Paula M. Burgi Kate E. Allstadt 20250919 raster digital data Reston, VA U.S. Geological Survey data release https://doi.org/10.5066/P1CQG4BN This data release contains optical and radar-based remote sensing products used to assess ground failure and surface deformation triggered by the February 6, 2023, magnitude 7.8 and magnitude 7.5 Kahramanmaraş, Türkiye earthquake doublet and subsequent aftershocks. The datasets span the northern portion of the earthquake rupture zone and were produced to assist with the U.S. Geological Survey (USGS) field response to this event in June 2023, and to more completely detect and map the landslides that occurred. The four included data products, described in detail below, include four different ways to detect change using multispectral surface reflectance and synthetic aperture radar images. Each product contributes to a multi-sensor approach for detecting different types of earthquake-triggered ground failure. The products differ in sensitivity to environmental conditions, ground cover, and failure styles (for example incoherent landslides, coherent slope deformation, or post-seismic creep). Dataset Descriptions (dataset abbreviation in parentheses): Red band difference (Reddiff) Purpose: Highlights surface change due to removal of vegetation or exposure of fresh rock/soil faces, which often has higher red reflectivity, typically associated with incoherent landslides in semi-arid to arid environments. Source: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012) Computation: Reddiff = Redpost – Redpre, where Red is the Sentinel-2 Red band (665nm), the “post” subscript indicates the post-event image, the “pre” subscript indicates the pre-event image, and the “diff” subscript indicates that the resulting difference between pre- and post-event. Input Image Dates: 2022-05-17 and 2023-05-02 2022-07-14 and 2023-07-14 Suggested visualization: Diverging color scheme; suggested range: -1500 to 1500 Guide for user interpretation: Positive values often indicate newly exposed bedrock/soil. Other large positive and negative signals are present in the data, including signals related to cloud cover and vegetation differences. Normalized Difference Vegetation Index [NDVI] difference (NDVIdiff) Purpose: Captures vegetation loss often associated with slope failures in vegetated terrain. Source: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012) Computation: NDVIdiff = NDVIpost – NDVIpre, where Red is the Sentinel-2 Red band (665nm), NIR is the Sentinel-2 Near-infrared band (834nm), and NDVI = (NIR - Red) / (NIR + Red). The “post” subscript indicates the post-event image, the “pre” subscript indicates the pre-event image, and the “diff” subscript indicates that the resulting difference between pre- and post-event. Input Image Dates: 2022-05-17 and 2023-05-02 2022-07-14 and 2023-07-14 Suggested visualization: Diverging color scheme; suggested range: -0.5 to 0 Guide for user interpretation: Negative values often indicate vegetation damage or loss. Optical pixel offset (PXO) Purpose: Measures ground displacement (typically 1s to 10s of meters) related to coherent landslides (i.e. landslides that move as an intact mass, generally maintaining their shape and structure). Source: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012) Products: PXOEW: East-West displacement (meters; +East, –West) PXONS: North-South displacement (meters; +South, –North) PXOcorr: Cross-correlation quality (0–255, 0=low correlation and 255=high correlation) Processing Tools: MicMac (mm3d MM2DPosSism, default parameters) (Rupnik and others, 2017) Input Image Dates: 2022-05-17 and 2023-05-02 2022-07-14 and 2023-07-14 Suggested visualization: Diverging color scale; pixel masking recommended for PXO_corr < 200 Guide for user interpretation: Landslides detected by PXO are generally characterized by coherent deformation patterns that exhibit a sharp contrast from the background values in the surrounding region and are consistent with downslope gravitational movement. InSAR post-seismic velocity (InSAR) Purpose: Captures cm-scale surface motion post-earthquake related to slow landslide reactivation. Source: Sentinel-1 L1 SLC data (Torres and others, 2012) Products: Secular velocity (cm/yr) in the satellite line-of-sight Average coherence (0-1, 0=random noise and 1=no noise). Processing tools: Unwrapped, co-registered interferograms generated using ISCE2 (Rosen and others, 2012) (parameters: range looks = 4, azimuth looks = 1, filter strength = 0, SRTM 30m DEM) Secular velocity derived from time series generated using MintPy (default parameters) (Yunjun and others, 2019) Input image paths/dates: Ascending path 116: 2023-02-28 – 2023-05-23 (7 adjacent interferograms) Descending path 21: 2023-02-10 – 2023-05-17 (7 adjacent interferograms) Suggested visualization: Diverging color scheme; suggested range: -0.4 to 0.4 cm/yr. Pixel masking recommended for average coherence < 0.5. Guide for user interpretation: Landslides detected by InSAR are generally characterized by coherent deformation patterns that exhibit a sharp contrast from the background values in the surrounding region and are consistent with downslope gravitational movement. Note that for the optical-based datasets (Reddiff, NDVIdiff, and PXO), we use post-event dates that are months after the earthquake sequence. This is because persistent snow and or cloud cover was present in earlier imagery. Finally, not all changes and deformation signals are related to earthquake-triggered landslides. The most common noise sources come from clouds and cloud shadows, as well as annual vegetation differences related to, for example, agricultural activity and timing of leaf out. Because the data was acquired using the same viewing geometry and at the same time of year, there is very little noise related to topographic distortion and illumination. In general, landslide-related changes/deformation must exist in both differenced time ranges of a given method, be located on a slope and follow gravitational paths, and not be co-located with common change/deformation sources, such as snow cover, mining, or active agriculture. List of Files Readme.txt NIRP_turkiyeEQ_files.zip: Reddiff_20220517_20230502.tif Reddiff_20220714_20230714.tif NDVIdiff_20220517_20230502.tif NDVIdiff_20220714_20230714.tif PXOEW_20220517_20230502.tif PXONS_20220517_20230502.tif PXOcorr_20220517_20230502.tif PXOEW_20220714_20230714.tif PXONS_20220714_20230714.tif PXOcorr_20220714_20230714.tif InSAR_20230228_20230523.tif InSARcorr_20230228_20230523.tif InSAR_20230210_20230517.tif InSARcorr_20230210_20230517.tif Disclaimer Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government References Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P., 2012, Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services: Remote Sensing of Environment, v. 120, p. 25–36, https://doi.org/10.1016/j.rse.2011.11.026. Rosen, P. A., Gurrola, E., Sacco, G. F., and Zebker, H. A., 2012, The InSAR scientific computing environment: EUSAR 2012, 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany. Rupnik, E., Daakir, M., and Pierrot Deseilligny, M., 2017, MicMac – a free, open-source solution for photogrammetry. Open Geospatial Data, Software and Standards, v. 2, no. 14. https://doi.org/10.1186/s40965-017-0027-2. Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A., Huchler, M., and Rostan, F., 2012, GMES Sentinel-1 mission: Remote Sensing of Environment, v. 120, p. 9–24. https://doi.org/10.1016/j.rse.2011.05.028. Yunjun, Z., Fattahi, H., and Amelung, F, 2019, Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction: Computers & Geosciences, v. 133, no. 104331. https://doi.org/10.1016/j.cageo.2019.104331. Purpose These data were produced to support USGS earthquake and landslide response activities following the February 6, 2023, Kahramanmaraş, Türkiye earthquake sequence and to provide remote sensing products for use in scientific analyses of earthquake-triggered ground failure. Rights Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. These data were produced to support USGS earthquake and landslide response activities following the February 2023 Kahramanmaraş, Türkiye earthquake sequence and to provide remote sensing products for use in scientific analyses of earthquake-triggered ground failure. 20230210 20230714 observed Complete None planned 37.7214 38.6487 38.1794 37.7301 ISO 19115 Topic Category geoscientificInformation USGS Thesaurus landslides landslide susceptibility assessment geographic information systems slope processes topography None Earthquake-triggered landslides February 2023 Kahramanmaraş, Türkiye earthquake sequence Remote sensing USGS Metadata Identifier USGS:68a37279d4be021d0f8b5b4d Common geographic areas Earthquake-triggered landslides February 2023 Kahramanmaraş, Türkiye earthquake sequence Optical remote sensing InSAR Pixel offsets USGS Thesaurus Current none none Paula M Burgi USGS - ROCKY MOUNTAIN REGION Research Geophysicist- Mendenhall mailing and physical

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Golden CO 80401 USA 303-273-8570 pburgi@usgs.gov Sentinel-1, Sentinel-2 Environment as of Metadata Creation: GDAL, ISCE2, MintPy, MicMac Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. No formal attribute accuracy tests were conducted Data follows all logical consistencies and no duplicates were found. Data set is considered complete for the information presented, for the date processed, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted Copernicus, ESA 2023 raster digital data France ESA https://browser.dataspace.copernicus.eu/ Digital and/or Hardcopy 20230517 20230714 ground condition Sentinel-2 data 2023 Copernicus GIS Input Copernicus, ESA 2023 raster digital data France ESA https://browser.dataspace.copernicus.eu/ Digital and/or Hardcopy 20230210 20230523 ground condition Sentinel-1 data 2023 Copernicus L1 SLC raster input Data collection: Data was collected by the European Space Agency's Sentinel-1 and Sentinel-2 satellites between 02/2023 - 07/2023. The data downloaded and processed by USGS was the Harmonized Sentinel-2 Level-2A Surface Reflectance and Sentinel-1 L1 SLC data. The data was downloaded via Copernicus Data Space Ecosystem (https://browser.dataspace.copernicus.eu/) and Alaska Satellite Facility Vertex data search (https://search.asf.alaska.edu/). Sentinel-2 data 2023 Copernicus Sentinel-1 data 2023 Copernicus 20230523 Red diff calculation: The red band change map was derived by subtracting the pre-event red band from the post-event red band (Reddiff = Redpost - Redpre), resulting in a raster that highlights areas of vegetation removal or damage. Positive values indicate higher red-band reflectivity, often associated with fresh rock and soil face exposure. This process was followed for the May and July date pairs. Sentinel-2 data 2023 Copernicus 20230523 NDVI diff calculation: For each dataset, the Normalized Difference Vegetation Index (NDVI) was calculated using the Red and Near Infrared (NIR) bands. The formula used was: NDVI = (NIR - Red) / (NIR + Red) This process was performed separately for the pre-event and post-event imagery. The NDVI change map was derived by subtracting the pre-event NDVI from the post-event NDVI, resulting in a raster that highlights areas of vegetation removal or damage. Negative values indicate vegetation loss or damage. This process was followed for the May and July date pairs. Sentinel-2 data 2023 Copernicus 20230523 To generate the pixel offset product, we use the MicMac software system. We use the following command: "mm3d MM2DPosSism input_image_pre.tif input_image_post.tif Dequant=false". We use gdal_translate to generate georeferenced tif files from the MicMac output, and finally, we multiply the east-west and north-south outputs by 10, to convert them from pixel units to meter units. This process was followed for the May and July date pairs. Sentinel-2 data 2023 Copernicus 20230523 Paula M Burgi USGS - ROCKY MOUNTAIN REGION Research Geophysicist- Mendenhall mailing and physical

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Golden CO 80401 USA 303-273-8500 pburgi@usgs.gov To generate post-seismic InSAR time series, we use two softwares: ISCE2 and MintPy. First, using ISCE2, we process the SLC SAR files such that we generate co-registered, unwrapped interferograms (with associated coherence and connected component files). Interferograms were generated only between adjacent SLC pairs. We used the ISCE2 stackSentinel.py, a filter strength of 0.4, SRTM DEM for corrections, and azimuth and range downlooking of 4 and 1. All other parameters were defaults. The files that MintPy uses to generate time series from unwrapped interferograms are: the unwrapped interferograms, connected components, and coherence. The reference point was set to: 37.966 N, 38.038 E. All other parameters were defaults. No atmospheric corrections were applied. This process was followed for ascending data along track 116, and descending data along track 21. Sentinel-1 data 2023 Copernicus 20230523 Raster Pixel 10327 5024 1 0.000898315 0.000898315 Decimal degrees WGS_1984 WGS 84 6378137.0 298.257223563 Reddiff_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -7686 10123 Reddiff_20220714_20230714.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -4409 12842 NDVIdiff_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -2 1.36 NDVIdiff_20220714_20230714.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -1.03 1.46 PXOEW_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -55 8 PXONS_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -55 7 Reddiff_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -7686 10123 PXOcorr_20220517_20230502.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined 0 255 PXOEW_20220714_20230714.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -110 10 PXONS_20220714_20230714.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -110 56 PXOcorr_20220714_20230714.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined 0 255 InSAR_20230228_20230523.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -0.76 0.61 InSARcorr_20230228_20230523.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined 0 0.98 InSAR_20230210_20230517.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined -0.77 0.64 InSARcorr_20230210_20230517.tif Raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined 0 0.99 U.S. Geological Survey GS ScienceBase mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The suggestions and illustrations included in this data release are intended to improve landslide identification and analysis; however, they do not guarantee that all landslide features will appear in the dataset. The contributors and sponsors of this product do not assume liability for any injury, death, property damage, or other effects of the landslides. Vector Digital Data Set (Point) https://doi.org/10.5066/P1CQG4BN None. No fees are applicable for obtaining the data set. 20250919 Paula Burgi U.S. Geological Survey, Geologic Hazards Science Center Research Geophysicist mailing and physical

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Golden Colorado 80401 USA 303-273-8500 ghsc_metadata@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998