA files (default False)

# Broad search: find all GSLC data over an area (any track, any direction)
all_gslcs = find_nisar(
    aoi=[-115, 43, -114, 44],
    start_date="2025-06-01",
    end_date="2025-12-01",
    product_type="GSLC",
)

# Narrow search: specific track and direction for time-series analysis
track_77 = find_nisar(
    aoi=[-115, 43, -114, 44],
    start_date="2025-06-01",
    end_date="2025-12-01",
    product_type="GSLC",
    path_number=77,
    direction="ASCENDING",
)

# Search for GUNW interferograms instead of SLCs
gunws = find_nisar(
    aoi=[-115, 43, -114, 44],
    start_date="2025-11-01",
    end_date="2026-02-01",
    product_type="GUNW",
)

# Download in parallel with automatic HDF5 validation
fps = download_urls(track_77, "local/gslcs/")

Read a Single File

from nisar_pytools import open_nisar
from nisar_pytools.utils.metadata import get_slc, get_orbit_info, get_acquisition_time

dt = open_nisar("NISAR_L2_PR_GSLC_...h5")

# Quick access to a polarization channel
hh = get_slc(dt, polarization="HH")           # lazy, CRS set
hv = get_slc(dt, polarization="HV")
hh_b = get_slc(dt, polarization="HH", frequency="frequencyB")

# Metadata without navigating the tree
print(get_acquisition_time(dt))  # 2025-11-03 12:46:15
print(get_orbit_info(dt))        # {'track_number': 77, 'frame_number': 24, ...}
print(hh.rio.crs)                # EPSG:32611

# Or access the full DataTree directly
freq_a = dt["science/LSAR/GSLC/grids/frequencyA"].dataset

Stack GSLCs into a Time Series

from nisar_pytools import stack_gslcs

# Stack multiple same-track GSLCs into a (time, y, x) DataArray
stack = stack_gslcs(
    ["gslc_date1.h5", "gslc_date2.h5", "gslc_date3.h5"],
    frequency="frequencyA",
    polarization="HH",
)
# Sorted by time, grid-validated, dask-backed, CRS assigned

Basic SAR Processing

from nisar_pytools.processing import (
    interferogram, coherence, multilook, unwrap, calculate_phase
)

# Interferogram (validates matching grids)
ifg = interferogram(slc1, slc2)
ifg_aa = interferogram(slc1, slc2, antialias="range")  # ISCE3 RSLC convention
ifg_2d = interferogram(slc1, slc2, antialias="2d")     # symmetric, right for GSLCs

# Multilooked interferogram
ml_ifg = multilook(ifg, looks_y=4, looks_x=4)

# Coherence estimation
coh = coherence(slc1, slc2, window_size=11)

# Phase unwrapping with SNAPHU
unw, conncomp = unwrap(ifg, coh, nlooks=20.0)

Crossmul antialiasing

interferogram() accepts an antialias argument with three modes:

antialias	What it does	When to use
False (default)	Naive slc1 * conj(slc2)	Followed by multilook (which absorbs alias noise). Fast, dask-friendly.
'range'	FFT 2× upsample → multiply → mean-of-pairs along axis=1 only. Matches the production NISAR crossmul workflow (isce3.signal.Crossmul) to numerical roundoff. Output amplitude is ~1× naive.	RSLCs in radar coordinates, where range is critically sampled and azimuth is over-sampled.
'2d'	Same algorithm applied symmetrically along both axes. Output amplitude is ~1× naive.	GSLCs (or any product on a projected x–y grid) where the SAR bandlimit is rotated diagonally and neither axis is privileged.

The naive (default) and 'range' outputs differ at full resolution by ~0.75 rad phase std on a representative GSLC chip; after 16×16 multilook the residual drops below the science noise floor at L-band, so the default is fine for the typical RSLC→multilook→unwrap pipeline. Set antialias only if you need the full-resolution wrapped phase clean of alias noise.

Note: isce3.signal.CrossMultiply (a separate, simpler ISCE3 class with a numpy-array API) uses multilookSummed instead of mean and gives 2× amplitude. We deliberately match the NISAR production Crossmul (mean) so coherence calculations stay bounded to [0, 1].

Coherence — sliding window vs multilooked

Two coherence functions for two use cases:

from nisar_pytools.processing import coherence, multilook_coherence

# Sliding-window estimate at SLC resolution (boxcar or gaussian)
coh = coherence(slc1, slc2, window_size=11)

# ISCE3 production-style: non-overlapping mean blocks, output on the
# multilooked grid -- matches RIFG/RUNW/GUNW coherenceMagnitude bands
coh_ml = multilook_coherence(slc1, slc2, looks_y=16, looks_x=5)
coh_aa = multilook_coherence(slc1, slc2, looks_y=16, looks_x=5, antialias="range")

coherence() is for visualization and analysis at full SLC resolution. multilook_coherence() produces the same estimator on the multilooked grid that the ISCE3 GUNW reports — numerator and denominator are kept consistent (both mean-based, both share the same multilook factors), so values stay bounded to [0, 1] regardless of antialias mode.

RSLC → GUNW (production InSAR pipeline)

rslc_to_gunw wraps nisar.workflows.insar to take an L1 RSLC pair and produce an L2 GUNW. The bundled environment.yml installs the full isce3 stack; users on a custom env need at minimum:

mamba install -c conda-forge isce3 libgdal-hdf5 snaphu pygrib pyaps3 raider pysolid netcdf4
pip install 'nisar-pytools[isce3]'   # for the rest via pip if you skip mamba

Minimal call — auto-fetches a Copernicus 30 m DEM, auto-detects the UTM zone + bbox from the reference RSLC, applies the bundled production-spec runconfig (JPL X05010 settings, 5×6 / 13×16 looks, full coregistration, split-spectrum ionosphere on, troposphere off):

from nisar_pytools.processing import rslc_to_gunw

gunw = rslc_to_gunw(
    reference_rslc="NISAR_L1_PR_RSLC_..._20251103...h5",
    secondary_rslc="NISAR_L1_PR_RSLC_..._20251115...h5",
    output_dir="gunw_run/",
)
print(gunw)  # gunw_run/product.h5

More control — supply your own DEM, crop the output, and tweak processing parameters via the merged-with-defaults overrides dict:

gunw = rslc_to_gunw(
    "ref.h5", "sec.h5", "gunw_run/",
    dem_file="dem.tif",
    aoi_bbox_utm=(464000, 4884080, 544000, 4964080),  # (xmin, ymin, xmax, ymax)
    output_epsg=32611,
    overrides={"runconfig": {"groups": {"processing": {
        # Faster + lower-res run: bigger looks, no rubbersheet
        "crossmul":      {"range_looks": 10, "azimuth_looks": 12},
        "phase_unwrap":  {"range_looks": 26, "azimuth_looks": 32},
        "rubbersheet":   {"enabled": False},
        "fine_resample": {"enabled": False},
    }}}},
)

Or pass an entirely user-supplied runconfig (paths and AOI bbox are still auto-injected on top):

gunw = rslc_to_gunw("ref.h5", "sec.h5", "gunw_run/", runconfig="my_runconfig.yaml")

Outputs land in gunw_run/:

• product.h5 — the GUNW
• runconfig.yaml — the resolved runconfig actually fed to the workflow
• scratch/ — intermediate RIFG/RUNW HDF5s, rdr2geo cache, log

Set restart=True to ignore cached step outputs and re-run from scratch (forwarded to nisar.workflows.persistence.Persistence).

Prepare GSLCs for dolphin

dolphin is an open-source InSAR time-series tool (phase linking, unwrapping, network inversion) from the OPERA/ISCE framework. prep_dolphin crops your GSLCs to an AOI, exports them as complex GeoTIFFs, and generates a ready-to-run dolphin config YAML.

from pathlib import Path
from nisar_pytools.processing import prep_dolphin

gslc_files = sorted(Path("gslcs/").glob("NISAR_L2_PR_GSLC_*.h5"))

config = prep_dolphin(
    gslc_paths=gslc_files,
    out_dir="dolphin_run/",
    aoi_wgs84=(-110.54, 44.65, -109.63, 45.18),    # Greater Yellowstone
    skip_dates={"20251104"},                          # drop early-season date
    dolphin_overrides=[
        (["phase_linking", "half_window", "y"], 20),
        (["phase_linking", "half_window", "x"], 20),
        (["output_options", "strides", "y"], 5),
        (["output_options", "strides", "x"], 5),
        (["unwrap_options", "unwrap_method"], "spurt"),
    ],
)
# Logs: "Ready. Review the config, then run:
#          dolphin run dolphin_run/dolphin_config.yaml"

You can also use crop_gslc_to_tif standalone to extract a single GSLC:

from nisar_pytools.processing import crop_gslc_to_tif

crop_gslc_to_tif("NISAR_L2_PR_GSLC_...h5", "out.tif",
                 bbox_utm=(540000, 4950000, 560000, 4970000))

Phase Linking

from nisar_pytools.processing import phase_link

# EMI phase linking on a GSLC stack
linked, temporal_coh = phase_link(stack, search_window=11, confidence=0.95)

Polarimetric Decomposition

Requires a quad-pol acquisition (HH + HV + VV). NISAR acquires quad-pol over specific regions — check listOfPolarizations in the frequency group to confirm your data has all three channels. Dual-pol (HH + HV only) data cannot be used for H-A-alpha decomposition.

from nisar_pytools import open_nisar
from nisar_pytools.utils.metadata import get_slc
from nisar_pytools.processing import h_a_alpha

dt = open_nisar("NISAR_L2_PR_GSLC_...h5")

# Extract the three quad-pol channels (must call .compute() for in-memory)
hh = get_slc(dt, "HH").compute()
hv = get_slc(dt, "HV").compute()
vv = get_slc(dt, "VV").compute()  # only available in quad-pol mode

# H-A-alpha decomposition (Cloude-Pottier)
ds = h_a_alpha(hh, hv, vv)
# Returns Dataset with: entropy, anisotropy, alpha, mean_alpha

Local Incidence Angle

import numpy as np
from nisar_pytools import open_nisar
from nisar_pytools.utils.local_incidence_angle import local_incidence_angle

# Open a GSLC or GUNW — both have radarGrid with LOS vectors
dt = open_nisar("NISAR_L2_PR_GSLC_...h5")

# Extract LOS vectors from the radarGrid metadata
rg = dt["science/LSAR/GSLC/metadata/radarGrid"].dataset
los_x = np.asarray(rg["losUnitVectorX"])  # shape: (n_heights, ny, nx)
los_y = np.asarray(rg["losUnitVectorY"])
los_z = np.sqrt(np.maximum(1.0 - los_x**2 - los_y**2, 0.0))  # derive Z

heights = np.asarray(rg.coords["z"])      # height layers
x_rg = np.asarray(rg.coords["x"])         # radarGrid x coordinates
y_rg = np.asarray(rg.coords["y"])         # radarGrid y coordinates
epsg = int(rg.attrs.get("projection"))     # CRS

# Compute LIA using a DEM (must be in projected CRS, same as radarGrid)
lia = local_incidence_angle(dem, los_x, los_y, los_z, heights, x_rg, y_rg, epsg=epsg)
# lia is an xr.DataArray in degrees with CRS set

Roadmap

• Lazy HDF5 reader returning xarray DataTree with CRS
• Prep dolphin to run GSLC to dolphin ready yaml + geotiffs
• ASF search and parallel download with validation
• GSLC time-series stacking
• Interferogram, coherence, multilooking, phase extraction
• Phase unwrapping (SNAPHU)
• Phase linking (EMI with SHP selection)
• Polarimetric decomposition (H-A-alpha)
• Local incidence angle computation
• Visualization helpers (amplitude, phase, interferogram, coherence)
• Support for additional NISAR product types

Contributing

Contributions are welcome! Please fork the repo and open a pull request, or open an issue to suggest improvements.

License

Distributed under the MIT License. See LICENSE.txt for more information.