remote sensing

Article Satellite Monitoring of Mass Changes and Ground Subsidence in Sudan’s Oil Fields Using GRACE and Sentinel-1 Data

Nureldin A.A. Gido 1,2,*, Hadi Amin 2 , Mohammad Bagherbandi 1,2 and Faramarz Nilfouroushan 2,3 1 Division of Geodesy and satellite positioning, Royal Institute of Technology (KTH), SE-10044 Stockholm, Sweden; [email protected] 2 Faculty of Engineering and Sustainable Development, University of Gävle, SE-80176 Gävle, Sweden; [email protected] (H.A.); [email protected] (F.N.) 3 Department of Geodetic Infrastructure, Geodata Division, Lantmäteriet, SE-80182 Gävle, Sweden * Correspondence: [email protected]



Received: 3 January 2020; Accepted: 29 May 2020; Published: 2 June 2020


Abstract: Monitoring environmental hazards, owing to natural and anthropogenic causes, is an important issue, which requires proper data, models, and cross-validation of the results. The geodetic satellite missions, for example, the Gravity Recovery and Climate Experiment (GRACE) and Sentinel-1, are very useful in this respect. GRACE missions are dedicated to modeling the temporal variations of the Earth’s gravity field and mass transportation in the Earth’s surface, whereas Sentinel-1 collects synthetic aperture radar (SAR) data, which enables us to measure the ground movements accurately. Extraction of large volumes of water and oil decreases the reservoir pressure and form compaction and, consequently, land subsidence occurs, which can be analyzed by both GRACE and Sentinel-1 data. In this paper, large-scale groundwater storage (GWS) changes are studied using the GRACE monthly gravity field models together with different hydrological models over the major oil reservoirs in Sudan, that is, Heglig, Bamboo, Neem, Diffra, and Unity-area oil fields. Then, we correlate the results with the available oil wells production data for the period of 2003–2012. In addition, using the only freely available Sentinel-1 data, collected between November 2015 and April 2019, the ground surface deformation associated with this oil and water depletion is studied. Owing to the lack of terrestrial geodetic monitoring data in Sudan, the use of GRACE and Sentinel-1 satellite data is very valuable to monitor water and oil storage changes and their associated land subsidence over our region of interest. Our results show that there is a significant correlation between the GRACE-based GWS anomalies (∆GWS) and extracted oil and water volumes. The trend of ∆GWS changes due to water and oil depletion ranged from –18.5 6.3 to –6.2 1.3 mm/year using the CSR GRACE monthly ± ± solutions and the best tested hydrological model in this study. Moreover, our Sentinel-1 SAR data analysis using the persistent scatterer interferometry (PSI) method shows a high rate of subsidence, that is, –24.5 0.85, –23.8 0.96, –14.2 0.85, and –6 0.88 mm/year over Heglig, Neem, Diffra, ± ± ± ± and Unity-area oil fields, respectively. The results of this study can help us to control the integrity and safety of operations and infrastructure in that region, as well as to study the groundwater/oil storage behavior.

Keywords: groundwater; GRACE; hydrological model; oil depletion; land subsidence; InSAR

1. Introduction Assessment of surface mass changes is valuable for natural resources management, hazard preparedness, and food security. Long-term monitoring of water and oil depletions using space-based

Remote Sens. 2020, 12, 1792; doi:10.3390/rs12111792 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1792 2 of 20 data is important for natural resources and hazard management. Gravity Recovery and Climate Experiment (GRACE) data have great potential to estimate total water storage (TWS) and are used comprehensively to study hydrological processes [1,2] and groundwater storage (GWS) changes due to, for example, water depletion [2,3], permafrost thawing [4,5], and vertical land motion [6]. Mass transportation can contribute to vertical land motion, that is, uplift or subsidence. Land subsidence is generally caused by a various set of natural and human causes, for example, mining, extraction of liquids near the Earth surface (e.g., petroleum extraction), permafrost thawing, and seismic activities. However, in some cases, natural processes generate relatively slow vertical movements, for example, land uplift in Fennoscandia and Laurentia [6], where, on the contrary, subsidence due to human activities is relatively rapid [7]. The classical techniques of subsidence monitoring, for example, repeated precise leveling, are extremely time-consuming and expensive, especially in large areas, in addition to their low spatial resolution. However, in contrast, the interferometric synthetic aperture radar (InSAR) technique has been widely used to measure the ground surface deformation caused by different geological and geophysical processes [8]. Development of the time-series algorithms and its positive impact of reducing atmospheric artifacts [9,10] enhanced the contribution of the InSAR to land subsidence associated with groundwater depletion. A key characteristic affecting most environmental studies in Africa is the lack of observations, particularly about sustained time-series data, which gives a considerable rise to uncertainty in the regional statistics. Excessive mass extraction (e.g., water) may result in problems related to water resources and environmental issues such as water resources conflicts and, in some cases, ground subsidence [11,12]. Numerous studies have been carried out recently on groundwater and hydrological aspects using different remote sensing techniques and in situ data. For example, Castellazzi et al. [13] investigated the GRACE/InSAR combination procedure for Central Mexico, where huge groundwater depletion was reported. They concluded that the most notable uncertainty lies in the selection of the GRACE data processing approach. They also presented InSAR-derived aquifer compaction maps. Rahaman et al. [14] proposed, developed, and tested a model to forecast the GRACE-derived groundwater anomalies for sustainable groundwater planning and management in the Colorado River, where a reasonable numerical results are achieved. Bonsor et al. [15] focused on the Nile basin and the use of the groundwater recharge model to interpret the seasonal variations in the terrestrial water storage indicated by the GRACE. Fallatah et al. [16] showed a substantial groundwater depletion that was attributed to both climatic and anthropogenic factors by quantifying the temporal variation of freshwater resources in the arid Arabian Peninsula in addition to identifying factors affecting these resources. Rateb and Kuo [17] explored the occurrences of land subsidence in response to groundwater level changes in the central part of the Tigris–Euphrates basin using GRACE and SAR data. Depletion was computed at a rate of 7.56 km3/yr for the GWS, which resulted in a subsidence − near the city of Baghdad at a rate of 10 mm/yr. Becker et al. [18] jointly analysed the GRACE, satellite − altimetry data, and precipitation, in order to study the spatiotemporal variability of hydrological parameters over the East African Great Lakes region. They found that the TWS change from GRACE and precipitation displays a common mode of variability at an interannual time scale, with a minimum in late 2005, followed by a rise in 2006–2007, which was attributed to the strong 2006 Indian Ocean Dipole (IOD) on East African rainfall. They also showed that the GRACE-based TWS was linked to the El Niño–Southern cycle and the combination of the altimetry data with the TWS allows estimating soil moisture and groundwater volume variations over the lakes drainage basins. Bonsor et al. [19] used GRACE monthly solution provided by three different processing centres to examine the TWS changes in 12 African sedimentary aquifers, to study relationships between the TWS and rainfall, and to estimate the GWS changes using four hydrological models, for example, the variable infiltration capacity model (VIC). They found that there were no continuous long-term decreasing trends in the GWS from 2002 to 2016 in any of the African basins. Du et al. [20] reported their findings based on SAR and GRACE data for monitoring ground surface subsidence owing to groundwater extraction and underground mining activities in the Ordos Basin, China. Ouma et al. [21] integrated GRACE Remote Sens. 2020, 12, 1792 3 of 20 measurements and Global Land Data Assimilation System (GLDAS) models in order to study the inter-annual variations and the GWS changes in the Nzoia River Basin in Kenya. They found that GRACE and GLDAS models could provide reliable data sets suitable for the study of small to large basin GWS variations, especially in areas with limited in situ data. Ahmed et al. [22] analyzed GRACE solutions over North and Central Africa along with relevant data sets (e.g., topography) to assess the utility of GRACE data for monitoring elements of hydrologic systems on local scales. They found that the hydrologic cycle is largely controlled by the estimated mass changes and GRACE data can be used to investigate the temporal local responses of a much larger scale of hydrologic systems. Land subsidence due to oil extraction has also been studied using SAR data. Xu et al. [23] observed 15 cm subsidence in the center of the Lost Hills field Southern California during a 105-day period. They concluded that this surface subsidence is because of the vertical shrinkage of the reservoir, which resulted in the pore pressure drop. Fielding et al. [24] reported subsidence rates as high as 40 mm in 35 days or >400 mm/yr owing to oil extraction in the San Joaquin Valley, California. Tamburini et al. [25] investigated surface deformation by the persistent scatterer InSAR (PSInSAR) method for monitoring of surface deformation in reservoir area because of fluid extraction from, or injection into, subsurface reservoirs. In most cases, oil field subsidence may not cause a large impact on surface structures as underground mining performs. However, the long-term effect of oil fields subsidence on the local ecological environment, the subsurface hydrological system, and the surface drainage pattern can be significant [26]. The objective of this study is to use available observations from different satellite remote sensing techniques to study the solid Earth response to mass transportation owing to current climate change and oil/water extraction in Sudan. Hereinafter, the term Sudan refers to the territory that includes Sudan and South Sudan. The satellite-based data are collected from low-orbited dedicated missions, which provide a homogeneous global coverage. We study the rate and extent of mass change and its corresponding ground subsidence using GRACE and Sentinel-1 SAR data. For this purpose, the mass variations are estimated in terms of the GWS change and surface deformation in the study region. Firstly, large-scale TWS anomalies (∆TWS) are studied using GRACE monthly solutions [27,28]. However, GRACE data suffer from inherent data processing problems and need to be addressed properly to achieve a reliable and accurate product. As satellite gravimetry is not sensitive to the Earth’s geocenter motion, degree one coefficients, which represent the position of the Earth’s instantaneous center of mass relative to an Earth-fixed reference frame, need to be added to the GRACE products by a model of the degree one variations [29]. Independent estimation of geocenter motion that is not quantified in the GRACE solutions is a source of uncertainty in GRACE-based mass change estimation. Moreover, comparing the time series of fluctuations in C20 coefficients observed by GRACE and those achieved by an analysis of satellite laser ranging (SLR) tracking data shows that there are significant differences over time. Therefore, as SLR-based C20 coefficients are more accurate, GRACE-based C20 coefficients should be replaced by those introduced by SLR solutions [30]. Accordingly, the uncertainty of the second degree of zonal spherical harmonic coefficients is another possible source of uncertainty in GRACE-based mass change estimation. Furthermore, as GRACE-derived Stokes coefficients are contaminated by spatially correlated errors [31], especially at smaller wavelengths in which the errors are significantly larger than the signal, surface mass change estimates from GRACE monthly solutions are corrupted by north–south stripe noise [32]. Hence, to filter out the effect of the correlated noises on the surface mass change estimates, one needs to utilize the Stokes coefficients that are decorrelated by applying appropriate filters. In this study, we used different isotropic and non-isotropic filters to filter out the effect of the correlated noises in the monthly-normalized spherical harmonic coefficients. We utilized both non-isotropic DDK filters [33,34] and a conventional Gaussian spatial smoothing function [27,35] with 300 and 500 km smoothing radiuses to investigate the effect of applying different filters on our GWS estimates over regional scales. Another task to be tackled is studying different hydrological models such as GLDAS and the WaterGAP Global Hydrology Model (WGHM) [36,37] for the estimation of ∆GWS. Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 20

production reservoirs in Sudan (see Figure 1). The long record of oil production data, collected from Jan 2003 to Sep 2012, is also utilized in this study. The paper is organized as follows. The study area is briefly summarized in Section 1. The data, such as GRACE, oil wells production record, and SAR data, and the processing methods are presented in Section 2. The results and discussions are provided in Section 3. On the basis of the results, the relation between the oil extraction and the estimated GRACE-based ΔGWS and then the ground subsidence is shown. This will be valuable information as one can use it for improving future hydrological models over the areas with heavy oil productions. Furthermore, these types of studies enrich our knowledge of reservoir behavior and help achieve more efficient reservoir management and prediction of future performance with obvious economic benefits.

Study Area The country of Sudan is approximately 29% desert, 19 % semi-desert, 27% low rainfall savanna, 14% high rainfall savanna, 10% flood regions (swamps and areas affected by floods), and less than 1% mountain vegetation. In recent years, some oil reservoirs have been exploited in Sudan. Our study area is in the oil production fields, located in the south region and lies between latitude 9 to 11 N and longitude 28 to 31 E (see Figure 1). Heglig, Unity, Munga, Bamboo, Diffra, Elnar, Neem, Eltoor, and Toma South are the main oil fields located in Muglad basin, South Kordofan state, and operated by Greater Nile Petroleum Operating Company (GNPOC). It is located within Um Rawaba Remoteformations Sens. 2020 ,and12, 1792Muglad basin, which is the largest of the Central African rift basins. The two large4 of 20 hydrocarbon accumulations, the Unity and Heglig fields, have combined recoverable reserves of 250– 300 million barrels of oil [39]. The climate of this region is hot with seasonal rainfall, which is affected Secondly, the permanent scatterer interferometry (PSI) technique [38] is used to estimate the by the annual shift of the intertropical zone. There are two main seasons in this region. The wet season surface ground deformation rate owing to water/oil depletion and drought in some selected oil begins about the end of April and ends about the late of November and the dry season, which lasts productionfor the rest reservoirs of the year. in SudanThe highest (seeFigure average1). temperature The long record is 43 °C of oilin April production and the data, lowest collected is 33 °C from in JanAugust. 2003 to The Sep soil 2012, type is alsoof the utilized study area in this ranged study. from silty sand in the north part to black cotton to the

FigureFigure 1. 1.(a )(a General) General map map showing showing digital digital elevationelevation of the region region ( (unit:unit: m), m), the the basins basins,, and and the the study study areaarea in Greaterin Greater Nile Nile Petroleum Petroleum Operating Operating Company Company (GNPOC) (GNPOC oil fields) oil fields concessions; concessions and (;b and) the b location) the of thelocation nine of oil the fields nine in oil the fields study inarea the study and their areatotal and extractiontheir total extraction between Janbetween 2003 andJan 2003 Sep 2012and inSep m 3. The2012 footprint in m3. ofThe the footprint three descending of the three Sentinel-1 descending data Sentinel sets over-1 data the sets nine over oil fieldsthe nine is shown oil fields by is rectangles, shown coveringby rectangles, the period covering between the period Nov 2015 between and AprilNov 2015 2019. and April 2019.

2. TheData paper and Methods is organized as follows. The study area is briefly summarized in Section1. The data, such asIn GRACE, this se oilction, wells we production explain the record, four utilized and SAR data data, sets and (i.e. the, GRACE processing data, methods oil wells are production presented in Sectionrecord,2. Thehydrological results and models discussions, and Sentinel are provided-1 SAR data) in Section and the3. On implemented the basis of methods the results, to study the relation mass betweenchange the and oil ground extraction surface and subsidence the estimated owing GRACE-based to oil and water∆GWS depletions. and then According the ground to the subsidence time span is shown.of the Thisavailable will beoil valuabledata, which information is from January as one 2003 can to use September it for improving 2012, thefuture GRACE hydrological and hydrological models overdata the sets areas were with chosen heavy to oil cover productions. the same Furthermore,period. For the these SAR types data, of we studies were enrichlimited our to knowledgeavailable of reservoirSentinel-1behavior data, which and were help freely achieve available more e, ffiandcient had reservoir a good coverage management for our and study prediction area. Sentinel of future- performance with obvious economic benefits.

Study Area The country of Sudan is approximately 29% desert, 19 % semi-desert, 27% low rainfall savanna, 14% high rainfall savanna, 10% flood regions (swamps and areas affected by floods), and less than 1% mountain vegetation. In recent years, some oil reservoirs have been exploited in Sudan. Our study area is in the oil production fields, located in the south region and lies between latitude 9◦ to 11◦ N and longitude 28◦ to 31◦ E (see Figure1). Heglig, Unity, Munga, Bamboo, Di ffra, Elnar, Neem, Eltoor, and Toma South are the main oil fields located in Muglad basin, South Kordofan state, and operated by Greater Nile Petroleum Operating Company (GNPOC). It is located within Um Rawaba formations and Muglad basin, which is the largest of the Central African rift basins. The two large hydrocarbon accumulations, the Unity and Heglig fields, have combined recoverable reserves of 250–300 million barrels of oil [39]. The climate of this region is hot with seasonal rainfall, which is affected by the annual shift of the intertropical zone. There are two main seasons in this region. The wet season begins about the end of April and ends about the late of November and the dry season, which lasts for the rest of the year. The highest average temperature is 43 ◦C in April and the lowest is 33 ◦C in August. The soil type of the study area ranged from silty sand in the north part to black cotton to the south.

2. Data and Methods In this section, we explain the four utilized data sets (i.e., GRACE data, oil wells production record, hydrological models, and Sentinel-1 SAR data) and the implemented methods to study mass change Remote Sens. 2020, 12, 1792 5 of 20 and ground surface subsidence owing to oil and water depletions. According to the time span of the available oil data, which is from January 2003 to September 2012, the GRACE and hydrological data sets were chosen to cover the same period. For the SAR data, we were limited to available Sentinel-1 data, which were freely available, and had a good coverage for our study area. Sentinel-1A was launched on 3 April 2014 and Sentinel-1B on 25 April 2016. Three SAR data sets were used for this study and cover periods of October 2016–April 2019, November 2015–June 2018, and January 2016–March 2019.

2.1. GRACE Data The Earth’s gravity field and shape change with time owing to various processes. Most of the changes in the Earth are secular and periodic. The largest secular mass changes in the Earth are caused by mantle convection, glacial isostatic adjustment, groundwater level change, as well as plate and intraplate motions [40]. Notable periodic variations are the result of the Earth’s rotation (e.g., the pole tide with 14 months’ time period), seasonal variations (e.g., water level change) caused by variations in atmospheric and hydrologic conditions, and tidal variations with various periods. All of the above-mentioned phenomena can affect the Earth’s gravity field, and it can be measured by space geodesy techniques, for example, GRACE mission. The GRACE data greatly complement some climate change-related aspects such as water resource studies because of their medium-wavelength characteristics. Analyses of the GRACE data allow determination of the temporal changes of the Earth’s gravity field to unprecedented accuracy. These data can be used for monitoring various types of mass redistributions in the Earth system, including the ice mass loss, sea level change, groundwater resource variations, and mass variation inside the Earth. Therefore, the GRACE mission is able to provide important information about land hydrology, that is, the sum of land water storage, which consists of surface and groundwater. The existing global hydrological models suffer from poor quality and lack of in situ data in some areas. One of the goals of the GRACE mission with global coverage was to model the hydrology signals and water mass transport to overcome these shortcomings and improve the existing hydrological models. Different institutes are working on the GRACE data for such purposes, such as the Center for Space Research (CSR) at the University of Texas, Austin; Jet Propulsion Laboratory (JPL) in the USA; and the Deutsches GeoForschungsZentrum, German Research Centre for Geosciences (GFZ) in Potsdam, Germany. The time-varying gravity models are useful for studying mass transport in the Earth. The 30-day series of these time-varying models have been released by the above-mentioned processing centers. Many studies have been conducted to compare the GRACE-based ∆TWS with the hydrological models. Wahr et al. [27] used the output from hydrological, oceanographic, and atmospheric models to estimate the variability in the Earth’s gravity field owing to those sources and developed a method for estimating surface mass change from the GRACE solutions. The GRACE can recover changes in the ∆GWS at scales of a few hundred kilometers and larger and at time scales of a few weeks and longer, with accuracies approaching 2 mm in water thickness over land [27]. Yeh et al. [41] studied the TWS change using this mission and concluded that the GRACE-based method of estimating monthly to seasonal GWS changes performs reasonably well at the 200,000 km2 scale of Illinois. Chen et al. [42] investigated the low degree harmonics of the GRACE solutions and found out that GRACE-based water storage changes are in agreement with estimates from NASA’s GLDAS [36] in the Mississippi, Amazon, Ganges, Ob, Zambezi, and Victoria basins.

GRACE Data Analysis and Groundwater Storage Estimation For this study, the most recent released GRACE monthly solutions (RL06) processed by the CSR (ftp://podaac-ftp.jpl.nasa.gov/allData/grace/L2/CSR/RL06/) were used for quantifying monthly ∆TWS over the period of January 2003 to September 2012, using the time series analysis explained in [43]. Because, using GRACE data, one cannot differentiate between the various surface water storage signals (such as soil moisture, groundwater, and precipitation), the obtained secular rate should be filtered by external hydrological models. The integration of GRACE data with the land surface hydrological Remote Sens. 2020, 12, 1792 6 of 20 models, for example, the GLDAS model, allowed to extract the individual components from the equivalent water storage estimated based on the GRACE solutions and lead to the determination of the ∆GWS. The GRACE-based ∆TWS was estimated after applying the following steps. Degree one coefficients were added based on the GRACE Technical Note #13b. Degree 2 coefficients were replaced by those introduced in TN-11_C20_SLR_RL06 (GRACE Technical Note TN-11), consistent with the GRACE SDS recommendations. The maximum degree/order of 96/96 was considered, and seasonal signals were not removed. Furthermore, the gravity data collected by the GRACE require smoothing to reduce the effects of errors present in short-wavelength components. Various methods have been proposed to filter the data [33,44]. For example, isotropic Gaussian [27] and non-isotropic [45] filters are the most popular methods. However, none of these methods account for correlated errors in the data. Owing to the Earth’s mass redistribution (e.g., water/oil depletion), a time-dependent change in the gravity field causes changes in the harmonic coefficients, which can be described by the residual spherical harmonic coefficients ∆Tnm that are obtained by subtracting the mean value Tnm from the GRACE monthly gravity solutions, where Tnm are the numerical values for the potential coefficients. The absolute disturbing potential T and its anomaly (∆T) can be estimated as follows:

n+1 ! nmax  n ! T GM X R X Tnm = Ynm(θ, λ) (1) ∆T R r ∆Tnm n=0 m= n −

∆Tnm = Tnm Tnm (2) − where GM is the geocentric gravitational constant (i.e., the product of Newton’s gravitational constant and the total mass of the Earth, including the atmosphere), R is the Earth’s mean radius, Ynm are the surface spherical harmonics of degree n and order m, and nmax is the maximum degree of harmonic expansion. Using linear regression analysis, for the repeated GRACE satellite tracks, secular trend of the disturbing potential can be determined as follows:

n n+1 n GM XmaxR X ∆·T = ∆T· Y (θ, λ) (3) R r nm nm n=0 m= n −

· where ∆Tnm are the secular changes of the residual spherical harmonic coefficients. According to Wahr et al. [27], the GRACE-based secular trend of the ∆TWS can be achieved by

nXmax Xn · Rρ 2n + 1 · ∆TWS = ∆TnmYnm(θ, λ) (4) 3ρw 1 + kn n=0 m= n − where ρw is the density of fresh water; ρ is the Earth’s mean density; and kn are load Love numbers of degree n, which can be modeled based on some Earth models [46,47]. For extracting ∆GWS, a hydrological model, for example, GLDAS, should be used to estimate for the ∆SWS (surface water storage anomalies) and ∆SM (soil moisture anomalies). Following the below formula, ∆GWS can be estimated.

∆GWS = ∆TWS ∆SWS ∆SM (5) − − where MATLAB scripts were used to analyze GRACE solutions and hydrological models.

2.2. Hydrological Data Land surface model output or hydrological models such as GLDAS, World-Wide Water Resources Assessment (W3RA), and WGHM are very essential tools to compensate for the disturbance Remote Sens. 2020, 12, 1792 7 of 20 factorsRemote Sens. that 20 contaminate20, 12, x FOR PEER the REVIEW GRACE-based estimates of ∆GWS (e.g., soil moisture, precipitation).7 of 20 The models provide global information on land surface meteorological and hydrological status (e.g.,precipitation). surface temperature The models and provide soil moisture) global with information temporal resolutionson land basedsurface on meteorological integrating satellite and andhydrological ground-based status observations.(e.g., surface temperature Different land and surface soil moisture) models with are currentlytemporal resolutions in use, for example,based on GLDASintegrating Version-1 satellite [36 and], specifically ground-based Mosaic, observations. NOAH, variable Different infiltration land surface capacity models (VIC), and are thecurrently common in landuse, modelfor example, (CLM). GLDAS The GLDAS Version Version-2-1 [36] contains, specifically four models,Mosaic, NOAH,NOAH, VIC,variable CLM, infiltration and the catchment capacity land(VIC) surface, and the model common (CLSM). land The m GLDASodel (CLM) models. The have GLDAS different Version spatial-2 resolutions, contains four for example,models, NOAH, 1.0 and 0.25VIC, arc-degrees CLM, and the for catchment NOAH and land 1.0 surface arc-degree model for (CLSM) the others.. The In GLDAS addition, models the layers have different and depths spat areial varied,resolutions, for example, for example, NOAH 1.0 hasandfour 0.25 layersarc-degrees ranging for from NOAH 0 to and 2.0 meter1.0 arc depths-degree [ 36for]. the The others. GLDAS In archivedaddition, datathe layers can be and accessed depths through are varied, http: for//disc.sci.gsfc.nasa.gov example, NOAH has/ .four The layers WGHM rang ising a 0.5 from arc-degree 0 to 2.0 globalmeter hydrologicaldepths [36]. The model, GLDAS which archived was developed data can at be the accessed Centre for through Environmental http://disc.sci.gsfc.nasa.gov/ Systems Research of. theThe University WGHM is of a Kassel,0.5 arc- Germany,degree global in cooperation hydrological with model, the National which Institutewas developed of Public at Healththe Centre and thefor EnvironmentEnvironmental of theSystems Netherlands Research (RIVM) of the [ 37University]. of Kassel, Germany, in cooperation with the NationalIn this Institute study, four of Public different Health data setsand ofthe monthly Environment hydrological of the landNetherlands surface models, (RIVM) the [37 1.0]. arc-degree of GLDASIn this data, study, that four is, NOAHv1.0,different data NOAHv2.1, sets of monthly CLSMv2.0 hydrological (Version-1 land and surface -2 respectively), models, the and1.0 arc the- 0.5degree arc-degree of GLDAS WGHM, data, that were is, utilizedNOAHv1.0, to removeNOAHv2.1, the CLSMv2.0 non-groundwater (Version components-1 and -2 respectively), from the GRACE-basedand the 0.5 arc-degree∆TWS, WGHM, over the were period utilized of January to remove 2003 the to non September-groundwater 2012. components Figure2 shows from the the secularGRACE rate-based of the ΔTWS, sum ofover∆SM the and period∆SWS of Jan obtaineduary 2003 from to the Sep GLDAS-CLSMtember 2012. Figure model 2 over shows the the study secular area. rate of the sum of ΔSM and ΔSWS obtained from the GLDAS-CLSM model over the study area.

Figure 2. Secular rate of the sum of soil moisture anomalies (∆SM) and surface water storage Figure 2. Secular rate of the sum of soil moisture anomalies (ΔSM) and surface water storage anomalies (∆SWS) obtained from Global Land Data Assimilation System catchment land surface model anomalies (ΔSWS) obtained from Global Land Data Assimilation System catchment land surface (GLDAS-CLSM) model for the period of January 2003–September 2012. Unit: cm/yr. model (GLDAS-CLSM) model for the period of January 2003–September 2012. Unit: cm/yr. 2.3. Oil wells Production Data 2.3. Oil wells Production Data Sudan started exporting oil in the year 1999, with an official production of about 400,000 barrels per daySudan until started mid-2006, exporting and reached oil in the about year 500,0001999, with barrels an official per day production at the end of of about 2009 400,000 with aproven barrels reserveper day value until ofmid about-2006 5, billionand reached in 2007 about [39]. The500,000 production barrels per and day exploration at the end have of 2009 been with limited a proven to the Mugladreserve value and Melut of about basins 5 billion (see Figurein 20071 a).[39] The. The Heglig, production Unity, and Munga, exploration Bamboo, have Di beenffra, limited Elnar, Neem, to the Eltoor,Muglad and and Toma Melut South basins oil (see fields Figure are the1a). mainThe Heglig, ones located Unity, inMunga, the Muglad Bamboo, Basin, Diffra, South Elnar, Kordofan Neem, stateEltoor (see, and Figure Toma1b). South The dataoil fields for this are study the main were ones provided located by thein the Greater Muglad Nile Basin, Petroleum South Operating Kordofan Companystate (see Figure (GNPOC), 1b). The who data operate for this the study oil fields. were Oil provi extractionded by the normally Greater generates Nile Petroleum large volumes Operating of producedCompanywater—approximately (GNPOC), who operate 2–10 the barrels oil fields of. water Oil extraction is associated normally with every generates barrel large of oil volumes produced of globallyproduced [39 water]. The— oilapproximately and water separation 2–10 barrels process of water for the is associated nine fields with is performed every barrel in Heglig of oil produced Facilities. Aglobally daily oil [39/water]. The production oil and water record separation in m3 unit, process for the for nine the nine fields fields covering is performed the period inof Heglig Jan 2003 Facilities. to Sep 2012,A daily was oil/water used in production this study. Figurerecord3 inshows m3 unit, the monthlyfor the nine total fields oil and covering water the extractions period of in Jan the 2003 fields. to AsSep can 2012 be, seenwas used in Figure in this1b, study. Heglig Figure has the 3 highershows oiltheproduction monthly to rate,tal oil while and water Toma Southextractions andUnity in the fieldsfields. are As incan the be second seen inand Figure third 1b, places, Heglig respectively. has the higher Owing oil production to political rate, issues while and Toma theseparation South and ofUnity South fields Sudan, areoil in productionthe second dramatically and third places decreased, respectively. after theyear Owing 2012. toThe political estimated issues associated and the separation of South Sudan, oil production dramatically decreased after the year 2012. The estimated associated water to the oil in our fields, for the mentioned period, is about four barrels of water for

Remote Sens. 2020, 12, 1792 8 of 20

waterRemote Sens. to the 2020 oil, 1 in2, x our FOR fields, PEER REVIEW for the mentioned period, is about four barrels of water for each one8 of of oil. 20 Thus, as can be seen in Figure3, almost equal amounts of oil and water are extracted at the beginning eachof the one production, of oil. Thus, and as then can gradually,be seen in waterFigure mass 3, almost extraction equal becameamount dominant.s of oil and water are extracted at the beginning of the production, and then gradually, water mass extraction became dominant.

Figure 3. Monthly oil and water production covering the period of January 2003 2003–September–September 2012 for the nine oil fields fields in the MugladMuglad basin in South Kordofan state. Yellow and blue colors denote for oil 3 and water production, respectively. Unit:Unit: m3.. 2.4. Sentinel-1 Data and Analysis 2.4. Sentinel-1 Data and Analysis Synthetic aperture radar (SAR) is an active side looking radar system that has been used in Synthetic aperture radar (SAR) is an active side looking radar system that has been used in Earth Earth remote sensing and geodesy for more than three decades. Interferometric SAR (InSAR) and remote sensing and geodesy for more than three decades. Interferometric SAR (InSAR) and differential InSAR (DInSAR) methods use the phase difference between radar images acquired at differential InSAR (DInSAR) methods use the phase difference between radar images acquired at different times and geometries and allow generation of digital elevation models and measurements different times and geometries and allow generation of digital elevation models and measurements of the centimeter-scale earth surface movements [10]. The DInSAR method is mostly used when of the centimeter-scale earth surface movements [10]. The DInSAR method is mostly used when measuring large deformations are induced, for example, by earthquakes or volcano activities. For small measuring large deformations are induced, for example, by earthquakes or volcano activities. For deformation, it has considerable limitations, where the most important one is the atmospheric phase small deformation, it has considerable limitations, where the most important one is the atmospheric contribution, which can be mitigated by applying the so-called permanent scatterers interferometry phase contribution, which can be mitigated by applying the so-called permanent scatterers (PSI) [38]. PSI exploits multiple SAR images acquired over the same area, and uses appropriate interferometry (PSI) [38]. PSI exploits multiple SAR images acquired over the same area, and uses data processing and analysis procedures to separate the displacement phase from the other phase appropriate data processing and analysis procedures to separate the displacement phase from the components [48]. The main outcomes of the PSI analysis include the deformation time series and other phase components [48]. The main outcomes of the PSI analysis include the deformation time velocity estimated over the analyzed area. The larger the number of available images, the better the series and velocity estimated over the analyzed area. The larger the number of available images, the quality of the PSI deformation velocity and time-series estimation, for example, at least 15–20 images better the quality of the PSI deformation velocity and time-series estimation, for example, at least 15– for C-band sensors [38]. The PSI method is applicable in many areas, such as disaster monitoring 20 images for C-band sensors [38]. The PSI method is applicable in many areas, such as disaster and risk assessment, caused as a result of landslides and volcanic eruptions, earthquakes, ice sheet monitoring and risk assessment, caused as a result of landslides and volcanic eruptions, earthquakes, motion and glacier flow, and ground subsidence due to subsurface clay deposits [49] or oil and water ice sheet motion and glacier flow, and ground subsidence due to subsurface clay deposits [49] or oil depletion [12,50]. and water depletion [12,50]. In this study, freely available C-band Sentinel-1 A and B data from the European Space Agency In this study, freely available C-band Sentinel-1 A and B data from the European Space Agency (ESA) were used to detect and quantify ground surface deformation related to the groundwater and oil (ESA) were used to detect and quantify ground surface deformation related to the groundwater and level change for nine oil fields in Muglad Basin, Sudan (Figure1). For the data processing, SARPROZ oil level change for nine oil fields in Muglad Basin, Sudan (Figure 1). For the data processing, software [51] was used, where the single look complex (SLC) images are co-registered to a single master. SARPROZ software [51] was used, where the single look complex (SLC) images are co-registered to Then, the SRTM 3-arc second digital elevation model (DEM) and the precise orbits for each image a single master. Then, the SRTM 3-arc second digital elevation model (DEM) and the precise orbits were used to remove the topographic and flat earth components, respectively. Reference points (R) for each image were used to remove the topographic and flat earth components, respectively. were chosen among the selected persistent scatter candidates (PSCs), which are relatively unaffected Reference points (R) were chosen among the selected persistent scatter candidates (PSCs), which are by deformation. Permanent scatters (PS) are the strong reflecting objects that are dominant in a cell. relatively unaffected by deformation. Permanent scatters (PS) are the strong reflecting objects that are Identification of the (PSCs) and PS was performed based on the amplitude stability index and the dominant in a cell. Identification of the (PSCs) and PS was performed based on the amplitude stability index and the temporal coherence [52]. Stable pixel selection depended on the use of amplitude dispersion index [53], which can be computed as in Kampes [10]:

 A DA  (6) mA

Remote Sens. 2020, 12, 1792 9 of 20 temporal coherence [52]. Stable pixel selection depended on the use of amplitude dispersion index [53], which can be computed as in Kampes [10]:

σA DA = (6) mA where σA and mA are the standard deviations and the mean of the amplitude, respectively. Therefore, a pixel with high reflection has a low DA. The amplitude stability index in the software is defined as 1 D . Temporal coherence explains the mean difference between the observed and modeled phase, − A for each pixel and interferogram [10], and it is a reflection of the standard deviation of the final time series, which is a measure of how well the time series fit its linear regression line. Three data sets consisting of 30, 30, and 32 single look complex (SLC) images on descending and interferometric wide swath (IW) mode, and single polarization (VV) (see Table1) were processed and analyzed over the studied oil fields. The images cover both dry and rainy seasons of the periods of Jan 2016–March 2019, Oct 2016–April 2019, and Nov 2015–June 2018, respectively. Figure S1 (see Supplementary Materials) shows the image pair selection for processing of the three data sets.

Table 1. Details of the Sentinel-1 A and B datasets used for the permanent scatterers interferometry (PSI) time series analysis and their properties.

Data Info First Set Second Set Third Set Number of scenes 30 30 32 January 2016–March November 2015–June Acquisition period October 2016–April 2019 2019 2018 Relative orbit 94 167 94 Acquisition track descending descending descending Acquisition mode Interferometry wide swath (IW) Product type Single look complex (SLC) Polarization VV

The first data set, including 30 images, was used to analyze Heglig and Bamboo’s oil fields (see Figure1b), where 0.80 and 0.70 amplitude stability indexes were utilized for the PSCs and PS selection, respectively, and a temporal coherence of 0.60 was used for masking purposes. The second set of data, including 30 images, was used to analyze Diffra (West of the field) and Neem (North of the field) fields (see Figure1b) where 0.85 and 0.70 amplitude stability indexes and 0.70 temporal coherence were utilized for Diffra’s PSCs and PS selection and masking, respectively. Then, 0.75 was chosen as an amplitude stability index and temporal coherence mask for the Neem field. The third set of data, including 32 images, was used to analyze Unity, Munga, Eltoor, Toma South, and Elnar’s oil fields (will be referred to as Unity-area), which lie in the South part of the oil fields. The 0.76 and 0.70 amplitude stability indexes and 0.65 temporal coherence were utilized for the PSCs and PS selection and for masking, respectively.

3. Results and Discussions In this section, we firstly report our results of GRACE data analysis and the relation between the obtained ∆GWS and the oil production data, and then we continue with PSI results and the estimated land subsidence rate over the selected oil reservoirs owing to oil and water extraction.

3.1. GRACE-Detected Mass Changes We utilized the GRACE monthly solutions processed by the CSR throughout Jan 2003–Sep 2012 to estimate the rate of ∆TWS within the region of interest. Different smoothing isotropic and non-isotropic filters were applied to filter out the effect of the correlated noises present in the monthly-normalized spherical harmonic coefficients. We utilized both DDK filters (non-isotropic) [34,45] and a conventional Gaussian (isotropic filter) spatial smoothing function [35] with 300 and 500 km smoothing radiuses. Remote Sens. 2020, 12, 1792 10 of 20

Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 20 The estimated changes of ∆TWS over the area are related to the variations in both groundwater/oil bothstorage groundwater and other hydrological/oil storagecomponents, and other hy fordrological example, soilcomponents, moisture. Tofor quantify example, the soil∆GWS moisture variations. To quantifyover the area,the ΔGWS the estimated variations hydrological over the area, signal the is estimated subtracted hydrological from the estimated signal is GRACE-based subtracted from∆TWS the estimated(see Equation GRACE (4)).- Figurebased 4Δa,bTWS show (see the Eq resultinguation (4 rate)). Figure of the 4a∆ TWSand b change show afterthe result applyinging rate the DDK1of the ΔTWSfilter to cha thenge GRACE after applying solution andthe DDK1 the rate filter of ∆ toGWS the GRACE variations solution based onand the the GRACE rate of ΔGWS solutions variations and the basedGLDAS-CLSM on the GRACE model solutions over the region,and the respectively. GLDAS-CLSM Through model the over figures, the region, the negative respectively. values Through illustrate themass figures, loss in the studynegative area, values while illustrate the positive mass values loss indicate in the study mass gain.area, while the positive values indicate mass gain.

Figure 4. Secular rate of ( a)) the estimated total water storage anomaly (ΔTWS) (∆TWS) and ( b) the estimated groundwater storage anomaly (ΔGWS), (∆GWS), using Gravity Recovery and Climate Experiment ( (GRACE)GRACE) ((CenterCenter forfor Space Spac Researche Research (CSR) (CSR centre) centre and DDK1and DDK1 filter) and filter) GLDAS and (CLSMGLDAS model) (CLSM data, model) respectively, data, respectively,over the period over of the Jan period 2003–Sep of Jan 2012. 2003 Unit:–Sep cm 2012./yr. Unit: cm/yr. A closer inspection of Figure4b reveals that the estimated rate of the ∆GWS change over the A closer inspection of Figure 4b reveals that the estimated rate of the ΔGWS change over the selected oil fields area is negative (i.e., losing mass). This situation also can be seen in the Northern selected oil fields area is negative (i.e., losing mass). This situation also can be seen in the Northern region, which contains the Nubian Sandstone aquifer, which covers about 2.2 million km2 of North-East region, which contains the Nubian Sandstone aquifer, which covers about 2.2 million km2 of North- Africa and extended in Sudan, Egypt, Libya, and Chad (see Figure S4 in the Supplementary Materials). East Africa and extended in Sudan, Egypt, Libya, and Chad (see Figure S4 in the Supplementary CEDARE report [54] showed that there is no significant groundwater recharge for this aquifer and it Materials). CEDARE report [54] showed that there is no significant groundwater recharge for this will be fully depleted in the future, without good planning for extraction. Furthermore, thousands aquifer and it will be fully depleted in the future, without good planning for extraction. Furthermore, of irrigation pumps located in the East El Owainat, South-West Egypt, which use the non-renewable thousands of irrigation pumps located in the East El Owainat, South-West Egypt, which use the non- Nubian groundwater aquifer, might be the reason for these negative signals, in addition to the artificial renewable Nubian groundwater aquifer, might be the reason for these negative signals, in addition Toshka Lake, which was created by the diversion of water from Lake Nasser through a canal into the to the artificial Toshka Lake, which was created by the diversion of water from Lake Nasser through Sahara Desert. South to Sudan-Egypt border and latitude of 20 degrees, the Nubian aquifer, which a canal into the Sahara Desert. South to Sudan-Egypt border and latitude of 20 degrees, the Nubian extends into Sudan, Chad, and Libya, is in a near-steady state. The dark negative signals in the East aquifer, which extends into Sudan, Chad, and Libya, is in a near-steady state. The dark negative (Ethiopia), the South (Uganda), and the South-West, around the Central African Republic and the signals in the East (Ethiopia), the South (Uganda), and the South-West, around the Central African Democratic Republic of Congo, show significant GWS loss. Republic and the Democratic Republic of Congo, show significant GWS loss. One of the goals of this study is to investigate different filters and hydrological models to study One of the goals of this study is to investigate different filters and hydrological models to study mass depletion in the study area in Sudan. As already mentioned, four different hydrological models mass depletion in the study area in Sudan. As already mentioned, four different hydrological models were used to remove the non-groundwater components from the GRACE-based estimated ∆TWS. were used to remove the non-groundwater components from the GRACE-based estimated ΔTWS. Considering the NOAH models, the ∆SM and ∆SWS, which include the surface runoff water, were Considering the NOAH models, the ΔSM and ΔSWS, which include the surface runoff water, were computed, whereas in the CLSM and WGHM models, the shallow groundwater is also taken into computed, whereas in the CLSM and WGHM models, the shallow groundwater is also taken into account. According to Vrbka et al. [55], depths to groundwater in Sudan vary from only a few meters in account. According to Vrbka et al. [55], depths to groundwater in Sudan vary from only a few meters the Nile valley to >250 m in remote and elevated areas. Furthermore, the reported depths for drinking in the Nile valley to >250 m in remote and elevated areas. Furthermore, the reported depths for water wells in the Heglig field are around 50 m. Hence, the groundwater in this region may not be drinking water wells in the Heglig field are around 50 m. Hence, the groundwater in this region may counted by the CLSM and WGHM models, as they may normally count for the shallow groundwater not be counted by the CLSM and WGHM models, as they may normally count for the shallow regions. The availability of in situ data in the Muglad basin (i.e., oil data) is the reason for performing groundwater regions. The availability of in situ data in the Muglad basin (i.e., oil data) is the reason this study in this area, where Heglig field coordinate (see Figure1b) was used for the GRACE and for performing this study in this area, where Heglig field coordinate (see Figure 1b) was used for the hydrological time series data collection (hereafter, it is called Heglig-area). GRACE and hydrological time series data collection (hereafter, it is called Heglig-area). Figure 5 shows the time series based on the four hydrological models and the GRACE monthly solutions using the DDK1 filter in terms of the (ΔSM + ΔSWS) and ΔTWS, respectively, in the Heglig-

Remote Sens. 2020, 12, 1792 11 of 20

Figure5 shows the time series based on the four hydrological models and the GRACE monthly solutionsRemote usingSens. 2020 the, 12, DDK1x FOR PEER filter REVIEW in terms of the (∆SM + ∆SWS) and ∆TWS, respectively,11 of 20 in the Heglig-area. Moreover, the estimated ∆TWS time series based on applying other smoothing and destrippingarea. Moreover, filters to the the estimated GRACE ΔTWS solutions time are series displayed based on in applying Figure other S2 in smoothing the Supplementary and destripping Materials. filters to the GRACE solutions are displayed in Figure S2 in the Supplementary Materials.

FigureFigure 5. Equivalent 5. Equivalent water water height heigh (EWH)t (EWH) in interms terms of ( ΔSM∆SM ++ Δ∆SWS)SWS) and and ΔTWS∆TWS based based on different on different hydrologicalhydrological models models and and GRACE-CSR GRACE-CSR solution solutionss using using DDK1 DDK1 filter,filter, respectively, respectively, in Heglig in-area Heglig-area,, over over 2003–2012.2003–2012.

It shouldIt should be noted be noted that hydrological that hydrological models models differ fromdiffer eachfrom other each mainlyother mainly in terms in of terms assimilation of algorithmsassimilation and input algorithms data. Forandinstance, input data. WGHM For instance, uses only WGHM Global uses Precipitation only Global Climatology Precipitation Centre (GPCC)Climatology rainfall dataCentre as (GPCC) a climate rainfall forcing data foras a its climate simulations. forcing for In its contrast, simulations. GLDAS In contrast, uses aGLDAS combined productuses is a derived combined from product terrestrial is derived data and from a number terrestrial of satellite-derived data and a number observations of satellite [36-].derived Moreover, observations [36]. Moreover, to estimate soil moisture variations from the GLDAS, one needs to apply to estimate soil moisture variations from the GLDAS, one needs to apply the sum of its four soil the sum of its four soil moisture layers (0–2 m depth), whereas WGHM represents soil moisture for moisture layers (0–2 m depth), whereas WGHM represents soil moisture for the complete soil the complete soil profile in a single layer whose depth refers to the rooting depth of the vegetation profilecover. in a Therefore, single layer for whoseinstance, depth for areas refers classified to the rootingas bare ground depth of(root the depth vegetation 0.1 m),cover. the total Therefore, soil for instance,moisture in for WGHM areas could classified be highly as underestimated. bare ground (root Within depth the GLDAS 0.1 m), products, the total the N soilOAH moisture model in WGHMuses could the Modified be highly IGBP underestimated. MODIS 20-category Within vegetation the GLDASclassification products, and the thesoil NOAHtexture based model on uses the Modifiedthe Hybrid IGBP STATSGO/FAO MODIS 20-category datasets. vegetation However, classificationthe CLSM utilizes and the the soil mosaic texture land based cover on the Hybridclassification, STATSGO /togetherFAO datasets. with soils, However, topographic, the CLSM and other utilizes model the-sp mosaicecific parameters land cover that classification, were togetherderived with in soils, a manner topographic, consistent and with other that model-specificof the NASA/GMAO’s parameters GEOS that-5 climate were mod derivedeling in system a manner consistent[36]. withMoreover, that of the the CLSM NASA uses/GMAO’s topographically GEOS-5 climate-derived modeling catchment system as the [land36]. Moreover,surface element, the CLSM instead of a grid in traditional land surface models (LSMs). Within the context of comparing uses topographically-derived catchment as the land surface element, instead of a grid in traditional NOAHv1.0 with NOAHv2.1, two major issues were found in the GLDASv1.0 forcing fields that have land surfacebeen addressed models (LSMs).in GLDASv2.1 Within theproduct context. First, of comparingthe Air Force NOAHv1.0 Weather withAgency’s NOAHv2.1, AGRicultural two major issuesMETeorological were found in modeling the GLDASv1.0 system (AGRMET) forcing fields shortwave that have downward been addressed radiation flux in GLDASv2.1was sharp during product. First,certain the Air years. Force Second, Weather there Agency’s was a dramatic AGRicultural change in METeorological precipitation in certain modeling locations system starting (AGRMET) in shortwave2009. Furthermore, downward radiationcomparisons flux of was GLDASv1.0 sharp during radiation certain and years.precipitation Second, fields there revealed was a dramaticthat changeGLDASv1.0 in precipitation had high in bias certain relative locations to the well starting-validated in 2009. surface Furthermore, radiation budget comparisons (SRB) dataset, of GLDASv1.0 and radiationGLDASv1.0 and precipitation precipitation fields had revealed low bias that relative GLDASv1.0 to the Global had high Precipitation bias relative Climatology to the well-validated Project surface(GPCP) radiation dataset. budget Similar (SRB) biases dataset, were observed and GLDASv1.0 compared precipitationwith GLDAS-2.0, had whose low biasradiation relative fields to the Globalwere Precipitation bias corrected Climatology to the SRB Projectdataset (GPCP)(one can dataset.find more Similar detailed biases information were observed in readme compared document with for NASA GLDAS version 2 data products). GLDAS-2.0, whose radiation fields were bias corrected to the SRB dataset (one can find more detailed The pattern of the four hydrological signals over the region may greatly depend on the climatic informationstatus, where in readme they show document a bimodal for behavior NASA GLDAS with a rainy version season 2 data that lasts products). for about 5–6 months, with Thea peak pattern in August, of the and four dry hydrological season for the signals rest of the over year the, with region a peak may in greatly about February depend and on theMarch. climatic status,Table where 2 provides they show a comparison a bimodal between behavior the with utilized a rainy hydrological season that models lasts in for terms about of 5–6the months,correlation with a peak incoefficients August, andbetween dry seasonthe estimated for the (ΔSM rest of+ ΔSWS) the year, time with series a peak overin different about Februaryperiods. This and comparison March. Table 2 providesreveals a comparison the similarities between and differences the utilized between hydrological the timemodels series estimated in terms based of the on correlation models, which coeffi iscients betweenhighly the affected estimated by (the∆SM lack+ ∆ofSWS) and scarce time seriesand sparse over didistributionfferent periods. of in situ This hydrological comparison and reveals the similaritiesmeteorological and measurements differences between in the study the region. time series estimated based on models, which is highly

Remote Sens. 2020, 12, 1792 12 of 20 affected by the lack of and scarce and sparse distribution of in situ hydrological and meteorological measurements in the study region. A closer inspection of Figure5 and Table2 reveals that there is a relatively good agreement between different time series over 2003–2009, where the NOAHv2.1 model shows a better performance with a correlation of 0.76 with the WGHM model. Then, after 2010, the NOAHv2.1 time series shows a notable deviation from the other models. Moreover, over the whole period 2003–2012, except NOAH 2.1, a better agreement (about 0.52) is shown between CLSM, NOAH1.0, and WGHM models. The peak shown of 2008 may be attributed to the reported high rainfall season that year. In addition, the lower peak pattern in 2009–2010 agrees with the reported low rain season for the same year.

Table 2. Correlation coefficients between different hydrological models in terms of the estimated (soil moisture anomalies (∆SM) + surface water storage anomalies (∆SWS)) time series over different periods.

Model 2003–2012 2003–2009 2009–2012 CLSM & Noahv1.0 0.52 0.64 0.59 CLSM & Noahv2.1 0.11 0.32 0.33 − − CLSM & WGHM 0.51 0.52 0.36 Noahv1.0 & Noahv2.1 0.47 0.63 0.53 − Noahv1.0 & WGHM 0.51 0.65 0.45 Noahv2.1 & WGHM 0.11 0.76 0.26 −

The estimated GRACE-based rates of ∆TWS changes over the area reveal negative values ranging from 1.45 0.63 to 0.23 0.15 cm/yr using different filters. Table3 shows the estimated rates of − ± − ± ∆GWS changes using the four hydrological models and different filters. We applied isotropic Gaussian filter, with the half-wavelength of radius of 300 and 500 km, as well as non-isotropic DDK filters to overcome the north–south strips noises. DKK1 is the strongest and DDK8 is the weakest filter. Accordingly, one can see that the estimated uncertainty increases as for applying weaker filters, that is, the weaker the filter, the higher the uncertainty. The results show that using different filters and hydrological models will result in different patterns of rates of ∆GWS changes. For instance, using different filters, the NOAHv2.1 hydrological model is the only one among the others that results in a positive rate of ∆GWS change. Moreover, the CLSM model provides a higher negative rate of ∆GWS change when compared with others.

Table 3. The rate and their uncertainties of groundwater storage anomaly (∆GWS) change based on the Gravity Recovery and Climate Experiment (GRACE) products and hydrological models for different filters. Unit: cm/yr (Uncertainties are computed based on the 95% confidence level).

Filters CLSM NOAHv1.0 NOAHv2.1 WGHM DDK1 0.719 0.123 0.277 0.188 0.527 0.119 0.450 0.163 − ± − ± ± − ± DDK2 0.821 0.165 0.378 0.191 0.425 0.130 0.552 0.202 − ± − ± ± − ± DDK3 1.004 0.195 0.561 0.197 0.242 0.147 0.735 0.227 − ± − ± ± − ± DDK4 1.051 0.205 0.609 0.206 0.195 0.160 0.782 0.239 − ± − ± ± − ± DDK5 1.210 0.253 0.767 0.254 0.036 0.220 0.941 0.292 − ± − ± ± − ± DDK6 1.333 0.290 0.890 0.286 0.086 0.261 1.063 0.329 − ± − ± − ± − ± DDK7 1.688 0.476 1.245 0.449 0.442 0.459 1.418 0.505 − ± − ± − ± − ± DDK8 1.846 0.631 1.403 0.595 0.600 0.617 1.576 0.651 − ± − ± − ± − ± Gaussian 300 km 0.976 0.241 0.533 0.234 0.270 0.206 0.707 0.263 − ± − ± ± − ± Gaussian 500 km 0.626 0.129 0.183 0.182 0.620 0.115 0.356 0.159 − ± − ± ± − ±

To evaluate different filters and hydrological models, one can rely upon the available in situ data (i.e., oil production), as the total mass extraction should agree with the estimated ∆GWS. Table4 summarizes the correlation coefficients between the monthly total oil extraction and the estimated ∆GWS change using different smoothing filters and hydrological models in the Heglig-area oil fields. We used the well-known Pearson’s equation [56] to quantify the similarity between in situ-achieved Remote Sens. 2020, 12, 1792 13 of 20

oil andRemote water Sens.extraction 2020, 12, x FOR and PEER the REVIEW GRACE-based ∆GWS estimates. The estimated ∆GWS based13 of 20 on the DDK1 and CLSM model shows a good agreement with the local mass extraction (see Figure3), that is, oil andDDK1 water and depletion CLSM model (with shows a correlation a good agreement coefficient with of the about local 0.74). mass extraction (see Figure 3), that is, oil and water depletion (with a correlation coefficient of about 0.74). Table 4. Correlation coefficients between estimated ∆GWS time series and monthly total mass (oil and water)Table extraction. 4. Correlation coefficients between estimated ΔGWS time series and monthly total mass (oil and water) extraction. Filter CLSM NOAHv1.0 NOAHv2.1 WGHM Filter CLSM NOAHv1.0 NOAHv2.1 WGHM DDK1 0.741 0.257 0.651 0.459 DDK1 0.741 0.257 − –0.651 0.459 DDK2 0.690 0.346 0.527 0.461 DDK2 0.690 0.346 − –0.527 0.461 DDK3 0.702 0.470 0.295 0.524 − DDK3 DDK40.702 0.699 0.482 0.470 0.224–0.295 0.527 0.524 − DDK4 DDK50.699 0.669 0.488 0.482 0.037–0.224 0.518 0.527 − DDK5 DDK60.669 0.654 0.4980.488 0.055–0.037 0.518 0.518 DDK6 DDK70.654 0.554 0.4570.498 0.1730.055 0.465 0.518 DDK8 0.483 0.403 0.178 0.414 DDK7 0.554 0.457 0.173 0.465 Gaussian 300 km 0.613 0.393 0.237 0.457 − DDK8Gaussian 500 km0.483 0.671 0.170 0.403 0.7270.178 0.381 0.414 Gaussian 300 km 0.613 0.393 − –0.237 0.457 Gaussian 500 km 0.671 0.170 –0.727 0.381 Figure6 illustrates the estimated ∆GWS based on the different hydrological models and applying the DDK1Figure filter to6 theillustrates GRACE the monthly estimated solutions ΔGWS inbased the Heglig-area.on the different Similar hydrological to Figure models5, the obtainedand ∆GWSapplying time series the DDK1 from filter diff erentto the GRACE hydrological monthly models solutions are in generally the Heglig agreed-area. Similar between to Figure 2003 5, and the 2009, however,obtained after ΔGWS 2009, time theestimated series from∆ differentGWS using hydrological the NOAHv2.1 models are model generally shows agreed a significant between deviation2003 fromand the other2009, however, models. after 2009, the estimated ΔGWS using the NOAHv2.1 model shows a significant deviation from the other models.

FigureFigure 6. Temporal 6. Temporal∆GWS ΔGWS variations variations between between2003 2003 and 2013 2013 obtained obtained from from the the subtraction subtraction of GRACE of GRACE (DDK1(DDK1 filter) filter) and and diff erentdifferent hydrological hydrological data. data. Unit: Unit: cm.cm.

3.2. PSI-Detected3.2. PSI-Detected Land Land Subsidence Subsidence The availabilityThe availability of theof the proper proper SAR SAR satellite satellite datadata over over the the given given region region and and revisiting revisiting timetime affects aff ects the capabilitythe capability to estimate to estimate deformation deformation phenomena phenomena over time time [5 [757]. ].Despite Despite the the suitability suitability of L- ofband L-band imagesima forges vegetated for vegetated areas areas in in terms terms of of penetration penetration depths, there there w wereere no nott enough enough free free L-band L-band images images to cover the study region for such a PSI analysis. As such, only Sentinel-1 data were used for this to cover the study region for such a PSI analysis. As such, only Sentinel-1 data were used for this study. As can be seen in the graph (Figure S1), there are a lack of data during periods of 23 Jan 2016 study. As can be seen in the graph (Figure S1), there are a lack of data during periods of 23 Jan 2016 and 21 July 2016 (six months) for the first data set, in addition to some sparse periods (2–3 months) and 21between July 2016 the acquisitions (six months) of forthe thethree first data data sets set, despite in addition the short to revisit some time sparse of Sentinel periods-1 (2–3 sensors months), betweenwhich the is acquisitions 6–12 days. of the three data sets despite the short revisit time of Sentinel-1 sensors, which is 6–12 days.The three SAR data sets were analyzed over the nine oil fields, where the PSI technique was Theapplied three to detect SAR datarelative sets ground were deformation analyzed over due theto oil nine and oil water fields, depletion, where using the PSI the techniqueprocessing was appliedcriteria to detect in Section relative 2.4. The ground line-of deformation-sight (LOS) disp duelacement to oil and rate waters at the depletion, selected PS using points the in Heglig processing criteria in Section 2.4. The line-of-sight (LOS) displacement rates at the selected PS points in Heglig

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Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 20 Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 20 andand Bamboo Bamboo (Figure (Figure7), Neem 7), Neem (Figure (Figure S6), S6), Di ffDiffrara (Figure (Figure8), and8), and the the Unity-area Unity-area fields fields (Figure (Figure S7b) S7b) were and Bamboo (Figure 7), Neem (Figure S6), Diffra (Figure 8), and the Unity-area fields (Figure S7b) computedwere computed relative torelative their referenceto their reference PS point PS (cf.point Figure (cf. Figure7). 7). were computed relative to their reference PS point (cf. Figure 7).

FigureFigure 7. Line-of-sight 7. Line-of-sight (LOS) (LOS displacement) displacement rates rates ofof the descending descending PS PS points points at atHeglig Heglig oil field oil field relative relative Figure 7. Line-of-sight (LOS) displacement rates of the descending PS points at Heglig oil field relative to reference PS, called R(H-124). The PS points (H-178) and (H-166) show the maximum displacement to referenceto reference PS, PS, called called R(H-124). R(H-124). The The PS PS points points (H-178)(H-178) and and (H (H-166)-166) show show the the maximum maximum displacement displacement rate (10.53 and 24.47 mm/yr, respectively) for the observation period October 2016April 2019. raterate ( 10.53 (10.5 and3 and24.47 24.47 mm mm/yr/yr,, respectively) respectively) for the o observationbservation period period Oct Octoberober 2016 2016–AprilApril 2019. 2019. Heglig− location− is shown in Figure 1b. HegligHeglig location location isshown is shown in in Figure Figure1b. 1b.

FigureFigure 8. LOS 8. LOS displacement displacement rate rate of the descending descending PS PS points points at Diffra at Di oilffra field oil fieldrelative relative to reference to reference PS, Figure 8. LOS displacement rate of the descending PS points at Diffra oil field relative to reference PS, called R(Dif-81) (not shown owing to the figure scale), Unit: mm/yr. Observation period October PS, calledcalled R(Dif-81)R(Dif-81) (not (not shown shown owing owing to to the the figure figure scale), scale), Unit: Unit: mm/yr mm/.yr. Observation Observation period period October October 2016–April 2019. Diffra location is shown in Figure 1b. 2016–April2016–Apr 2019.il 2019. Di ffDiffrara location location is is shown shown in inFigure Figure1 1b.b. Table 5 summarizes the cumulative displacement LOS, temporal coherence, velocity, and TableTable5 summarizes 5 summarizes the cumulative the cumulative displacement displacement LOS, LOS temporal, temporal coherence, coherence, velocity, velocity and, velocityand velocity standard deviation of the selected PS points (i.e., those have maximum velocity), including standardvelocity deviation standard ofdeviation the selected of the PSselected points PS (i.e.,points those (i.e., havethose have maximum maximum velocity), velocity) including, including their their references for Heglig and Bamboo, Neem, Diffra, and Unity-area oil fields, respectively. referencestheir references for Heglig for and Heglig Bamboo, and Neem,Bamboo, Di ffNeem,ra, and Diffra, Unity-area and Unity oil fields,-area respectively.oil fields, respectively. Furthermore, Furthermore, Figures 9 and Figure S7 illustrate the cumulative LOS displacement in millimetre for FigureFurthermore,9 and Figure Figures S7 illustrate 9 and Figure the cumulative S7 illustrate LOSthe cumulative displacement LOS indisplacement millimetre in for millimetre the selected for PS the selected PS points relative to their corresponding reference points. Land covers of Neem oil field, pointsthe relative selected to PS their points corresponding relative to their reference corresponding points. reference Land covers points. of Land Neem cov oilers field, of Neem which oilis field, located which is located in the northern part of the study area, are mostly silty sand and less vegetated with in thewhich northern is located part in of the the northern study area,part of are the mostly study area, silty are sand mostly and lesssilty vegetatedsand and less with vegetated limited with surface limited surface structure (i.e., most of the PS points are laid on the ground) (see Figure S6). Reference limited surface structure (i.e., most of the PS points are laid on the ground) (see Figure S6). Reference structurepoint, (i.e., R (N most-568), of at the the PS top points of the are figure laid, oncan the be ground)seen among (see the Figure very S6). stable Reference PS points point, (i.e., Rgreen (N-568), point, R (N-568), at the top of the figure, can be seen among the very stable PS points (i.e., green at thecolor), top ofand the shows figure, a stable can beand seen consistent among time the series very stable(Figure PS S6). points The time (i.e., series green of color),PS points and N- shows586, a color), and shows a stable and consistent time series (Figure S6). The time series of PS points N-586, stableN- and653, consistentN-669, N-741 time, and series N-761 (Figure shows S6).a continuous The time subsiding series of PSrate points with no N-586,table deviations N-653, N-669, in May, N-741, N-653, N-669, N-741, and N-761 shows a continuous subsiding rate with notable deviations in May, andJune, N-761 and shows July that a continuous can be attributed subsiding to the rate rainy with season. notable deviations in May, June, and July that can June, and July that can be attributed to the rainy season. be attributed to the rainy season. To the south of Neem, where Heglig, Bamboo, and Differa fields are located, the land is more vegetated and soil types are more clay. Heglig is the head quarter of the nine fields and it contains Remote Sens. 2020, 12, 1792 15 of 20 most of the oil infrastructure (oil central processing facilities (CPFs), airport, and the main camps). This is why the density of the PS points is larger compared with that of other fields (see Figure7). The reference point R(H-124) in Figure7 can be seen among the very stable PS points (i.e., green color), and also shows stable time series, as can be seen in Figure9a. In this figure, the time series of point (H-178) illustrates a consistent subsidence rate of 10.53 mm/yr. The time series of the PS point − (Heg-166) with an estimated 24.47 mm/yr subsidence rate shows notable deviations during months − of April and May, which can be attributed to the rainy season. The three PS points (Bam-6, Bam-8, and Bam-9) are in Bamboo field, and show consistent time series generally with small changes in the rainy seasons. Therefore, the reasons for such deformation in this area can be attributed mainly to the oil extraction and the soil type. The use of pile foundation type in building constructions, which is dominant in Heglig field, can greatly reduce the soil type effect. Thus, the main reason for such displacement in Heglig, which has a higher production rate, probably could be assigned to oil and water depletion. The reference PS point in Diffra field (Dif-81) shows more temporal varieties in May, June, and October (i.e., the rainy season) (Figure9b). To the south, in the Unity-area fields, the most vegetated area and large swamps limited our PSI analysis. Figure S7b shows the reference PS point, R (Uni-40), which is less stable compared with other references. For these fields, the effect of the soil type and swamps, and the lack of stable reflectors (e.g., buildings) on the PS time series analysis are visible. The comparison of the subsidence rates for different oil fields (Table5) clearly shows clearly the Heglig oil field is the most deformed one, with an accumulative displacement of about 77.6 mm − during a three-year period. Its higher production rate may be the reason for such large subsidence, in addition to the dense PS points, which allow for closer inspection. Moreover, and according to Castelazzi [58], InSAR applications to groundwater depletion are limited to reservoir systems sensitive to measurable deformation (clays and silts), which is the case in the study area.

Table 5. Cumulative line-of-sight (LOS) displacement, coherence, and velocity, including the references for Heglig and Bamboo, Neem, Diffra, and Unity area oil fields (Unity, Munga, Toma South, Eltoor, and Elnar).

Field Point ID Cumul. Dis. (mm) Coherence Velocity (mm/yr) H-124 0 0.98 0 0.74 ± H-166 –77.59 0.69 –24.47 0.85 ± H-178 –33.38 0.85 –10.53 0.95 Heglig and Bamboo ± Bam-6 –47.40 0.90 –14.95 0.82 ± Bam-8 –53.92 0.87 –17.01 0.79 ± Bam-9 –43.35 0.85 –13.67 N-568 0 1 0 0.82 ± N-653 –40.96 0.76 –16.19 0.89 ± N-669 –41.13 0.74 –16.26 0.91 Neem ± N-586 –38.80 0.78 –15.03 0.87 ± N-741 –21.42 0.85 –10.84 0.89 ± N-761 –60.08 0.65 –23.75 0.96 ± Dif-26 –35.80 0.96 –14.20 0.85 ± Dif-36 –31.90 0.97 –12.60 0.84 ± Dif-51 –35.60 0.93 –14.10 0.85 Diffra ± Dif-57 –33.70 0.94 –13.30 0.86 ± Dif-65 –33.10 0.96 –13.10 0.84 ± Dif-81 0 0.92 0 0.81 ± Uni-4 –9.84 0.77 –3.81 0.86 ± Uni-6 –15.42 0.69 –5.98 0.88 ± Uni-9 –2.84 0.97 –1.10 0.81 Unity-area ± Uni-16 –1.53 0.99 –0.59 0.81 ± Uni-28 –2.35 0.96 –0.91 0.82 ± Uni-40 0 0.91 0 0.77 ± Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 20

Remote Sens. 2020, 12, 1792 16 of 20

FigureFigure 9. Cumulative 9. Cumulative displacement displacement in in (mm) (mm) ofof thethe selected PSI PSI points points relative relative to reference to reference point point R in R in (a) Heglig(a) Heglig and and Bamboo Bamboo and and (b )(b Di) Diffraffra oil oil fields. fields.

3.3. Potential3.3. Potential and an Limitationsd Limitations for for Linking Linking GRACE GRACE andand PSI R Resultsesults DespiteDespite their their diff differenterent resolution resolution and and application, application, GRACE GRACE and and SAR SAR can can provide provide complementary complementary data fordata monitoring for monitoring of waterof water and and oil oil reservoirs reservoirs [[5858].]. The water water and and oil oil depletion depletion cause causess changes changes in in massmass distribution, distributio andn, theand consequentthe consequent reservoir reservoir compaction compaction causes caus groundes ground surface surface subsidence, subsidence which, which can be detected by InSAR (e.g., PSI analysis in our case). According to [58,59], the generated can be detected by InSAR (e.g., PSI analysis in our case). According to [58,59], the generated PSI-based PSI-based groundwater depletion map can be used as downscaling data to partially mitigate GRACE groundwater depletion map can be used as downscaling data to partially mitigate GRACE resolution resolution limitation. For such a process, additional in situ data are required (e.g., the vertical and limitation.horizontal For variability such a process, of sediment additional compressibility) in situ data to are achieve required such (e.g.,an inversion the vertical from and PSI- horizontalbased variabilitydisplacement of sediment map tocompressibility) volume of depleted to GWS. achieve However, such anthis inversion analysis was from not included PSI-based in this displacement study map toowing volume to a lack of depleted of such data. GWS. However, this analysis was not included in this study owing to a lack of suchIn the data. context of this study, and owing to the lack of SAR data for our study area before 2015, Inwe the could context not have of this the study,temporal and overlap owing with to theother lack data of sets, SAR that data is, for GRACE our study and oil area wells before data. 2015, we could not have the temporal overlap with other data sets, that is, GRACE and oil wells data. However, owing to the continuous oil production between 2002 and 2019 (Figure S5), the analyzed trend of subsidence over oil fields resulting from our PSI analysis can be projected approximately for different periods in which GRACE and oil wells data sets are available. However, with the accumulation of Sentinel-1 or other SAR data together with the new data collected by, for example, GRACE-FO, a more detailed analysis and link between results will be possible. Remote Sens. 2020, 12, 1792 17 of 20

4. Conclusions In this paper, we carried out a comprehensive study about the relation of oil production, GRACE-based ∆GWS estimation, and monitoring ground subsidence in response to extraction cycles in nine oil fields in Sudan. We used GRACE monthly solutions (RL06) processed by the CSR processing centre to assess the oil and water depletion in the selected oil fields. In this study, we employed both non-isotropic (DDK filters) and isotropic (Gaussian) filters to reduce the effect of the correlated noises in GRACE data. We computed the rates of ∆GWS changes (due to the oil production) by utilizing different hydrological models, namely, GLDAS-NOAHv1.0, GLDAS-NOAHv2.1, GLDAS-CLSMv2.0, and WGHM models. Our results show that the achieved rates of ∆GWS changes are highly dependent on the choice of decorrelation filter and the hydrological models. Our analysis based on applying DDK1 decorrelation filter and utilizing the GLDAS-CLSMv2.0 model results in a high correlation (0.74 correlation coefficients) between the estimated ∆GWS and oil and water extraction in the Heglig-area oil fields. Except for the resolution of GRACE data, the main source of uncertainty in the estimated ∆GWS is related to the hydrological models, which reveal a strong relation with the rainfall trend in the region. According to the achieved results, GRACE data can help to monitor the mass changes owing to heavy oil productions, if we could solve remarkable uncertainty in hydrological models in the future. We also computed the rate of land subsidence owing to the oil extraction in different oil fields using SAR data and the persistent scatterer interferometry (PSI) technique. For instance, we observed 77.6, 60.1, 35.8, and 15.4 mm subsidence in Heglig, Neem, Diffra, and Unity-area oil fields − − − − during a three-year period (i.e., Nov 2015–Apr 2019), which correspond to 24.47 0.85, 23.8 0.96, − ± − ± 14.2 0.85, and 6 0.88 mm/yr, respectively. This surface subsidence can be assigned to the pore − ± − ± pressure drop owing to the vertical compaction of the reservoir.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/11/1792/s1, Figure S1: Image graph for the 30, 32, and 30 descending sets of images, (a) for Diffra and Neem’s sites; (b) for Unity, Munga, Eltoor, Toma South, and Elnar’s sites; and (c) for Heglig and Bamboo’s sites, respectively. The figure shows acquisition date (horizontal axis), chosen master, and the perpendicular baselines in meter unit; Figure S2: Computed TWS based on the different hydrological models and GRACE using different fillters in Heglig area; Figure S3: GWS obtained from subtraction of GRACE (different filters) and different hydrological data; Figure S4: Groundwater resources in Sudan and South Sudan; Figure S5: Oil production in Sudan and South Sudan; Figure S6: Displacement trend of the descending PS points at Neem oil field relative to reference point R (N-568). Unit: mm/yr. Period: Oct 2016–Apr 2019; Figure S7: Cumulative displacement in (mm) of the selected PSI points relative to reference point R in (a) Neem and (b) Unity area oil fields. Author Contributions: N.A.A.G. wrote the manuscript and processed and analyzed the data. H.A. carried out data processing and helped to draft the manuscript. M.B. designed the study and helped to write and prepare the manuscript. He also contributed to data processing. F.N. contributed to data processing and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors are grateful for the free use of the Sentinel-1 data provided by the European Space Agency (ESA), and to the SARPROZ Company for providing free licence for SARPROZ software for this study. We would also like to show our gratitude to Mr. Osama Ibrahim and the Greater Nile Petroleum Operating Company (GNPOC) for sharing the oil production data. We are very grateful to the editor and five reviewers who provided constructive and useful comments. Special thanks to the University of Gävle for financing publishing cost of this study. Conflicts of Interest: The authors declare no conflict of interest.

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