MiamiSenAT48
miaplpy_MDCBeach_201601_202310/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
unwrapPhase_wrap_1.png
unwrapPhase_wrap_2.png
unwrapPhase_wrap_3.png
unwrapPhase_1.png
unwrapPhase_2.png
unwrapPhase_3.png
connectComponent_1.png
connectComponent_2.png
connectComponent_3.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
20160412
geo_velocity.kmz
Download file.
MiamiSenAT48.template
minsar.processor = isce
#cleanopt = 0
ssaraopt.platform = SENTINEL-1A,SENTINEL-1B
ssaraopt.startDate = 20190101
ssaraopt.startDate = 20160401
ssaraopt.endDate = 20231031
ssaraopt.relativeOrbit = 48
topsStack.boundingBox = 25.60 26.20 -80.45 -80.106 #25.4 26.1 -82.0 -80.0
topsStack.subswath = 2 3
topsStack.numConnections = 2
topsStack.azimuthLooks = 5
topsStack.rangeLooks = 20
topsStack.filtStrength = 0.3
topsStack.coregistration = geometry
###### Brickell and Miami Beach #
mintpy.subset.lalo = 25.745:25.9,-80.21:-80.115 # Brickell and Miami Beach
mintpy.reference.lalo = auto # 25.7582,-80.2028 # W Brickel Marquis condo
###### CGrove ##############
mintpy.subset.lalo = 25.725:25.795,-80.262:-80.186 # Coconut Grove and Brickell
mintpy.reference.lalo = auto # 25.7582,-80.2028 # W Brickel Marquis condo
###### Sunny Isles #
mintpy.subset.lalo = 25.8960:26.02,-80.145:-80.1146 # Brickell and Miami Beach
mintpy.reference.lalo = auto # 25.9334,-80.1237 # W Brickel Marquis condo
##### Sara paper
#miaplpy.subset.lalo = 25.8384:25.909,-80.1656:-80.1174 #[S:N,W:E / no], auto for no
###### MiamiBeach
mintpy.subset.lalo = 25.765:25.840,-80.147:-80.1195 #[S:N,W:E / no], auto for no
miaplpy.subset.lalo = 25.765:25.840,-80.147:-80.1195 #[S:N,W:E / no], auto for no
mintpy.reference.lalo = auto # 25.8248,-80.1222 # auto refPointLat=25.82477&refPointLon=-80.12222 # 25.8791,-80.1259 # W Brickel Marquis condo
minsar.miaplpyDir.addition = MiamiBeach #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.load.startDate = 20160901
###### Airport (MIA)
mintpy.subset.lalo = 25.784:25.808,-80.295:-80.261 #[S:N,W:E / no], auto for no
miaplpy.subset.lalo = 25.784:25.808,-80.295:-80.261 #[S:N,W:E / no], auto for no
mintpy.reference.lalo = auto # 25.8248,-80.1222 # auto refPointLat=25.82477&refPointLon=-80.12222 # 25.8791,-80.1259 # W Brickel Marquis condo
minsar.miaplpyDir.addition = Airport #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.load.startDate = 20200101
mintpy.reference.minCoherence = 0.7
###### test
minsar.miaplpyDir.addition = t3aPhaseVelo #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
mintpy.subset.lalo = 25.765:25.840,-80.147:-80.1195 #[S:N,W:E / no], auto for no
miaplpy.subset.lalo = 25.765:25.840,-80.147:-80.1195 #[S:N,W:E / no], auto for no
mintpy.reference.lalo = auto # 25.8248,-80.1222 # auto refPointLat=25.82477&refPointLon=-80.12222 # 25.8791,-80.1259 # W Brickel Marquis condo
miaplpy.load.startDate = 20150921
mintpy.reference.minCoherence = 0.7
mintpy.topographicResidual.phaseVelocity = yes #[yes / no], auto for no - use phase velocity for minimization
#mintpy.topographicResidual.polyOrder = 1 #[1-inf], auto for 2, poly order of temporal deformation model
###### Surfside2 Sara
minsar.miaplpyDir.addition = lalo #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate)
minsar.miaplpyDir.addition = Surfside2 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.8384:25.909,-80.1656:-80.1174 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.8384:25.909,-80.1656:-80.1174 #[S:N,W:E / no], auto for no
mintpy.reference.lalo = auto # 25.8791,-80.1259 # ! Good one
mintpy.reference.lalo = auto #5.8865,-80.1225 # works for 2015_202111 (Sara's time period)
miaplpy.load.startDate = 20150921
###### Surfside
minsar.miaplpyDir.addition = Surfside #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.8384:25.909,-80.147:-80.1174 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.8384:25.909,-80.147:-80.1174 #[S:N,W:E / no], auto for no
#mintpy.reference.lalo = 25.8755,-80.1266 # ! Good One
#mintpy.reference.lalo = 25.8791,-80.1259 # ! Good one
mintpy.reference.lalo = auto # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
###### MiamiBeach Sunny Isles
minsar.miaplpyDir.addition = MDCBeach #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.8384:25.909,-80.147:-80.1174 #[S:N,W:E / no], auto for no
#mintpy.reference.lalo = 25.8791,-80.1259 # ! Good one
mintpy.reference.lalo = auto # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
###### Southern Surfside ######
minsar.miaplpyDir.addition = SSurfside #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.871:25.92,-80.123:-80.119 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.871:25.92,-80.123:-80.119 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20160101
mintpy.reference.lalo = auto # 6/2023: updated because out of mask
###### Southern Surfside ######
minsar.miaplpyDir.addition = SSurfside3 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.871:25.90,-80.123:-80.119 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.871:25.90,-80.123:-80.119 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20190101
mintpy.reference.lalo = auto # 6/2023: updated because out of mask
###### MineBlasting
minsar.miaplpyDir.addition = Blasting #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
mintpy.subset.lalo = 25.85:26.00,-80.42:-80.22 # 25.774:25.897,-80.3857:-80.2569 # Coconut Grove
miaplpy.subset.lalo = 25.85:26.00,-80.42:-80.22 # 25.774:25.897,-80.3857:-80.2569 # Coconut Grove
mintpy.reference.lalo = auto # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
miaplpy.load.startDate = auto
###### Miami-Dade County Beaches ######
minsar.miaplpyDir.addition = MDCBeach #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20160101
miaplpy.load.endDate = 20231031
mintpy.reference.lalo = auto # 25.9334,-80.1237 # W Brickel Marquis condo
#mintpy.reference.lalo = 25.874,-80.123 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
#mintpy.reference.lalo = 25.88009,-80.12318 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
#mintpy.reference.lalo = 25.87374,-80.12274 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
###### SouthernSurfside ######
minsar.miaplpyDir.addition = SSurfside #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.8715:25.879,-80.122:-80.1205 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.8715:25.879,-80.122:-80.1205 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20190101
miaplpy.load.endDate = 20231031
mintpy.reference.lalo = auto # 25.9334,-80.1237 # W Brickel Marquis condo
###### SouthernSurfside ######
minsar.miaplpyDir.addition = SSurfside2 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.820:25.92,-80.14:-80.1205 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.820:25.92,-80.14:-80.1205 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20190101
miaplpy.load.endDate = 20231031
mintpy.reference.lalo = auto # 25.9334,-80.1237 # W Brickel Marquis condo
###### Miami-Dade County Beaches ######
minsar.miaplpyDir.addition = MDCBeach #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 25.765:25.98,-80.147:-80.1146 #[S:N,W:E / no], auto for no
miaplpy.load.startDate = 20160101
miaplpy.load.endDate = 20231031
mintpy.reference.lalo = auto # 25.9334,-80.1237 # W Brickel Marquis condo
#mintpy.reference.lalo = 25.874,-80.123 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
#mintpy.reference.lalo = 25.88009,-80.12318 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
#mintpy.reference.lalo = 25.87374,-80.12274 # 25.8865,-80.1225 # works for 2015_202111 (Sara's time period)
#############################################
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 = sequential # qq
miaplpy.interferograms.networkType = mini_stacks # qq
miaplpy.interferograms.networkType = delaunay # network
miaplpy.interferograms.delaunayBaselineRatio = 4 # [1, 4, 9] Ratio between perpendiclar and temporal baselines, auto for 4
miaplpy.interferograms.connNum = 6 # Number of connections in sequential interferograms, auto for 3
miaplpy.inversion.rangeWindow = 24 # range window size for searching SHPs, auto for 15
miaplpy.inversion.azimuthWindow = 7 # azimuth window size for searching SHPs, auto for 15
#############################################
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 = DS # [PS,DS,PSDS,geo,all], miaplpy dataset to ingest (Default: geo) (MintPy is always geo)
#insarmaps_flag = True