Deciphering climate-induced displacement in Somalia: A remote sensing perspective

between 1992 and 2018. SMIa ¼ SMI SMIðavgÞ SMIðstdÞ ð2Þ SMI(avg) represents the long-term average soil moisture and SMI(std) corresponds to its standard deviation and SMI represents one soil moisture image corresponding to a specific date. NDVIa is an indicator of access vegetation health and productivity, and it represents deviations of the NDVI from the long-term mean values. NDVIa ¼ NDVI NDVIðavgÞ NDVIðstdÞ ð3Þ The expressions NDVI(avg) and NDVI(std) denote the long-term averages and standard deviation, respectively. Given the non-existence of the Sentinel-2 data prior to late 2015, the values for NDVI(avg) and NDVI(std) were derived from the Sentinel-2 archive spanning late 2015 to December 2019. It is imperative to note that the utilisation of Landsat data to augment the archive was not deemed viable. This decision was underpinned by the discrepancies in spa- tial and spectral band configurations between the Sentinel-2 and Landsat datasets, making them incompatible for cohesive analysis. Flood. The established methodology for flood mapping capitalises on the variation in backscatter intensity discerned from pre- and post-flood SENTINEL-1 SAR imagery. The SENTINEL-1 imagery was pre-processed using SeNtinel’s Application Platform (SNAP) Tool- box 7.0 to carry out the following steps: (a) Apply orbit file, (b) Ground Range Detected (GRD) Border Noise Removal (border limit: 5000, trim threshold: 1.0), (c) Thermal Noise removal, (d) multi-looking (2 looks in range and 2 in azimuth), (e) speckle filtering (Refined

Lee filter which is an enhancement of the Lee filter in order to provide better preservation of edges, linear features, and texture information, 5x5 window), (f) range Doppler terrain correc- tion using SRTM 1arc sec and geo-coding to WGS84/UTM Zone 38N. The VV polarisation was considered to detect flooding as opposed to HH or VH polarisation [34, 35]. Intriguingly, full-polarisation SAR data extends the capability to apply advanced polarimetric techniques, such as ’target decomposition’, which could potentially refine object detection further than raw polarised data alone. While beyond this paper’s scope, future research could harness these techniques to heighten accuracy, especially in discerning specific objects within flood contexts. Subsequently, pixel-based change detection between the pre-flood event at time (t1) and the post-flood event at time (t2) was performed using the intensity ratio: R ¼ It1ðiÞ It2ðiÞ ð4Þ Where "It1(i)" is the backscatter intensity of pixel "i" in image acquired at “t1”, and "It2(i)" is the corresponding intensity pixel in image acquired at time "t2". The pixel ratio R(i) is a sample of a random process whose Probability Density Function (PDF) for uncorrelated SAR Table 2. ADI classification scheme with three drought levels. Drought level Conditions Interpretation Watch (yellow) SPEI-3 < -1 or SPEI-1 < -2 Low SPEI means precipitation deficit. Warning (orange) SMI < -1 & (SPEI-3 < -1 or SPEI-1 < -2) Precipitation deficit plus a negative anomaly of soil moisture. Alert (red) NDVIa < -1 & (SPEI-3 < -1 or SPEI-1 < -2)

Precipitation deficit plus a negative anomaly of vegetation ‘greenness’. https://doi.org/10.1371/journal.pone.0304202.t002 PLOS ONE Deciphering Climate-induced Displacement in Somalia: A Remote Sensing Perspective PLOS ONE | https://doi.org/10.1371/journal.pone.0304202 August 9, 2024 10 / 27