# vim: set filetype=cfg: [Ada] partition = "normal" ntaskpernode = "1" nodes = "1" cpuxtask = "1" memxcpu = "30G" mail = "" [Dirs] path = "117" frame = "143" rootdirdata = rootdirwork = rootdirplot = rootdirintf = rootdirouterrpath = [Download] # Polarization: Specifies the polarization ("VV"). polarization = "VV" # Start date: Specifies the start date for downloading ("2014-06-14"). startdate = "2014-06-14" # End date: Specifies the end date for downloading ("2023-12-20"). enddate = "2023-12-20" # sl: An arbitrary parameter in degrees to download a DEM slightly larger than the SAR images. sl = "0.5" [Pre-proc] # tbsln: Maximum temporal baseline for selecting pairs (in days). tbsln = "100" # pbsln: Maximum perpendicular baseline for selecting pairs (in meters). pbsln = "100" [Proc] # Filter wavelength (m): Used to calculate the Gaussian filter to achieve 0.5 gain at this wavelength in the geocoding phase. The GRD file is downsampled to 1/4 of this wavelength. filter_wavelength = "400" # Decimation factor: Set to 1 for full resolution or 2 for coarse resolution. dec_factor = "2" # Range decimation: Value of decimation to be applied in the range direction. range_dec = "16" # Azimuth decimation: Value of decimation to be applied in the azimuth direction. azimuth_dec = "4" # Det_stitch: Enable it if there are clear errors in the subswath merging phase. Det_stitch enables the calculation of stitching position based on the NaN area in the grids. det_stitch = "1" # Threshold mask: Coherence threshold chosen for creating the coherence mask. threshold_mask = "0.15" # Landmask: Enable it when there is a large mirror of waters in the area. landmask = "1" # Rerun: Used to re-run specific pairs that failed in the format: 20170729_20170921,20180302_20180513,20181227_20190213,20191116_2020010. Leave empty to run all the pairs. rerun = "" # Threshold Snaphu: During Snaphu, correlation is reset to zero when it is less than threshold_snaphu. threshold_snaphu = "0.1" # Maximum discontinuity: Allows phase discontinuity in unwrapped phase. This is needed for interferograms having sharp phase jumps. maximum_discontinuity = "0" # Near_interp: Interpolate masked or low coherence pixels with their nearest neighbors. Set to 1 to interpolate; set to others or leave blank to use original phase. near_interp = "1" #time series ######------------------------ smallbaselineApp.cfg ------------------------######### ########## computing resource configuration mintpy.compute.maxMemory = 50 #[float > 0.0], auto for 4, max memory to allocate in GB ########## Load ORBIT_DIRECTION = ascending HEADING = -14.1 #[float], satellite heading angle, measured from the north in clockwise as positive One could open the *.kml file in Google Earth and measure it manually mintpy.load.processor = gmtsar mintpy.load.updateMode = yes mintpy.load.unwFile = /*/unwrap_ll.grd mintpy.load.corFile = /*/corr_ll.grd #mintpy.load.connCompFile = /*/conncomp_ll.grd mintpy.load.demFile = demsampl.grd mintpy.load.incAngleFile = auto #[path of incidence angle file], optional but recommended mintpy.load.azOffStdFile = auto #[path pattern of azimuth offset variance file], optional but recommended mintpy.load.rgOffStdFile = auto #[path pattern of range offset variance file], optional but recommended ########## modify_network ##Network modification based on temporal/perpendicular baselines, date, num of connections etc. mintpy.network.tempBaseMax = auto #[1-inf, no], auto for no, max temporal baseline in days mintpy.network.perpBaseMax = auto #[1-inf, no], auto for no, max perpendicular spatial baseline in meter mintpy.network.connNumMax = auto #[1-inf, no], auto for no, max number of neighbors for each acquisition mintpy.network.startDate = auto #[20090101 / no], auto for no mintpy.network.endDate = auto #[20110101 / no], auto for no mintpy.network.excludeDate = auto #[20080520,20090817 / no], auto for no mintpy.network.excludeIfgIndex = auto #[1:5,25 / no], auto for no, list of ifg index (start from 0) mintpy.network.referenceFile = auto #[date12_list.txt / ifgramStack.h5 / no], auto for no ##Data-driven network modification mintpy.network.coherenceBased = yes #[yes / no], auto for no, exclude interferograms with coherence < minCoherence mintpy.network.areaRatioBased = yes #[yes / no], auto for no, exclude interferograms with area ratio < minAreaRatio mintpy.network.minCoherence = 0.10 #[0.0-1.0], auto for 0.7 mintpy.network.minAreaRatio = 0.15 #[0.0-1.0], auto for 0.75 mintpy.network.keepMinSpanTree = yes #[yes / no], auto for yes, keep interferograms in Min Span Tree network mintpy.network.aoiYX = auto #[y0:y1,x0:x1 / no], auto for no, area of interest for coherence calculation mintpy.network.aoiLALO = auto #[S:N,W:E / no], auto for no - use the whole area ########## reference_point mintpy.reference.yx = auto #[257,151 / auto] mintpy.reference.lalo = 44.84877,11.12316 #[31.8,130.8 / auto] mintpy.reference.maskFile = auto #[filename / no], auto for maskConnComp.h5 mintpy.reference.coherenceFile = auto #[filename], auto for avgSpatialCoh.h5 mintpy.reference.minCoherence = auto #[0.0-1.0], auto for 0.85, minimum coherence for auto method ########## invert_network mintpy.networkInversion.weightFunc = var #[var / fim / coh / no], auto for var mintpy.networkInversion.minNormVelocity = auto #[yes / no], auto for yes, min-norm deformation velocity / phase mintpy.networkInversion.maskDataset = no #[coherence / connectComponent / rangeOffsetStd / azimuthOffsetStd / no], auto for no mintpy.networkInversion.maskThreshold = auto #[0-inf], auto for 0.4 mintpy.networkInversion.minRedundancy = 1 #[1-inf], auto for 1.0, min num_ifgram for every SAR acquisition mintpy.networkInversion.minTempCoh = 0.5 #[0.0-1.0], auto for 0.7, min temporal coherence for mask mintpy.networkInversion.minNumPixel = 100 #[int > 1], auto for 100, min number of pixels in mask above mintpy.networkInversion.shadowMask = auto #[yes / no], auto for yes [if shadowMask is in geometry file] or no. ########## correct_troposphere mintpy.troposphericDelay.method = pyaps #[pyaps / height_correlation / gacos / no], auto for pyaps mintpy.troposphericDelay.weatherModel = ERA5 #[ERA5 / MERRA / NARR], auto for ERA5 mintpy.troposphericDelay.weatherDir = tropo/ mintpy.troposphericDelay.polyOrder = auto #[1 / 2 / 3], auto for 1 mintpy.troposphericDelay.looks = auto #[1-inf], auto for 8, extra multilooking num mintpy.troposphericDelay.minCorrelation = auto #[0.0-1.0], auto for 0 ########## deramp mintpy.deramp = auto #[no / linear / quadratic], auto for no - no ramp will be removed mintpy.deramp.maskFile = auto #[filename / no], auto for maskTempCoh.h5, mask file for ramp estimation ########## correct_topography mintpy.topographicResidual = yes #[yes / no], auto for yes mintpy.topographicResidual.polyOrder = 2 #[1-inf], auto for 2, poly order of temporal deformation model mintpy.topographicResidual.phaseVelocity = no #[yes / no], auto for no - use phase velocity for minimization mintpy.topographicResidual.stepFuncDate = no #[20080529,20190704T1733 / no], auto for no, date of step jump mintpy.topographicResidual.excludeDate = no #[20070321 / txtFile / no], auto for exclude_date.txt mintpy.topographicResidual.pixelwiseGeometry = yes #[yes / no], auto for yes, use pixel-wise geometry info ########## residual_RMS (root mean squares for noise evaluation) mintpy.residualRMS.maskFile = auto #[file name / no], auto for maskTempCoh.h5, mask for ramp estimation mintpy.residualRMS.deramp = auto #[quadratic / linear / no], auto for quadratic mintpy.residualRMS.cutoff = auto #[0.0-inf], auto for 3 ########## velocity mintpy.timeFunc.startDate = auto #[20070101 / no], auto for no mintpy.timeFunc.endDate = auto #[20101230 / no], auto for no mintpy.timeFunc.excludeDate = auto #[exclude_date.txt / 20080520,20090817 / no], auto for exclude_date.txt intpy.timeFunc.polynomial = auto #[int >= 0], auto for 1, degree of the polynomial function mintpy.timeFunc.periodic = auto #[1,0.5 / list_of_float / no], auto for no, periods in decimal years mintpy.timeFunc.stepDate = auto #[20110311,20170908 / 20120928T1733 / no], auto for no, step function(s) mintpy.timeFunc.exp = auto #[20110311,60 / 20110311,60,120 / 20110311,60;20170908,120 / no], auto for no mintpy.timeFunc.log = auto #[20110311,60 / 20110311,60,120 / 20110311,60;20170908,120 / no], auto for no mintpy.timeFunc.uncertaintyQuantification = auto #[residue, covariance, bootstrap], auto for residue mintpy.timeFunc.timeSeriesCovFile = auto #[filename / no], auto for no, time series covariance file mintpy.timeFunc.bootstrapCount = auto #[int>1], auto for 400, number of iterations for bootstrapping