SanFranSenD115

miaplpy_MissionD_201604_202505/network_delaunay_4/pic

geo_velocity.png

geo_temporalCoherence.png

geo_temporalCoherence_lowpass_gaussian.png

geo_maskTempCoh.png

geo_maskTempCoh_lowpass_gaussian.png

geo_maskPS.png

temporalCoherence.png

temporalCoherence_lowpass_gaussian.png

maskTempCoh.png

maskTempCoh_lowpass_gaussian.png

maskPS.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

unwrapPhase_wrap_1.png

unwrapPhase_wrap_2.png

unwrapPhase_wrap_3.png

unwrapPhase_wrap_4.png

unwrapPhase_1.png

unwrapPhase_2.png

unwrapPhase_3.png

unwrapPhase_4.png

connectComponent_1.png

connectComponent_2.png

connectComponent_3.png

connectComponent_4.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

20210626

geo_velocity.kmz

SanFranSenD115.template

minsar.processor                             = isce
#cleanopt                              = 0
ssaraopt.platform                     = SENTINEL-1A,SENTINEL-1B
ssaraopt.startDate                    = 20160401
ssaraopt.relativeOrbit                = 115

topsStack.subswath                    = 1
topsStack.referenceDate              = 20161026
topsStack.numConnections              = 2
topsStack.azimuthLooks                = 5
topsStack.rangeLooks                  = 20
topsStack.filtStrength                = 0.3
topsStack.coregistration              = geometry
topsStack.excludeDates                = 20230331,20240325,20231102,20240101,20240617
 
###### Mission  District ######
minsar.miaplpyDir.addition           = MissionD                         #[name / lalo / no ]  auto for no (miaply_$name_startDate_endDate)) 
mintpy.subset.lalo                   = 37.785:37.804,-122.412:-122.387    #[S:N,W:E / no], auto for no
miaplpy.subset.lalo                  = 37.785:37.804,-122.412:-122.387    #[S:N,W:E / no], auto for no
miaplpy.load.startDate               = auto
miaplpy.load.endDate                 = auto
mintpy.reference.lalo                = auto    # 25.9334,-80.1237                         # W Brickel Marquis condo
 
#############################################
miaplpy.load.processor                       = isce
#FA: for miaplpy.phase_inversion we should use 40 but for mintpy.phase_to_range 20 as it uses dask and fails with 40
miaplpy.multiprocessing.numProcessor         = 40
miaplpy.timeseries.tempCohType               = auto     # [full, average], auto for full.
miaplpy.timeseries.minTempCoh                = auto     # auto for 0.5
miaplpy.interferograms.networkType           = single_reference    # network
miaplpy.interferograms.networkType           = delaunay    # network
miaplpy.interferograms.delaunayBaselineRatio = 4     # [1, 4, 9] Ratio between perpendiclar and temporal baselines, auto for 4
#############################################
mintpy.load.autoPath                 = yes
mintpy.compute.cluster               = local
mintpy.compute.numWorker             = 20
mintpy.troposphericDelay.method      = no # auto  #[pyaps / height_correlation / no / gacos], auto for pyaps
mintpy.save.hdfEos5                  = yes                        # [yes / update / no], auto for no, save timeseries to UNAVCO InSAR Archive format
mintpy.save.hdfEos5.update           = no                                                # [yes / no], auto for no, put XXXXXXXX as endDate in output filename
mintpy.save.hdfEos5.subset           = yes     #[yes / no], auto for no, put subset range info   in output filename
#mintpy.reference.date               = 20191205   #[reference_date.txt / 20090214 / no], auto for reference_date.txt
#mintpy.topographicResidual.excludeDate = 20161015,20191006  #[20070321 / txtFile / no], auto for exclude_date.txt
#mintpy.unwrapError.method           = bridging  #[bridging / phase_closure / bridging+phase_closure / no], auto for no
#mintpy.unwrapError.connCompMinArea  = 200  #[1-inf], auto for 2.5e3, discard regions smaller than the min size in pixels
#mintpy.reference.yx                 = 349,1393   #[257,151 / auto]
#mintpy.reference.date                = 20191205   #[reference_date.txt / 20090214 / no], auto for reference_date.txt

miaplpy.timeseries.minTempCoh        = 0.7     # auto for 0.5
mintpy.networkInversion.minTempCoh   = 0.7
#############################################
minsar.insarmaps_dataset             = PS       # [PS,DS,PSDS,geo,all], miaplpy dataset to ingest (Default: geo) (MintPy is always geo)
minsar.insarmaps_flag                = true       # [PS,DS,PSDS,geo,all], miaplpy dataset to ingest (Default: geo) (MintPy is always geo)
minsar.upload_flag                   = true       # [PS,DS,PSDS,geo,all], miaplpy dataset to ingest (Default: geo) (MintPy is always geo)