Version 1
: Received: 21 October 2024 / Approved: 22 October 2024 / Online: 23 October 2024 (17:00:20 CEST)
How to cite:
Pirotti, F.; Toffah, F. . E.; Guarnieri, A. Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR. Preprints2024, 2024101774. https://doi.org/10.20944/preprints202410.1774.v1
Pirotti, F.; Toffah, F. . E.; Guarnieri, A. Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR. Preprints 2024, 2024101774. https://doi.org/10.20944/preprints202410.1774.v1
Pirotti, F.; Toffah, F. . E.; Guarnieri, A. Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR. Preprints2024, 2024101774. https://doi.org/10.20944/preprints202410.1774.v1
APA Style
Pirotti, F., Toffah, F. . E., & Guarnieri, A. (2024). Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR. Preprints. https://doi.org/10.20944/preprints202410.1774.v1
Chicago/Turabian Style
Pirotti, F., Felix Enyimah Toffah and Alberto Guarnieri. 2024 "Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR" Preprints. https://doi.org/10.20944/preprints202410.1774.v1
Abstract
The correlation of periodic seasonal vertical ground movement (SVGM) with climatic data from earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets respectively. Measurement Points (MP) from EGMS over Italy provided a value of ground vertical movement approximately every 6 days. Precipitation and temperature datasets were processed to provide Drought Code (DC) maps calculated ad hoc for this study at 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analysed to assess correlation with Spearman’s rank correlation coefficient (ρ) between this measure and DC from Copernicus CEMS (DCCEMS) and from MODIS+CHIRP (DC1km) and from temperature. Results over the considered area (Italy) showed that 0.46% of all MPs (32 826 MPs out of 7 193 676 MPs) have a ρ greater than 0.7: 12 142 of those have positive correlation, and 20 684 have negative correlation. DC1km was the climatic factor that provided the higher number of correlated MPs, roughly giving +59% more correlated MPs than DCCEMS and +300% than temperature data. If ρ greater than 0.8 was considered, the number of MPs drops by a factor of 10; from 12 142 to 1 275 for positive correlation and from 20 684 to 2 594 for negative correlation between DC1km values and SVGM measurements. Correlations lagged in time resulted in most of the correlated MPs being inside a window of +/- 6 days (a single satellite overpass time). Because DC and temperature are strongly co-linear, further analysis to assess which is better at explaining the seasonality of MPs was carried out, resulting in DC1km significantly explaining more variance of the SVGM than temperature for the inversely correlated points more than the directly correlated points. Spatial distribution of correlated MPs shows that they are unevenly distributed in clusters the Italian territory. This work will lead to further investigation both at local scale and at a pan-European scale. An interactive WebGIS application open to the public is available for data consultation. Data are shared in a public repository for replication of the method.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
