**1. Introduction**

Land subsidence (LS) is a major worldwide hazard, and it is defined as the downward, mainly vertical, displacement of the Earth's surface relative to a stable reference level [1,2]. LS is caused by a wide variety of processes of natural and anthropogenic origin. The natural-driven processes, such as glacial isostatic adjustment (GIA) [3,4], tectonic movements (except co-seismic displacement) [5,6], and sediment compaction [7,8], often cause a slow and steady motion (a few mm/yr). Human activities that cause subsidence include withdrawal of groundwater [9–12], hydrocarbons [13–15], geothermal water, and brine [16–18]; mining [19–21]; loading of engineered structures [22,23]; and wetland drainage [24]. Generally, the observed rates of human-induced subsidence greatly exceed the rates of natural subsidence, reaching centimeters per year, to even meters per year (e.g., mining activities [25]). LS damage to urban and civil infrastructure causes constant and considerable economic losses. However, the most notable impact of LS is produced in coastal areas, coastal plains, and river deltas, where LS increases flood vulnerability (flood frequency, inundation depth, and duration of floods) [26–28]. Identifying LS-prone areas and estimating their rate and spatial extension is essential in this phenomenon's assessment and management.

The use of satellite data and remote sensing (RS) techniques is a common practice in Earth surface observations. The advantages of satellite RS techniques are their comprehensive area coverage, non-invasiveness, and cost-effectiveness. In particular, the differential interferometric synthetic aperture radar (DInSAR) technique has become an effective RS

tool for monitoring and assessing the Earth surface displacements induced by a variety of geophysical and geological processes, including earthquakes, volcanoes, landslides, LS, and sinkholes, among others [29]. The DInSAR technique is based on acquiring complex SAR images over the same area at different times using repeated passes. The standard DInSAR approach (or conventional DInSAR) exploits the phase difference of the SAR image pairs, providing a measurement of surface displacements occurring between the two acquisitions with a sub-centimetric accuracy and a decametric spatial resolution (e.g., [30–32]).

The uncertainties in the measurement of conventional DInSAR, due to the contribution of non-displacement signals, such as the digital elevation model (DEM) and orbitals errors, and atmospheric delay, are the handicaps of this approach [33]. In addition, the temporal and geometrical decorrelation limit its practical applications [34]. Advanced-DInSAR techniques, based on large stacks composed of many SAR images, partly overcome DInSAR limitations (e.g., [35–37]). Despite the considerable advances in DInSAR processing techniques, applying DInSAR for displacement measurements in areas where the conditions of the land surface change significantly, e.g., densely vegetated areas, remains challenging.

Tabasco is an oil-rich state located in the southeast of Mexico; its northern border runs along the Gulf of Mexico. Much of the state is a wide alluvial coastal plain, the so-called Tabasco Coastal Plain (TCP). Due to its climatic and hydro-geologic conditions, Tabasco is one of the most flood-prone Mexican states [38,39]. The state's high incidence of floods has been exacerbated by sea-level rises and possibly LS, through natural or anthropogenic effects. LS is not considered a high-risk phenomenon in the Tabasco state. The LS phenomenon has been poorly investigated, and its effects on the increase of TCP area's vulnerability to flooding and coastal erosion is unknown. Hydrocarbon production is the main economic activity in the region, with more than a thousand wells distributed in 106 oil fields, so the possibility of significant anthropogenic subsidence occurrence cannot be discarded and must be investigated in detail.

DInSAR techniques have proven practical LS detection and monitoring tools in coastal areas (e.g., [26,40–45]). However, to the authors' knowledge, there are not formal papers published or submitted to journals where DInSAR was applied to investigate LS in Tabasco. Early DInSAR results for the Tabasco region were published only as a conference paper [46]. Therefore, this study evaluates DInSAR's potential for land subsidence detection and monitoring in the TCP. Conventional DInSAR and the interferograms stacking procedure (A-DInSAR) were applied to identify the Earth's surface displacement in TCP. Sentinel-1 data from January 2018 to January 2020 were used. The achieved results allowed the identification of land sinking areas during the period covered by the study, which should be the target of more detailed investigations.
