LongonotSenA130
mintpy/pic
geo_velocity.png

geo_temporalCoherence.png

geo_maskTempCoh.png

temporalCoherence.png

maskTempCoh.png

geo_avgSpatialCoh.png

avgSpatialCoh.png

maskConnComp.png

network.png

coherenceHistory.png

coherenceMatrix.png

rms_timeseriesResidual_ramp.png

numTriNonzeroIntAmbiguity.png

numInvIfgram.png

velocity.png

geometryRadar.png

coherence_1.png

coherence_2.png

coherence_3.png

coherence_4.png

coherence_5.png

coherence_6.png

coherence_7.png

unwrapPhase_wrap_1.png

unwrapPhase_wrap_2.png

unwrapPhase_wrap_3.png

unwrapPhase_wrap_4.png

unwrapPhase_wrap_5.png

unwrapPhase_wrap_6.png

unwrapPhase_wrap_7.png

unwrapPhase_1.png

unwrapPhase_2.png

unwrapPhase_3.png

unwrapPhase_4.png

unwrapPhase_5.png

unwrapPhase_6.png

unwrapPhase_7.png

connectComponent_1.png

connectComponent_2.png

connectComponent_3.png

connectComponent_4.png

connectComponent_5.png

connectComponent_6.png

connectComponent_7.png

timeseries_demErr_wrap10_1.png

timeseries_demErr_wrap10_2.png

geo_timeseries_demErr_wrap10_1.png

geo_timeseries_demErr_wrap10_2.png

avgPhaseVelocity.png

pbaseHistory.png

timeseries_wrap10_1.png

timeseries_wrap10_2.png

reference_date.txt
20170730
geo_velocity.kmz
Download file.
LongonotSenA130.template
#####################################################
# If the data are in $TESTDATA_ISCE, run using
# minsarApp.bash /work2/05861/tg851601/stampede2/code/rsmas_insar/samples/unittestGalapagosSenDT128.template --miaplpy --start dem
######################################################
ssaraopt.platform = SENTINEL-1A,SENTINEL-1B
ssaraopt.relativeOrbit = 130
ssaraopt.startDate = 20140601
######################################################
topsStack.boundingBox = -1.53 -0.52 34.24 38.75 # '-1 0.15 -91.6 -90.9'
topsStack.subswath = 1 # '1 2'
topsStack.numConnections = 4 # comment
topsStack.azimuthLooks = 5 # comment
topsStack.rangeLooks = 15 # comment
topsStack.filtStrength = 0.2 # comment
topsStack.unwMethod = snaphu # comment
topsStack.coregistration = geometry # [NESD geometry], auto for NESD
######################################################
mintpy.compute.cluster = local #[local / slurm / pbs / lsf / none], auto for none, cluster type
mintpy.compute.numWorker = 32 #[int > 1 / all], auto for 4 (local) or 40 (non-local), num of workers
mintpy.compute.maxMemory = 128 #[float > 0.0], auto for 4, max memory to allocate in GB
mintpy.reference.lalo = auto # -0.82,-91.14 # S of SN
mintpy.networkInversion.parallel = yes #[yes / no], auto for no, parallel processing using dask
mintpy.troposphericDelay.method = no # pyaps #[pyaps / height_correlation / base_trop_cor / no], auto for pyaps
mintpy.save.hdfEos5 = yes #[yes / update / no], auto for no, save timeseries to UNAVCO InSAR Archive format
mintpy.save.hdfEos5.update = yes #[yes / no], auto for no, put XXXXXXXX as endDate in output filename
mintpy.save.hdfEos5.subset = yes #[yes / no], auto for no, put XXXXXXXX as endDate in output filename
mintpy.save.kmz = yes #[yes / no], auto for yes, save geocoded velocity to Google Earth KMZ file
mintpy.load.autoPath = yes
######################################################
mintpy.networkInversion.minTempCoh = 0.6 # auto for 0.5
minsar.insarmaps_flag = False
minsar.upload_flag = True