From 67b7624ae9cf44bd807bf805c8ac12244c42fe29 Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Fri, 26 Jul 2024 16:36:48 +0800 Subject: [PATCH 1/4] `iono_split_spectrum`: hardwire dset while inverting for iono TS (#1239) + iono_split_spectrum: - hardwire dataset name to "unwrapPhase" while inverting for iono TS - remove the template file input, so it can ignore the template inputs, such as the "unwrapPhase_bridging" if one turns on the PU error correction. + defaults.smallbaselineApp.cfg: update correct_ionosphere reference, to be consistent with iono_split_spectrum.py help message. --- src/mintpy/defaults/smallbaselineApp.cfg | 5 ++++- src/mintpy/iono_split_spectrum.py | 3 ++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/src/mintpy/defaults/smallbaselineApp.cfg b/src/mintpy/defaults/smallbaselineApp.cfg index 1ba6fc89a..82f56464a 100644 --- a/src/mintpy/defaults/smallbaselineApp.cfg +++ b/src/mintpy/defaults/smallbaselineApp.cfg @@ -206,7 +206,10 @@ mintpy.solidEarthTides = auto #[yes / no], auto for no ########## 7. correct_ionosphere (optional but recommended) ## correct ionospheric delay [need split spectrum results from ISCE-2 stack processors] -## reference: Fattahi et al. (2017, IEEE-TGRS); Liang et al. (2017 IEEE-TGRS; 2018 IEEE-TGRS) +## reference: +## ISCE-2 topsApp/topsStack: Liang et al. (2019, IEEE-TGRS) +## ISCE-2 stripmapApp/stripmapStack: Fattahi et al. (2017, IEEE-TGRS) +## ISCE-2 alos2App/alosStack: Liang et al. (2018, IEEE-TGRS) mintpy.ionosphericDelay.method = auto #[split_spectrum / no], auto for no mintpy.ionosphericDelay.excludeDate = auto #[20080520,20090817 / no], auto for no mintpy.ionosphericDelay.excludeDate12 = auto #[20080520_20090817 / no], auto for no diff --git a/src/mintpy/iono_split_spectrum.py b/src/mintpy/iono_split_spectrum.py index d43256c47..adc0d909a 100644 --- a/src/mintpy/iono_split_spectrum.py +++ b/src/mintpy/iono_split_spectrum.py @@ -35,9 +35,10 @@ def run_iono_split_spectrum(inps): reference_point.main(cmd.split()[1:]) # 3. estimate iono time-series + # hardwire "--dset unwrapPhase" to ignore dataset name change from unwrapping error correction options print('\n'+'-'*80) print('Estimate ionospheric delay time-series via ifgram_inversion.py ...') - cmd = f'ifgram_inversion.py {inps.iono_stack_file} -t {inps.template_file} -w no --update' + cmd = f'ifgram_inversion.py {inps.iono_stack_file} --dset unwrapPhase --weight-func no --update' print(cmd) ifgram_inversion.main(cmd.split()[1:]) From 22a1c4a5f92eec6dc456969178524bc27fd76b05 Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Fri, 26 Jul 2024 17:12:05 +0800 Subject: [PATCH 2/4] docs: switch fig links to github page - part 2 (#1241) + docs: switch the image src links from Yunjun's obsolete wordpress website to the github page of insarlab/figs, for better independency --- docs/QGIS.md | 4 ++-- docs/README.md | 2 +- docs/_layouts/default_tactile.html | 2 +- docs/api/colormaps.md | 4 ++-- docs/api/coord.md | 2 +- docs/dask.md | 2 +- docs/demo_dataset.md | 10 +++++----- docs/google_earth.md | 6 +++--- docs/hdfeos5.md | 2 +- 9 files changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/QGIS.md b/docs/QGIS.md index df10f3aba..e62c06efe 100644 --- a/docs/QGIS.md +++ b/docs/QGIS.md @@ -10,11 +10,11 @@ ramp_color('RdBu', scale_linear(VEL, -20, 20, 0, 1)) ```

- +

5. Click the [PS Time Series Viewer logo](https://gitlab.com/faunalia/ps-speed/blob/master/icons/logo.png) to activate the tool, and click/play on the map to display the time-series!

- +

diff --git a/docs/README.md b/docs/README.md index 73fb0d4ce..6f999629a 100644 --- a/docs/README.md +++ b/docs/README.md @@ -51,7 +51,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/FernandinaSenDT128.txt ```

- +

Results are plotted in **./pic** folder. To explore more data information and visualization, try the following scripts: diff --git a/docs/_layouts/default_tactile.html b/docs/_layouts/default_tactile.html index dcc0755f6..9c26b3dc7 100644 --- a/docs/_layouts/default_tactile.html +++ b/docs/_layouts/default_tactile.html @@ -18,7 +18,7 @@

- +

diff --git a/docs/api/colormaps.md b/docs/api/colormaps.md index d3b9a9144..a405a1b4c 100644 --- a/docs/api/colormaps.md +++ b/docs/api/colormaps.md @@ -9,7 +9,7 @@ MintPy support the following colormaps: We recommend to use cyclic colormap `cmy` for wrapped phase/displacement measurement.

- +

To use colormap `cmy` in view.py: @@ -64,7 +64,7 @@ The following colormaps is included by default: + More at [Scientific Color-Maps](http://www.fabiocrameri.ch/colourmaps.php) ([Crameri, 2018](https://doi.org/10.5194/gmd-11-2541-2018))

- +

### Interactive [web tool](https://jdherman.github.io/colormap/) to generate custom colormaps by Jon Herman ### diff --git a/docs/api/coord.md b/docs/api/coord.md index 8294ef2e3..965c60ed3 100644 --- a/docs/api/coord.md +++ b/docs/api/coord.md @@ -11,4 +11,4 @@ Y_UNIT degrees X/Y_FIRST are the longitude/latitude value of the first (upper left corner) pixel’s upper left corner, as shown below: - + diff --git a/docs/dask.md b/docs/dask.md index e2ffef464..f9753a0d6 100644 --- a/docs/dask.md +++ b/docs/dask.md @@ -54,7 +54,7 @@ A typical run time without local cluster is 30 secs and with 8 workers 11.4 secs To show the run time improvement, we test three datasets (South Isabela, Fernandina, and Kilauea) with different number of cores and same amount of allocated memory (4 GB) on a compute node in the [Stampede2 cluster's skx-normal queue](https://portal.tacc.utexas.edu/user-guides/stampede2#overview-skxcomputenodes). Results are as below: -![Dask LocalCluster Performance](https://yunjunzhang.files.wordpress.com/2020/08/dask_local_cluster_performance.png) +![Dask LocalCluster Performance](https://insarlab.github.io/figs/docs/mintpy/dask_local_cluster_performance.png) #### 1.5 Known problems #### diff --git a/docs/demo_dataset.md b/docs/demo_dataset.md index 532dee82c..1ed4ef7bc 100644 --- a/docs/demo_dataset.md +++ b/docs/demo_dataset.md @@ -14,7 +14,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/FernandinaSenDT128.txt ```

- +

Relevant literature: @@ -35,7 +35,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/SanFranSenDT42.txt ```

- +

Relevant literature: @@ -69,7 +69,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/SanFranBaySenD42.txt ```

- +

Relevant literature: @@ -90,7 +90,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/WellsEnvD2T399.txt ```

- +

Relevant literature: @@ -124,7 +124,7 @@ smallbaselineApp.py ${MINTPY_HOME}/docs/templates/KujuAlosAT422F650.txt ```

- +

Relevant literature: diff --git a/docs/google_earth.md b/docs/google_earth.md index 0727e56c2..d075d46f0 100644 --- a/docs/google_earth.md +++ b/docs/google_earth.md @@ -5,7 +5,7 @@ MintPy use [pyKML](https://pythonhosted.org/pykml/) to generate KMZ (Keyhole Mar `save_kmz_timeseries.py` takes 3D displacement time-series file and outputs a KMZ file with interactive time-seires plot.

- +

[Download KMZ file](https://miami.box.com/v/FernandinaSenDT128TS) @@ -15,7 +15,7 @@ MintPy use [pyKML](https://pythonhosted.org/pykml/) to generate KMZ (Keyhole Mar `save_kmz.py` takes any 2D matrix and outputs a KMZ file with a overlay image.

- +

[Download KMZ file](https://miami.box.com/v/FernandinaSenDT128VEL) @@ -35,7 +35,7 @@ The script samples the input 3D dataset at 3 levels of details by default (`--st The low- and moderate-resolution LODs cover the entire region, while the high-resolution LOD covers only the actively deforming regions. These regions (red boxes below) are currently identified as boxes having >20% pixels with velocity magnitude > the global velocity median absolute deviation (`mintpy.save_kmz_timeseries.get_boxes4deforming_area`).

- +

2. Region-based Network Links diff --git a/docs/hdfeos5.md b/docs/hdfeos5.md index be05436c7..a40a11f25 100644 --- a/docs/hdfeos5.md +++ b/docs/hdfeos5.md @@ -90,4 +90,4 @@ HDF-EOS5 file format is used as the input of the University of Miami's web viewe

http://insarmaps.miami.edu

-[![InSAR Web Viewer](https://yunjunzhang.files.wordpress.com/2019/06/web_viewer_kujualosat422.png)](http://insarmaps.miami.edu/) +[![InSAR Web Viewer](https://insarlab.github.io/figs/docs/mintpy/insarmaps-KujuAlosA422.png)](http://insarmaps.miami.edu/) From 75c56080f440a132efb32813e5f9cd2ff097e639 Mon Sep 17 00:00:00 2001 From: Zhang Yunjun Date: Mon, 29 Jul 2024 16:05:25 +0800 Subject: [PATCH 3/4] gnss: rename `JPL-SIDESHOW` to `SIDESHOW` (#1240) + utils.arg_utils.add_gnss_argument(): rename --gnss-source JPL-SIDESHOW to SIDESHOW for simplicity + objects.gnss: use "SIDESHOW" instead of "JPL-SIDESHOW" or "JPL_SIDESHOW" for all variable names and values + objects.gnss: add reference info in the code comments for GNSS_UNR/SIDESHOW, did not find citation/reference info for GNSS_ESESES, thus, did not add it yet. + docs.references.md: update to reflect the current references noted in the smallbaselineApp.cfg file. Unrelated changes: + utils.network.simulate_coherence(): convert SNR from dB to 1 for thermal decorrelation + README: fix broken link --- docs/README.md | 2 +- docs/references.md | 68 +++++++++++++++++++++++------------ src/mintpy/objects/gnss.py | 43 ++++++++++++++-------- src/mintpy/utils/arg_utils.py | 7 ++-- src/mintpy/utils/network.py | 4 +-- 5 files changed, 81 insertions(+), 43 deletions(-) diff --git a/docs/README.md b/docs/README.md index 6f999629a..600ee5405 100644 --- a/docs/README.md +++ b/docs/README.md @@ -112,4 +112,4 @@ _This disclaimer was adapted from the [MetPy project](https://github.com/Unidata Yunjun, Z., Fattahi, H., and Amelung, F. (2019), Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, _Computers & Geosciences_, _133_, 104331. [ [doi](https://doi.org/10.1016/j.cageo.2019.104331) \| [arxiv](https://doi.org/10.31223/osf.io/9sz6m) \| [data](https://doi.org/10.5281/zenodo.3464190) \| [notebook](https://github.com/geodesymiami/Yunjun_et_al-2019-MintPy) ] -In addition to the above, we recommend that you cite the original publications that describe the algorithms used in your specific analysis. They are noted briefly in the [default template file](../mintpy/defaults/smallbaselineApp.cfg) and listed in the [references.md file](./references.md). +In addition to the above, we recommend that you cite the original publications that describe the algorithms used in your specific analysis. They are noted briefly in the [default template file](../src/mintpy/defaults/smallbaselineApp.cfg) and listed in the [reference file](./references.md). diff --git a/docs/references.md b/docs/references.md index b918d0ee7..374479713 100644 --- a/docs/references.md +++ b/docs/references.md @@ -1,45 +1,67 @@ -## Revalent Scientific Papers +## Revalent Literature -+ Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, _Geoscience and Remote Sensing, IEEE Transactions on, 40_(11), 2375-2383, doi:10.1109/TGRS.2002.803792. ++ Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. _IEEE Transactions on Geoscience and Remote Sensing, 40_(11), 2375-2383. doi:[10.1109/TGRS.2002.803792](https://doi.org/10.1109/TGRS.2002.803792) -+ Chaussard, E., F. Amelung, and Y. Aoki (2013), Characterization of open and closed volcanic systems in Indonesia and Mexico using InSAR time series, _Journal of Geophysical Research: Solid Earth, 118_(8), 3957-3969, doi:10.1002/jgrb.50288. ++ Blewitt, G., Hammond, W., & Kreemer, C. (2018). Harnessing the GPS data explosion for interdisciplinary science. _Eos, 99_. doi:[10.1029/2018EO104623](https://doi.org/10.1029/2018EO104623) -+ Chaussard, E., R. Bürgmann, H. Fattahi, R. M. Nadeau, T. Taira, C. W. Johnson, and I. Johanson (2015), Potential for larger earthquakes in the East San Francisco Bay Area due to the direct connection between the Hayward and Calaveras Faults, _Geophysical Research Letters, 42_(8), 2734-2741, doi:10.1002/2015GL063575. ++ Chaussard, E., Amelung, F., & Aoki, Y. (2013). Characterization of open and closed volcanic systems in Indonesia and Mexico using InSAR time series. _Journal of Geophysical Research: Solid Earth, 118_(8), 3957-3969. doi:[10.1002/jgrb.50288](https://doi.org/10.1002/jgrb.50288) -+ Chen, C. W., and H. A. Zebker (2001), Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization, _JOSA A, 18_(2), 338-351, doi:10.1364/JOSAA.18.000338. ++ Chaussard, E., Bürgmann, R., Fattahi, H., Nadeau, R. M., Taira, T., Johnson, C. W., & Johanson, I. (2015). Potential for larger earthquakes in the East San Francisco Bay Area due to the direct connection between the Hayward and Calaveras Faults. _Geophysical Research Letters, 42_(8), 2734-2741. doi:[10.1002/2015GL063575](https://doi.org/10.1002/2015GL063575) -+ Doin, M. P., C. Lasserre, G. Peltzer, O. Cavalié, and C. Doubre (2009), Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models, _Journal of Applied Geophysics, 69_(1), 35-50, doi:10.1016/j.jappgeo.2009.03.010. ++ Chen, C. W., & Zebker, H. A. (2001). Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization. _Journal of the Optical Society of America A, 18_(2), 338-351. doi:[10.1364/JOSAA.18.000338](https://doi.org/10.1364/JOSAA.18.000338) -+ Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, _Geoscience and Remote Sensing, IEEE Transactions on, 51_(7), 4249-4259, doi:10.1109/TGRS.2012.2227761. ++ Doin, M. P., Lasserre, C., Peltzer, G., Cavalié, O., & Doubre, C. (2009). Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models. _Journal of Applied Geophysics, 69_(1), 35-50. doi:[10.1016/j.jappgeo.2009.03.010](https://doi.org/10.1016/j.jappgeo.2009.03.010) -+ Fattahi, H., and F. Amelung (2015), InSAR bias and uncertainty due to the systematic and stochastic tropospheric delay, _Journal of Geophysical Research: Solid Earth, 120_(12), 8758-8773, doi:10.1002/2015JB012419. ++ Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. _Statistical science_, 54-75. doi:[10.1214/ss/1177013815](https://doi.org/10.1214/ss/1177013815) -+ Fattahi, H., P. Agram, and M. Simons (2016), A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis, _IEEE Transactions on Geoscience and Remote Sensing, 55_(2), 777-786, doi:10.1109/TGRS.2016.2614925. ++ Fattahi, H., & Amelung, F. (2013). DEM Error Correction in InSAR Time Series. _IEEE Transactions on Geoscience and Remote Sensing, 51_(7), 4249-4259. doi:[10.1109/TGRS.2012.2227761](https://doi.org/10.1109/TGRS.2012.2227761) -+ Jolivet, R., R. Grandin, C. Lasserre, M. P. Doin, and G. Peltzer (2011), Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data, _Geophysical Research Letters, 38_(17), L17311, doi:10.1029/2011GL048757. ++ Fattahi, H., & Amelung, F. (2015). InSAR bias and uncertainty due to the systematic and stochastic tropospheric delay. _Journal of Geophysical Research: Solid Earth, 120_(12), 8758-8773. doi:[10.1002/2015JB012419](https://doi.org/10.1002/2015JB012419) -+ Jolivet, R., P. S. Agram, N. Y. Lin, M. Simons, M. P. Doin, G. Peltzer, and Z. Li (2014), Improving InSAR geodesy using global atmospheric models, _Journal of Geophysical Research: Solid Earth, 119_(3), 2324-2341, doi:10.1002/2013JB010588. ++ Fattahi, H., Agram, P., & Simons, M. (2016). A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis. _IEEE Transactions on Geoscience and Remote Sensing, 55_(2), 777-786. doi:[10.1109/TGRS.2016.2614925](https://doi.org/10.1109/TGRS.2016.2614925) -+ Marinkovic, P., and Y. Larsen (2013), Consequences of long-term ASAR local oscillator frequency decay - An empirical study of 10 years of data, paper presented at _Proceedings of the Living Planet Symposium_ (abstract), European Space Agency, Edinburgh, U. K. ++ Fattahi, H., Simons, M., & Agram, P. (2017). InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique. _IEEE Transactions on Geoscience and Remote Sensing, 55_(10), 5984-5996. doi:[10.1109/TGRS.2017.2718566](https://doi.org/10.1109/TGRS.2017.2718566) -+ Morales Rivera, A. M., F. Amelung, and P. Mothes (2016), Volcano Deformation Survey over the Northern and Central Andes with ALOS InSAR Time Series, _Geochemistry, Geophysics, Geosystems_, 17, 2869-2883, doi:10.1002/2016GC006393. ++ Gomba, G., Parizzi, A., Zan, F. D., Eineder, M., & Bamler, R. (2016). Toward Operational Compensation of Ionospheric Effects in SAR Interferograms: The Split-Spectrum Method. _IEEE Transactions on Geoscience and Remote Sensing, 54_(3), 1446-1461. doi:[10.1109/TGRS.2015.2481079](https://doi.org/10.1109/TGRS.2015.2481079) -+ Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms, _Geoscience and Remote Sensing, IEEE Transactions on, 44_(9), 2374-2383, doi:10.1109/TGRS.2006.873207. ++ Heflin, M., Donnellan, A., Parker, J., Lyzenga, G., Moore, A., Ludwig, L. G., et al. (2020). Automated Estimation and Tools to Extract Positions, Velocities, Breaks, and Seasonal Terms From Daily GNSS Measurements: Illuminating Nonlinear Salton Trough Deformation. _Earth and Space Scien10.1029/2011JB008731ce, 7_(7), e2019EA000644, doi:[10.1029/2019EA000644](https://doi.org/10.1029/2019EA000644) -+ Perissin, D., and T. Wang (2012), Repeat-pass SAR interferometry with partially coherent targets, _Geoscience and Remote Sensing, IEEE Transactions on, 50_(1), 271-280, doi:10.1109/tgrs.2011.2160644. ++ Hetland, E., Musé, P., Simons, M., Lin, Y., Agram, P., & DiCaprio, C. (2012). Multiscale InSAR time series (MInTS) analysis of surface deformation. _Journal of Geophysical Research: Solid Earth, 117_(B2). doi:[10.1029/2011JB008731](https://doi.org/10.1029/2011JB008731) -+ Rosen, P. A., S. Hensley, G. Peltzer, and M. Simons (2004), Updated repeat orbit interferometry package released, _Eos Trans. AGU, 85_(5), 47-47, doi:10.1029/2004EO050004. ++ Jolivet, R., Grandin, R., Lasserre, C., Doin, M. P., & Peltzer, G. (2011). Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. _Geophysical Research Letters, 38_(17), L17311. doi:[10.1029/2011GL048757](https://doi.org/10.1029/2011GL048757) -+ Rosen, P. A., E. Gurrola, G. F. Sacco, and H. Zebker (2012), The InSAR scientific computing environment, paper presented at _EUSAR 2012_, 23-26 April 2012. ++ Jolivet, R., Agram, P. S., Lin, N. Y., Simons, M., Doin, M. P., Peltzer, G., & Li, Z. (2014). Improving InSAR geodesy using global atmospheric models. _Journal of Geophysical Research: Solid Earth, 119_(3), 2324-2341. doi:[10.1002/2013JB010588](https://doi.org/10.1002/2013JB010588) -+ Tough, R. J. A., D. Blacknell, and S. Quegan (1995), A Statistical Description of Polarimetric and Interferometric Synthetic Aperture Radar Data, _Proceedings: Mathematical and Physical Sciences, 449_(1937), 567-589, doi:10.1098/rspa.1995.0059. ++ Kang, Y., Lu, Z., Zhao, C., Xu, Y., Kim, J.-w., & Gallegos, A. J. (2021). InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California. _Remote Sensing of Environment, 258_, 112400. doi:[10.1016/j.rse.2021.112400](https://doi.org/10.1016/j.rse.2021.112400) -+ Werner, C., U. Wegmüller, T. Strozzi, and A. Wiesmann (2000), Gamma SAR and interferometric processing software, paper presented at _Proceedings of the ERS-Envisat symposium_, Gothenburg, Sweden. ++ Liang, C., Liu, Z., Fielding, E. J., & Bürgmann, R. (2018). InSAR Time Series Analysis of L-Band Wide-Swath SAR Data Acquired by ALOS-2. _IEEE Transactions on Geoscience and Remote Sensing, 56_(8), 4492-4506. doi:[10.1109/TGRS.2018.2821150](https://doi.org/10.1109/TGRS.2018.2821150) -+ Yu, C., Z. Li, and N. T. Penna (2018), Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model, Remote Sensing of Environment, 204, 109-121, doi:10.1016/j.rse.2017.10.038. ++ Liang, C., Agram, P., Simons, M., & Fielding, E. J. (2019). Ionospheric Correction of InSAR Time Series Analysis of C-band Sentinel-1 TOPS Data. _IEEE Transactions on Geoscience and Remote Sensing, 59_(9), 6755 - 6773. doi:[10.1109/TGRS.2019.2908494](https://doi.org/10.1109/TGRS.2019.2908494) -+ Yu, C., Z. Li, N. T. Penna, and P. Crippa (2018), Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations, Journal of Geophysical Research: Solid Earth, 123(10), 9202-9222, doi:10.1029/2017JB015305. ++ Marinkovic, P., & Larsen, Y. (2013). Consequences of long-term ASAR local oscillator frequency decay - An empirical study of 10 years of data. Paper presented at _Proceedings of the Living Planet Symposium_ (abstract), European Space Agency, Edinburgh, UK. -+ Yunjun, Z., H. Fattahi, and F. Amelung (2019), Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, _Computers & Geosciences, 133_, 104331, doi:10.1016/j.cageo.2019.104331. ++ Milbert, D. (2018), "solid: Solid Earth Tide", [Online]. Available: http://geodesyworld.github.io/SOFTS/solid.htm. Accessd on: 2020-09-06. -+ Zebker, H. A., P. A. Rosen, and S. Hensley (1997), Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps, _Journal of Geophysical Research: Solid Earth, 102_(B4), 7547-7563, doi:10.1029/96JB03804. ++ Morales Rivera, A. M., Amelung, F., & Mothes, P. (2016). Volcano Deformation Survey over the Northern and Central Andes with ALOS InSAR Time Series. _Geochemistry, Geophysics, Geosystems, 17_, 2869-2883. doi:[10.1002/2016GC006393](https://doi.org/10.1002/2016GC006393) + ++ Pepe, A., & Lanari, R. (2006). On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms. _IEEE Transactions on Geoscience and Remote Sensing, 44_(9), 2374-2383. doi:[10.1109/TGRS.2006.873207](https://doi.org/10.1109/TGRS.2006.873207) + ++ Perissin, D., & Wang, T. (2012). Repeat-pass SAR interferometry with partially coherent targets. _IEEE Transactions on Geoscience and Remote Sensing, 50_(1), 271-280. doi:[10.1109/tgrs.2011.2160644](https://doi.org/10.1109/tgrs.2011.2160644) + ++ Rosen, P. A., Hensley, S., Peltzer, G., & Simons, M. (2004). Updated repeat orbit interferometry package released. _Eos Trans. AGU, 85_(5), 47-47. doi:[10.1029/2004EO050004](https://doi.org/10.1029/2004EO050004) + ++ Rosen, P. A., Gurrola, E., Sacco, G. F. & Zebker, H. (2012). The InSAR scientific computing environment. Paper presented at _EUSAR 2012_, Nuremberg, Germany. + ++ Seymour, M. S., & Cumming, I. G. (1994). Maximum likelihood estimation for SAR interferometry. Paper presented at the _Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium_. doi:[10.1109/IGARSS.1994.399711](https://doi.org/10.1109/IGARSS.1994.399711) + ++ Tough, R. J. A., Blacknell, D., & Quegan, S. (1995). A Statistical Description of Polarimetric and Interferometric Synthetic Aperture Radar Data. _Proceedings: Mathematical and Physical Sciences, 449_(1937), 567-589. doi:[10.1098/rspa.1995.0059](https://doi.org/10.1098/rspa.1995.0059) + ++ Werner, C., Wegmüller, U., Strozzi, T., & Wiesmann, A. (2000). Gamma SAR and interferometric processing software. Paper presented at the _Proceedings of the ERS-Envisat symposium_, Gothenburg, Sweden. + ++ Yu, C., Li, Z., Penna, N. T., & Crippa, P. (2018). Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations. _Journal of Geophysical Research: Solid Earth, 123_(10), 9202-9222. doi:[10.1029/2017JB015305](https://doi.org/10.1029/2017JB015305) + ++ Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. _Computers & Geosciences, 133_, 104331. doi:[10.1016/j.cageo.2019.104331](https://doi.org/10.1016/j.cageo.2019.104331) + ++ Yunjun, Z., Fattahi, H., Pi, X., Rosen, P., Simons, M., Agram, P., & Aoki, Y. (2022). Range Geolocation Accuracy of C-/L-Band SAR and its Implications for Operational Stack Coregistration. IEEE Transactions on Geoscience and Remote Sensing, 60, 5227219. doi:[10.1109/TGRS.2022.3168509](https://doi.org/10.1109/TGRS.2022.3168509) + ++ Zebker, H. A., Rosen, P. A., & Hensley, S. (1997). Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps. _Journal of Geophysical Research: Solid Earth, 102_(B4), 7547-7563. doi:[10.1029/96JB03804](https://doi.org/10.1029/96JB03804) diff --git a/src/mintpy/objects/gnss.py b/src/mintpy/objects/gnss.py index 6d26b28e8..51b6cc4c3 100644 --- a/src/mintpy/objects/gnss.py +++ b/src/mintpy/objects/gnss.py @@ -21,10 +21,10 @@ from mintpy.utils import ptime, readfile, time_func, utils1 as ut GNSS_SITE_LIST_URLS = { - 'UNR' : 'http://geodesy.unr.edu/NGLStationPages/DataHoldings.txt', - 'ESESES' : 'http://garner.ucsd.edu/pub/measuresESESES_products/Velocities/ESESES_Velocities.txt', - 'JPL-SIDESHOW' : 'https://sideshow.jpl.nasa.gov/post/tables/table2.html', - 'GENERIC' : None, + 'UNR' : 'http://geodesy.unr.edu/NGLStationPages/DataHoldings.txt', + 'ESESES' : 'http://garner.ucsd.edu/pub/measuresESESES_products/Velocities/ESESES_Velocities.txt', + 'SIDESHOW' : 'https://sideshow.jpl.nasa.gov/post/tables/table2.html', + 'GENERIC' : None, } GNSS_SOURCES = list(GNSS_SITE_LIST_URLS.keys()) @@ -64,8 +64,8 @@ def search_gnss(SNWE, start_date=None, end_date=None, source='UNR', site_list_fi sites = read_UNR_site_list(site_list_file) elif source == 'ESESES': sites = read_ESESES_site_list(site_list_file) - elif source == 'JPL-SIDESHOW': - sites = read_JPL_SIDESHOW_site_list(site_list_file) + elif source == 'SIDESHOW': + sites = read_SIDESHOW_site_list(site_list_file) elif source == 'GENERIC': sites = read_GENERIC_site_list(site_list_file) @@ -143,7 +143,7 @@ def read_UNR_site_list(site_list_file:str): def read_ESESES_site_list(site_list_file:str): - """Return names and lon/lat values for JPL GNSS stations. + """Return names and lon/lat values for JPL/SOPAC ESESES GNSS stations. """ fc = np.loadtxt(site_list_file, skiprows=17, dtype=str) sites = { @@ -154,8 +154,8 @@ def read_ESESES_site_list(site_list_file:str): return sites -def read_JPL_SIDESHOW_site_list(site_list_file:str): - """Return names and lon/lat values for JPL-SIDESHOW GNSS stations. +def read_SIDESHOW_site_list(site_list_file:str): + """Return names and lon/lat values for JPL SIDESHOW GNSS stations. """ fc = np.loadtxt(site_list_file, comments='<', skiprows=9, dtype=str) sites = { @@ -348,8 +348,8 @@ def get_gnss_class(source:str): return GNSS_UNR elif source == 'ESESES': return GNSS_ESESES - elif source == 'JPL-SIDESHOW': - return GNSS_JPL_SIDESHOW + elif source == 'SIDESHOW': + return GNSS_SIDESHOW elif source == 'GENERIC': return GNSS_GENERIC else: @@ -791,6 +791,11 @@ class GNSS_UNR(GNSS): at University of Nevada, Reno (UNR). Website: http://geodesy.unr.edu/NGLStationPages/GlobalStationList + + Reference: + Blewitt, G., Hammond, W., & Kreemer, C. (2018). Harnessing the GPS data + explosion for interdisciplinary science. Eos, 99. doi:10.1029/2018EO104623 + """ def __init__(self, site: str, data_dir=None, version='IGS14', url_prefix=None): super().__init__( @@ -1037,17 +1042,25 @@ def read_displacement(self, start_date=None, end_date=None, print_msg=True, disp self.std_e, self.std_n, self.std_u) -class GNSS_JPL_SIDESHOW(GNSS): - """GNSS class for daily solutions processed by JPL-SIDESHOW. +class GNSS_SIDESHOW(GNSS): + """GNSS class for daily solutions processed by JPL SIDESHOW, + funded by NASA's Space Geodesy Task. Website: https://sideshow.jpl.nasa.gov/pub/ + https://sideshow.jpl.nasa.gov/post/series.html + + Reference: + Heflin, M., Donnellan, A., Parker, J., Lyzenga, G., Moore, A., Ludwig, L. G., et al. + (2020). Automated Estimation and Tools to Extract Positions, Velocities, Breaks, and + Seasonal Terms From Daily GNSS Measurements: Illuminating Nonlinear Salton Trough + Deformation. Earth and Space Science, 7(7), e2019EA000644, doi:10.1029/2019EA000644 """ def __init__(self, site: str, data_dir=None, version='IGS14', url_prefix=None): super().__init__( site=site, data_dir=data_dir, version=version, - source='JPL-SIDESHOW', + source='SIDESHOW', url_prefix=url_prefix, ) @@ -1068,7 +1081,7 @@ def get_site_lat_lon(self, print_msg=False) -> (float, float): Returns: self.lat/lon - float """ # need to refer to the site list - site_list_file = os.path.basename(GNSS_SITE_LIST_URLS['JPL-SIDESHOW']) + site_list_file = os.path.basename(GNSS_SITE_LIST_URLS['SIDESHOW']) # find site in site list file with open(site_list_file) as site_list: diff --git a/src/mintpy/utils/arg_utils.py b/src/mintpy/utils/arg_utils.py index a82eb8a49..b7c713a8a 100644 --- a/src/mintpy/utils/arg_utils.py +++ b/src/mintpy/utils/arg_utils.py @@ -278,8 +278,11 @@ def add_gnss_argument(parser): gnss.add_argument('--show-gnss','--show-gps', dest='disp_gnss', action='store_true', help='Show UNR GNSS location within the coverage.') gnss.add_argument('--gnss-source','--gnss-src','--gps-source', dest='gnss_source', default='UNR', - choices={'UNR', 'ESESES', 'JPL-SIDESHOW', 'GENERIC'}, - help='Source of the GNSS displacement solution (default: %(default)s).') + choices={'UNR', 'SIDESHOW', 'ESESES', 'GENERIC'}, + help='Source of the GNSS displacement solution (default: %(default)s).\n' + 'UNR : Nevada Geodetic Lab at Univ. of Nevada, Reno (Blewitt et al., 2018, Eos)\n' + 'SIDESHOW : Jet Propulsion Lab (JPL) GNSS time series (Heflin et al., 2020, ESS)\n' + 'ESESES : Enhanced Solid Earth Science ESDR System (ESESES) by JPL and SOPAC') # compare GNSS with InSAR gnss.add_argument('--gnss-comp','--gps-comp', dest='gnss_component', diff --git a/src/mintpy/utils/network.py b/src/mintpy/utils/network.py index cb7f27560..754ff04c3 100644 --- a/src/mintpy/utils/network.py +++ b/src/mintpy/utils/network.py @@ -244,7 +244,7 @@ def simulate_coherence_v2(date12_list, decor_time=200.0, coh_resid=0.2, inc_angl date_list = sorted(list(set(date1s + date2s))) tbase_list = ptime.date_list2tbase(date_list)[0] - SNR = 22 # NESZ = -22 dB from Table 1 in https://sentinels.copernicus.eu/web/sentinel/ + SNR = 10 ** (22 / 10) # NESZ = -22 dB from Table 1 in https://sentinels.copernicus.eu/web/sentinel/ coh_thermal = 1. / (1. + 1./SNR) # bperp @@ -314,7 +314,7 @@ def simulate_coherence(date12_list, baseline_file='bl_list.txt', sensor_name='En tbase_list = ptime.date_list2tbase(date_list)[0] # Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4) - SNR = 19.5 # hardwired for Envisat (Guarnieri, 2013) + SNR = 10 ** (19.5 / 10) # hardwired for Envisat (Guarnieri, 2013) coh_thermal = 1. / (1. + 1./SNR) pbase_c = critical_perp_baseline(sensor_name, inc_angle) From c03694d288b7233a138ce94d858a52816e52fe3c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 30 Jul 2024 10:57:28 +0800 Subject: [PATCH 4/4] =?UTF-8?q?[pre-commit.ci]=20pyupgrade:=20v3.16.0=20?= =?UTF-8?q?=E2=86=92=20v3.17.0=20(#1244)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/asottile/pyupgrade: v3.16.0 → v3.17.0](https://github.com/asottile/pyupgrade/compare/v3.16.0...v3.17.0) --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5d195b1b3..cf291d243 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -36,7 +36,7 @@ repos: '--combine-as'] - repo: https://github.com/asottile/pyupgrade - rev: "v3.16.0" + rev: "v3.17.0" hooks: - id: pyupgrade name: modernize python