Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

The potential use of time-series Sentinel-1 synthetic aperture radar (SAR) data for rock unit discrimination has never been explored in previous studies. Here, we employed time-series Sentinel-1 data to discriminate Dananhu formation, Xinjiang group, Granite, Wusu group, Xishanyao formation, and Diorite in Xinjiang, China. Firstly, the temporal variation of the backscatter metrics (backscatter coefficient and coherence) from April to October derived from Sentinel-1, was analyzed. Then, the significant differences of the time-series SAR metrics among different rock units were checked using the Kruskal–Wallis rank sum test and Tukey’s honest significant difference test. Finally, random forest models were used to discriminate rock units. As for the input features, there were four groups: (1) time-series backscatter metrics, (2) single-date backscatter metrics, (3) time-series backscatter metrics at VV, and (4) VH channel. In each feature group, there were three sub-groups: backscatter coefficient, coherence, and combined use of backscatter coefficient and coherence. Our results showed that time-series Sentinel-1 data could improve the discrimination accuracy by roughly 9% (from 55.4% to 64.4%), compared to single-date Sentinel-1 data. Both VV and VH polarization provided comparable results. Coherence complements the backscatter coefficient when discriminating rock units. Among the six rock units, the Granite and Xinjiang group can be better differentiated than the other four rock units. Though the result still leaves space for improvement, this study further demonstrates the great potential of time-series Sentinel-1 data for rock unit discrimination.

[1]  Meng Zhang,et al.  Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data , 2019 .

[2]  Ahmed Gaber,et al.  Investigating the use of the dual-polarized and large incident angle of SAR data for mapping the fluvial and aeolian deposits , 2017 .

[3]  Howard A. Zebker,et al.  Decorrelation in interferometric radar echoes , 1992, IEEE Trans. Geosci. Remote. Sens..

[4]  Snehamoy Chatterjee,et al.  Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Andrzej Borkowski,et al.  Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland , 2020, Remote. Sens..

[6]  Fabrizio D'Amico,et al.  Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis , 2021, Sensors.

[7]  Paul M. Mather,et al.  An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills, Sudan , 1998 .

[8]  M. Dehmollaian,et al.  Measuring the surface roughness of geological rock surfaces in SAR data using fractal geometry , 2017 .

[9]  J. Harris,et al.  Integration of spectral, thermal, and textural features of ASTER data using Random Forests classification for lithological mapping , 2017 .

[10]  Roberto Tomás,et al.  Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry , 2016 .

[11]  B. Leblon,et al.  Rock unit discrimination on Landsat TM, SIR-C and Radarsat images using spectral and textural information , 2004 .

[12]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[13]  Richard Gloaguen,et al.  Remote sensing based improvement of the geological map of the Neoproterozoic Ras Gharib segment in the Eastern Desert (NE–Egypt) using texture features , 2015 .

[14]  Le Yu,et al.  Towards automatic lithological classification from remote sensing data using support vector machines , 2010, Comput. Geosci..

[15]  N. Clinton,et al.  A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .

[16]  Rejith Rajan Girija,et al.  Mapping of mineral resources and lithological units: a review of remote sensing techniques , 2019, International Journal of Image and Data Fusion.

[17]  F. Pirajno,et al.  A review of Cu-dominant mineral systems in the Kalatag district, East Tianshan, China , 2020 .

[18]  Qiuming Cheng,et al.  Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China , 2018, Remote. Sens..

[19]  Waldir R. Paradella,et al.  Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region , 2013, Remote. Sens..

[20]  Mahendra K. Pal,et al.  Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers , 2020, Remote. Sens..

[21]  Meng Li,et al.  Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data , 2018, Applied Sciences.

[22]  J. Salisbury,et al.  Emissivity of terrestrial materials in the 3–5 μm atmospheric window☆ , 1992 .

[23]  Dong-Chen He,et al.  Recognition of lithological units in airborne SAR images using new texture features , 1990 .

[24]  Allison K. Thurmond,et al.  Optical-radar-DEM remote sensing data integration for geological mapping in the Afar Depression, Ethiopia , 2006 .

[25]  Ramon F. Hanssen,et al.  Vegetation Characterization through the Use of Precipitation-Affected SAR Signals , 2018, Remote. Sens..

[26]  Jie Zou,et al.  Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage , 2019, Sensors.

[27]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[28]  Arnon Karnieli,et al.  Exploring the utility potential of SAR interferometric coherence images , 1998 .

[29]  A. Harti,et al.  Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications , 2020 .

[30]  W. Bakker,et al.  Measuring rock microstructure in hyperspectral mineral maps , 2019, Remote Sensing of Environment.

[31]  Ana Cláudia Teodoro,et al.  Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco , 2019, ISPRS Int. J. Geo Inf..

[32]  Mehrez Zribi,et al.  Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data , 2014, Remote. Sens..

[33]  Zhiguo Meng,et al.  Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data , 2021, Remote. Sens..

[34]  Kevin K. Williams,et al.  Laboratory and field measurements of the modification of radar backscatter by sand , 2004 .

[35]  Yu-hai Fan,et al.  Application of remote sensing to identify Copper–Lead–Zinc deposits in the Heiqia area of the West Kunlun Mountains, Chinas , 2020, Scientific Reports.

[36]  K. Tansey,et al.  Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data , 2010 .

[37]  Huaqing Chen,et al.  Lithological mapping of East Tianshan area using integrated data fused by Chinese GF-1 PAN and ASTER multi-spectral data , 2018, Open Geosciences.

[38]  Tobias Ullmann,et al.  Surface Roughness Estimation in the Orog Nuur Basin (Southern Mongolia) Using Sentinel-1 SAR Time Series and Ground-Based Photogrammetry , 2020, Remote. Sens..

[39]  Damien Arvor,et al.  Big earth observation time series analysis for monitoring Brazilian agriculture , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[40]  R. Lyon.  Analysis of rocks by spectral infrared emission (8 to 25 microns) , 1965 .

[41]  Surface configuration as an explanation for lithology-related cross-polarized radar image anomalies , 1973 .

[42]  Jonathan Naden,et al.  Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain , 2011 .

[43]  Farouk El-Baz,et al.  Applications of Orbital Imaging Radar for Geologic Studies in Arid Regions: The Saharan Testimony , 2000 .

[44]  Lai Fuqiang,et al.  A lithology identification method for continental shale oil reservoir based on BP neural network , 2018 .

[45]  T. Kusky,et al.  ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt , 2007 .

[46]  Biswajeet Pradhan,et al.  Band Ratios Matrix Transformation (BRMT): A Sedimentary Lithology Mapping Approach Using ASTER Satellite Sensor , 2018, Sensors.

[47]  Xiya Zhang,et al.  Lithological mapping from hyperspectral data by improved use of spectral angle mapper , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[48]  Marc Leblanc,et al.  The use of radar satellite data from multiple incidence angles improves surface water mapping , 2014 .

[49]  F. El-Baz,et al.  Using full-polarimetric SAR data to characterize the surface sediments in desert areas: A case study in El-Gallaba Plain, Egypt , 2015 .