Temporal coherence appears to be fully white

[Pavithra S L]

I am doing SBAS InSAR using MintPy and the temporal coherence is coming as fully white, the average spatial coherence results are fine. Can somebody suggest what may be the reason? I have tried by changing the minimum threshold values also still the same is coming. I am attaching the screenshot of the temporal coherence and also the configuration file  
CONFIG_TXT = f'''# vim: set filetype=cfg:
mintpy.load.processor         = hyp3
mintpy.compute.maxMemory = auto #[float > 0.0], auto for 4, max memory to allocate in GB
ORBIT_DIRECTION = ascending
##---------interferogram datasets:
mintpy.load.unwFile          = {hyp3_dir}/*/*_unw_phase_clipped.tif
mintpy.load.corFile          = {hyp3_dir}/*/*_corr_clipped.tif
mintpy.load.connCompFile    = {hyp3_dir}/*/*_connComp_clipped.tif  #[path pattern of connected components    files], optional but recommended
mintpy.load.intFile         = {hyp3_dir}/*/*_wrapped_phase_clipped.tif
##---------geometry datasets:
mintpy.load.demFile          = {hyp3_dir}/*/*_dem_clipped.tif
mintpy.load.incAngleFile     = {hyp3_dir}/*/*_lv_theta_clipped.tif
mintpy.load.azAngleFile      = {hyp3_dir}/*/*_lv_phi_clipped.tif
mintpy.load.waterMaskFile   = {hyp3_dir}/*/*_water_mask_clipped.tif, optional but recommended

mintpy.network.coherenceBased  = yes  #[yes / no], auto for no, exclude interferograms with coh < minCoh
mintpy.network.minCoherence    = auto #[0.0-1.0], auto for 0.7
mintpy.network.keepMinSpanTree = auto  #[yes / no], auto for yes, keep interferograms in Min Span Tree network
mintpy.network.maskFile        = no #[file name, no], auto for waterMask.h5 or no [if no waterMask.h5 found]
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

mintpy.reference.yx            = auto   #[257,151 / auto]
mintpy.reference.lalo          = auto #[31.8,130.8 / auto]
mintpy.reference.maskFile      = {work_dir}/maskConnComp.h5   #[filename / no], auto for maskConnComp.h5
mintpy.reference.coherenceFile = {work_dir}/avgSpatialCoh.h5   #[filename], auto for avgSpatialCoh.h5
mintpy.reference.minCoherence  = auto #[0.0-1.0], auto for 0.85, minimum coherence for auto method

mintpy.reference.lalo        = 27.662,89.055 #[31.8,130.8 / auto] #[N,E]
mintpy.reference.date        = auto #[reference_date.txt / 20090214 / no], auto for reference_date.txt
mintpy.solidEarthTides       = yes    #[yes / no], auto for no
mintpy.deramp                = quadratic    #[no / linear / quadratic], auto for no - no ramp will be removed
mintpy.deramp.maskFile = {work_dir}/maskTempCoh.h5 #[filename / no], auto for maskTempCoh.h5,
                                                             #mask file for ramp estimation
mintpy.troposphericDelay.method = height_correlation  #[pyaps / height_correlation / gacos / no], auto for pyaps
mintpy.topographicResidual                   = auto  #[yes / no], auto for yes
mintpy.topographicResidual.polyOrder         = auto  #[1-inf], auto for 2, poly order of temporal deformation model
mintpy.topographicResidual.phaseVelocity     = {work_dir}/avgPhaseVelocity.h5  #[yes / no], auto for no - use phase velocity for minimization
mintpy.topographicResidual.stepFuncDate      = auto  #[20080529,20190704T1733 / no], auto for no, date of step jump
mintpy.topographicResidual.excludeDate       = auto  #[20070321 / txtFile / no], auto for exclude_date.txt
mintpy.topographicResidual.pixelwiseGeometry = {work_dir}/inputs/geometryGeo.h5  #[yes / no], auto for yes, use pixel-wise geometry info

mintpy.residualRMS.maskFile = {work_dir}/maskTempCoh.h5  #[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

mintpy.save.hdfEos5          = yes     #[yes / no], auto for no, save time-series to HDF-EOS5 format
mintpy.plot.dpi              = 400  #[int], auto for 150, number of dots per inch (DPI)
mintpy.geocode.laloStep      = auto  #[-0.000555556,0.000555556 / None], auto for None, output resolution in degree
## c. bridging+phase_closure - recommended when there is a small percentage of errors left after bridging
mintpy.unwrapError.method    = auto #[bridging / phase_closure / bridging+phase_closure / no], auto for no
mintpy.unwrapError.waterMaskFile   = auto  #[waterMask.h5 / no], auto for waterMask.h5 or no [if not found]
mintpy.unwrapError.connCompMinArea = auto  #[1-inf], auto for 2.5e3, discard regions smaller than the min size in pixels

## phase_closure options:
## numSample - a region-based strategy is implemented to speedup L1-norm regularized least squares inversion.
##     Instead of inverting every pixel for the integer ambiguity, a common connected component mask is generated,
##     for each common conn. comp., numSample pixels are radomly selected for inversion, and the median value of the results
##     are used for all pixels within this common conn. comp.
mintpy.unwrapError.numSample       = auto  #[int>1], auto for 100, number of samples to invert for common conn. comp.

## bridging options:
## ramp - a phase ramp could be estimated based on the largest reliable region, removed from the entire interferogram
##     before estimating the phase difference between reliable regions and added back after the correction.
## bridgePtsRadius - half size of the window used to calculate the median value of phase difference
mintpy.unwrapError.ramp            = auto  #[linear / quadratic], auto for no; recommend linear for L-band data
mintpy.unwrapError.bridgePtsRadius = auto  #[1-inf], auto for 50, half size of the window around end points

mintpy.load.connCompFile        = {hyp3_dir}/*/*_connComp_clipped.tif  # Path to SNAPHU-generated connected component files
mintpy.networkInversion.minTempCoh  = auto #[0.0-1.0], auto for 0.7, min temporal coherence for mask
'''
print(CONFIG_TXT)
configName = os.path.join(work_dir, "{}.txt".format(proj_name))
configure_template_file(configName, CONFIG_TXT)