Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought conditions

Abstract

In this work the relationship between climatic indices and seasonal vertical ground motion (SVGM) from earth observation data is investigated. The European Ground Motion Service (EGMS) vertical ground movement measurements provided from 2018 to 2022 are used together with temperature and precipitation data from MODIS and CHIRP datasets respectively. Precipitation and temperature are further processed to provide Drought Code (DC) maps calculated ad hoc for this study at 1 km spatial resolution and daily temporal resolution. Measurement Points (MP) from EGMS over Italy provide a value of ground vertical movement approximately every 6 days. Seasonality is analysed to assess correlation between SVGM and DC from Copernicus CEMS (DC1) and from MODIS+CHIRP (DC2) and from temperature using Spearman's rank correlation coefficient (ρ). Initial results over Italy show that DC2 is significantly better correlated to SVGM than DC1 and temperature, with a stronger median absolute value of ρ of 0.025 and 0.042 respectively for negative and positive correlation scenarios. A total of 1275 MPs have correlation coefficients between DC2 map and EGMS measurements above 0.8 (positive correlation) and 2594 ρ < −0.8 (negative correlation). Correlations lagged in time are also analysed, resulting in most being inside a window of ± 6 days. Because DC and temperature are strongly collinear, further analysis to assess which is better at explaining the seasonality of GM was carried out, resulting in DC2 significantly explaining more variance of the SVGM than temperature for the inversely correlated points more than the directly correlated points. These points are unevenly distributed in the Italian territory, with clusters in some areas that appear to show reliable SVGM-DC correlations. We conclude that further investigation is necessary at a local scale. An interactive web-gis open to the public is available for data consultation and all data are shared in a public repository for full replicability of the method.