remote sensing

Article Analysis of Salt Lake Volume Dynamics Using Sentinel-1 Based SBAS Measurements: A Case Study of Lake Tuz, Turkey

Burhan Baha Bilgilio˘glu 1,2,*, Esra Erten 3 and Nebiye Musao˘glu 3

1 Department of Geomatics Engineering, Faculty of Engineering and Natural Sciences, Gumushane University, Gumushane 29000, Turkey 2 Building of Graduate School, Ayazaga Campus, Istanbul Technical University, Istanbul 34469, Turkey 3 Department of Geomatics Engineering, Civil Engineering Faculty, Istanbul Technical University, Istanbul 34469, Turkey; [email protected] (E.E.); [email protected] (N.M.) * Correspondence: [email protected]; Tel.: +90-456-233-10-00-1754

Abstract: As one of the largest hypersaline lakes, Lake Tuz, located in the middle of Turkey, is a key waterbird habitat and is classified as a Special Environmental Protection Area in the country. It is a dynamic lake, highly affected by evaporation due to its wide expanse and shallowness (water depth <40 cm), in addition to being externally exploited by salt companies. Monitoring the dynamics of its changes in volume, which cause ecological problems, is required to protect its saline lake functions. In this context, a spatially homogeneous distributed gauge could be critical for monitoring and rapid response; however, the number of gauge stations and their vicinity is insufficient for the



 entire lake. The present study focuses on assessing the feasibility of a time-series interferometric technique, namely the small baseline subset (SBAS), for monitoring volume dynamics, based on Citation: Bilgilio˘glu,B.B.; Erten, E.; freely available Sentinel-1 data. A levelling observation was also performed to quantify the accuracy Musao˘glu,N. Analysis of Salt Lake of the SBAS results. Regression analysis between water levels, which is one of the most important Volume Dynamics Using Sentinel-1 volume dynamics, derived by SBAS and levelling in February, April, July and October was 67%, 80%, Based SBAS Measurements: A Case Study of Lake Tuz, Turkey. Remote 84%, and 95% respectively, for correlation in the range of 10–40 cm in water level, and was in line Sens. 2021, 13, 2701. https://doi.org/ with levelling. Salt lake components such as water, vegetation, moist soil, dry soil, and salt, were also 10.3390/rs13142701 classified with Sentinel-2 multispectral images over time to understand the reliability of the SBAS measurements based on interferometric coherence over different surface types. The findings indicate Academic Editors: Meisam Amani, that the SBAS method with Sentinel-1 is a good alternative for measuring lake volume dynamics, Qiusheng Wu, Brian Brisco, including the monitoring of water level and salt movement, especially for the dry season. Even Hooman Latifi, Sahel Mahdavi and though the number of coherent, measurable, samples (excluding water) decrease during the wet Arsalan Ghorbanian season, there are always sufficient coherent samples (>0.45) over the lake.

Received: 24 May 2021 Keywords: InSAR; Sentinel; wetland; water level; Lake Tuz Accepted: 5 July 2021 Published: 9 July 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in published maps and institutional affil- In providing a productive ecosystem and suitable habitat for a wide variety of plant iations. and animal species, wetlands are vital ecological and important social-economic areas [1,2]. It is important to protect and monitor wetlands, which feed groundwater resources, increase the agricultural soil fertility, regulate the water cycle, and reduce carbon emissions, among other functions [3,4]. Policies aiming at the management of wetlands strive to monitor their physical, chemical, and biological characteristics by measuring variations in water Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. level/area/volume, pollutant factors, input–output, and the impact of the surrounding This article is an open access article industries on the lake [5]. Among these variations, volume dynamics is an important distributed under the terms and parameter that directly affects the population of plant and animal communities in this conditions of the Creative Commons habitat; climate change and human activities are the main drivers of this change [6–8]. In Attribution (CC BY) license (https:// this context, information on deformation in the lake surface and its water levels, with a creativecommons.org/licenses/by/ high temporal and spatial resolution (frequency of gauge stations), plays an important role 4.0/). in wetland monitoring [9]. However, many lakes do not have gauge stations or they are

Remote Sens. 2021, 13, 2701. https://doi.org/10.3390/rs13142701 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2701 2 of 19

not sufficient in frequency and number to extract hydrological information for the entire lake [10]. In addition to the low spatial resolution of these stations, there may be difficulties in acquiring data from institutions and organizations that manage and trade the data gathered by gauge stations [11]. In this regard, remote sensing (RS) instruments developed in parallel with technological advances are an ideal platform for solving such current problems and are highly utilized in assessing the volume dynamics of wetlands [12]. Since the United States Geological Survey (USGS) began to provide data freely to all users following a data policy change in 2008, it was easier for researchers to access RS (new and archived Landsat) data [13]. Additionally, the European Commission’s Copernicus program, which launched its first satellite in 2014, started to offer alternatives free of charge for users. This program, created by the European Space Agency (ESA), is the European Union’s Earth Observation (EO) program, which aims to monitor our planet and its environment. It provides freely available information services to its users via satellites and in situ data such as ground stations, airborne sensors, and seaborne sensors. Since the launch of the Sentinel-1A SAR satellite in 2014, EO data have been actively used in many research areas, such as agriculture, climate change, the environment, marine observation, insurance, disaster management, and the blue economy [14]. Sentinel-1 SAR images with a high spatial resolution (5 m × 20 m on the ground) provide dual-polarization (VV and VH) backscatter coefficients in the C-band. Their high temporal resolution (12- and 6-day repeat cycle option) and their day, night, and all-weather imaging capability have excellent application prospects in the field of environmental monitoring [15,16]. The short revisit time is especially advantageous for observing dynamic areas with rapid changes, such as wetlands, landslide zones, and sinkhole areas [17–20]. With the launch of the Sentinel-2A in 2015, Copernicus made both SAR and optical satellite images available free of charge. The purpose of the Sentinel-2 mission is to create an operational multispectral EO system that ensures sustainability and works in harmony with the Landsat and Spot satellites [21]. Sentinel-2 has some advantages over Landsat. It has higher spatial resolution and higher spectral resolution in the near-infrared region and also has three vegetation red-edge bands. The Sentinel-2 sensor, the EO satellite of the Copernicus program, has 12 bands with spatial resolutions of 10 m (four visible and near-infrared bands), 20 m (six red-edge and shortwave infrared bands), and 60 m (three atmospheric correction bands) [22–24]. It is also important, especially for long-term monitoring, that the Sentinel missions are guaranteed to continue until 2030 [25]. In this context, the high temporal resolution, coupled with free availability, achieved by optical Sentinel-2 and SAR Sentinel-1 images paves the way for new hydrological applications aimed at the identification and monitoring of wetlands [26,27]. The Single Look Complex (SLC) imaging mode of Sentinel-1, with high temporal resolution across large areas, makes it possible to use large stacks of interferometric SAR (InSAR) data to monitor surface displacements on the Earth’s surface over time [28]. In this context, many different studies have been conducted on interferometric Sentinel-1 data coupled with the persistent scatter interferometry (PSInSAR) method [29], specifi- cally in urban monitoring, such as in reclamation areas [30], dams [31], bridges [32], and buildings [33]. The popularity of the PSInSAR technique lies in the fact that it obviates the need for time-consuming and costly geodetic surveying methods, and provides detailed information about surface displacement. However, when it comes to surfaces in rural settings, as opposed to urban ones, PSInSAR implementation is challenging due to the lack of persistent scatters in high coherence targets [34]. In this context, another powerful time-series InSAR method, namely small baseline subsets (SBAS), offers not only unique surface change (deformation) information but also information related to the dielectric constant and structure of the surface [35]. When determining the main and dependent pairs for interferograms in the SBAS network, they are selected according to the average base- line parameters for the signal of interest, without considering the temporal baseline [36]. The SBAS technique provides highly sensitive information about surface deformations in urban centers and open areas for determining land subsidence [37,38]. However, in Remote Sens. 2021, 13, 2701 3 of 19

highly complex situations, such as monitoring water level changes and salt movement, ignoring the temporal separation of interferograms results in poor coherence. In wetlands, INSAR measurements and water level determinations are related to the characteristics of the region [39]. Nevertheless, there are distinct seasonal deformation features in areas close to the water body [40]. For this reason, the temporal baseline should be as small as possible in the determination of volume dynamics, such as cases in which the water level of wetlands is being monitored [41]. In this study, we aimed to monitor the volume dynamics, including water level and salt movement, in one of the largest hypersaline lakes in the world [42], namely Lake Tuz (Turkey), using EO satellites of the Copernicus program. For this purpose, the SBAS method, an advanced interferometric technique, was applied to Sentinel-1 SAR data from November 2017 to January 2019. We have compared the levelling measurements conducted in the field with those calculated using SBAS in terms of water level accuracy. Additionally, the consistency and applicability of the SBAS methodology have been evaluated with mete- orological and optical Sentinel-2 data to understand whether SBAS meets the requirements of water level information in the salt lake area, where there is a dynamic transition among the salt lake components, namely water, vegetation, salt, moist soil, and dry soil.

2. Materials and Methods 2.1. Study Region Lake Tuz, located in the middle of Turkey, is one of the largest hypersaline lakes in the world and the second-largest lake in Turkey. It is mainly fed by underground water. With specific kinds of flora and fauna, it was declared a Special Environmental Protection Area (SEPA) by Turkey’s Cabinet of Ministers in 2000. This type of lake is generally shallow and saline, with its salinity being about 35%. Lake Tuz covers an area approximately 1500 km2, with a length of 80 km and a width of 50 km (Figure1). Its bright visibility from space, strong reflection, and flatness make it one of the EO’s eight calibration sites in the world. The lake is categorized as a wetland, and its salinity varies seasonally with the water level, being at its lowest in the wet season and highest in the dry season, due to evaporation [43,44]. In the winter, it spreads over a large area due to the effect of precipitation and dissolves the salt particles, while, in the summer approximately 95% of the lake dries up and turns into a salt flatland [45,46]. More than half of Turkey’s salt needs are covered by salt pans in the lake [47]. It is known that approximately 1200 km2 of the Lake Tuz area is a salt region. The lake’s salt reservoir totals approximately 210 million tons. With this reserve potential, the lake is the second-largest source of salt production in the world [48]. The water level, and hence the salt pans, are particularly influenced by industrial activities, as salt lakes are the main areas where the salt industry operates. In the lake, industrial salt production starts around mid-April by drawing water from the lake to the salt pan. This continues for four or five months depending on weather conditions, as high temperatures are required for evaporation. Salt formation starts in August and continues until October. In October, underground waters emerge from numer- ous crevices to feed the lake bed and thus the salt production cycle continues. There are four meteorological stations and two dams around Lake Tuz, as shown in Figure1. The dams were built on the Melendiz and Pecenek streams, which feed the lake. Moreover, the beds of the Degirmenozu and Insuyu streams were modified to feed other lakes that were about to dry out. It is worth noting that the channel of the General Directorate of State Hydraulic Works (DSI), located in the southwest region where seasonal floods occur every year, is the only source that directly feeds Lake Tuz. The lake system is well represented on the eastern side, where detailed field work (electronic moisture measurement, spectrora- diometer measurement, and levelling measurement) was carried out. Eleven points of the Turkish National Fundamental GPS Network (TUTGA) are homogeneously distributed in the study area, and GPS measurements taken from the salt pans in the Lake Tuz are used as calibration data for SBAS results in the interferogram calibration stage; these data were obtained from the General Directorate of Mapping (HGM). RemoteRemote Sens. Sens.2021 2021, ,13 13,, 2701 x FOR PEER REVIEW 4 of4 of19 19

Figure 1. Geographic location of of Lake Lake Tuz Tuz overlaid on the hillside background, background, as as well well as as the the spa- spatial distributiontial distribution of the of the meteorological meteorological and and hydrological hydrological stations stations and and in situin situ areas areas used used in thisin this study. The referencestudy. The area reference where area the levellingwhere the measurement levelling measurement was carried was out carried is enlarged. out is enlarged.

2.2. MethodologyIn the lake, industrial salt production starts around mid-April by drawing water from the lakeFigure to 2the depicts salt pan. the generalThis continues framework for offour the or methodology five months useddepending in this work,on weather which isconditions, based on as Sentinel-1/-2 high temperatures and field are required survey data. for evaporation. An interferometric Salt formation stack starts of Sentinel-1 in Au- datagust acquiredand continues over Lakeuntil TuzOctober. was usedIn October, first to obtainunderground information waters on emerge the volume from dynamicsnumer- byous the crevices SBAS to method feed the using lake ENVI bed and SARscape thus the software salt production [49,50]. cycle Sentinel-2 continues. and field There survey are datafour meteorological (GPS, temperature, stations moisture, and two and dams levelling around measurements) Lake Tuz, as shown were used in Figure subsequently 1. The todams evaluate were thebuilt feasibility on the Melendiz of the method and Pecenek for Lake streams, Tuz in which terms offeed the the temporal lake. Moreover, dynamics amongthe beds its of components: the Degirmenozu water, and vegetation, Insuyu streams salt, moist were soil, modified and dry to soil. feed other lakes that wereIn about the study,to dry Sentinel-1out. It is worth and Sentinel-2noting that satellite the channel data of obtained the General freely Directorate from the of EO CopernicusState Hydraulic program Works were(DSI), used. located Short in the revisit southwest time, region which where is an seasonal important floods feature occur in monitoringevery year, is areas the only with source rapid changes,that directly was feeds available Lake inTuz. both The the lake SAR system and optical is well satelliterepre- datasented (Table on the1). eastern The Copernicus side, where Open detailed Access field Hub work has (electronic provided moisture Level-2A measurement, products of Sentinel-2spectroradiometer imagery measurement, data over Europe and fromlevellin 28g March measurement) 2017 [51]. was Sentinel-2 carried Multispectralout. Eleven Instrumentpoints of the Level Turkish 2A National (S2-MSIL2A) Fundamen imagerytal wasGPS used;Network this (TUTGA) delivered are atmospherically homogeneously and geometricallydistributed in the corrected study area, bottom-of-atmosphere and GPS measurements (BOA) taken reflectance from the images. salt pans in the Lake Tuz are used as calibration data for SBAS results in the interferogram calibration stage; these data were obtained from the General Directorate of Mapping (HGM).

2.2. Methodology Figure 2 depicts the general framework of the methodology used in this work, which is based on Sentinel-1/-2 and field survey data. An interferometric stack of Sentinel-1 data acquired over Lake Tuz was used first to obtain information on the volume dynamics by

Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 19

the SBAS method using ENVI SARscape software [49,50]. Sentinel-2 and field survey data Remote Sens. 2021, 13, 2701 (GPS, temperature, moisture, and levelling measurements) were used subsequently5 ofto 19 evaluate the feasibility of the method for Lake Tuz in terms of the temporal dynamics among its components: water, vegetation, salt, moist soil, and dry soil.

FigureFigure 2. 2. WorkflowWorkflow implemented implemented for for the the water water level level monitoring. monitoring.

Table 1. PropertiesIn the of study, Sentinel-1/-2. Sentinel-1 Text and highlighted Sentinel-2 in * satellite refers to thedata chosen obtained features. freely from the EO Co- pernicus program were used. Short revisit time, which is an important feature in moni- Satellite (Optical) Spectraltoring Res. areas (µm) with rapid Spatialchanges, Res. was (m) available in Rad.both Res. the (bit)SAR and optical Temp. satellite Res. (Day) data (Table 1). The Copernicus* B2, B3,Open B4, Access B8:10 m Hub has provided Level-2A products of Sentinel- 13 Bands * B5, B6, B7, B8a, B11, Sentinel 2 MSI 2 imagery data over Europe from 28 March 2017 [51]. Sentinel-212 Multispectral Instrument 5 Level(0.44–2.20) 2A (S2-MSIL2A) imageryB12:20m was used; this delivered atmospherically and geometri- B1, B9, B10: 60 m cally corrected bottom-of-atmosphere (BOA) reflectance images. Satellite (SAR) Polarization * Spatial Res. (m) Incidence Angle (◦) Temp. Res. (Day) Sentinel-1 TableHH, 1. HV, Properties of Sentinel-1/-2.5 (ground range) Text ×highlighted20 in bold refers to the chosen features. 29.1◦–46.0◦ 6 (C band) * VV, VH (azimuth) Satellite (Optical) Spectral Res. (µm) Spatial Res. (m) Rad. Res. (bit) Temp. Res. (Day) *B2, B3, B4, B8:10 m 13 Bands Sentinel 2 MSI The Sentinel-1*B5, InterferometricB6, B7, B8a, B11, Wide B12:20m (IW) swath mode12 with C-band (radar 5 frequency (0.44–2.20)of 5.4 GHz) SAR sensors were considered for applying the SBAS method and determining B1, B9, B10: 60 m the water level. Thirty-four ascending VV polarized Sentinel-1 (orbit no 87) images were Satellite (SAR) Polarization* Spatial Res. (m) Incidence Angle (°) Temp. Res. (Day) used, covering the period from 30 November 2017 to 12 January 2019. VV polarization Sentinel-1 HH, HV, was preferred out5 (ground of the tworange)×20(azimuth) polarization options because 29.1°–46.0° it produces better 6 results in (C band) *VV,wetlands VH [52]. Since the use of ascending and descending tracks in flat basins such as Lake Tuz gave consistently similar results, a single track was preferred in this study [53]. The SBAS technique is a DInSAR approach that uses a large number of SAR acqui- sitions, based the increase in the quantity of existing data sets and the developments in interferometric processes, thus minimizing the potential for negative data features such as decorrelation, atmospheric propagation delay, and topographic errors. SBAS uses singular Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 19

The Sentinel-1 Interferometric Wide (IW) swath mode with C-band (radar frequency of 5.4 GHz) SAR sensors were considered for applying the SBAS method and determining the water level. Thirty-four ascending VV polarized Sentinel-1 (orbit no 87) images were used, covering the period from 30 November 2017 to 12 January 2019. VV polarization was preferred out of the two polarization options because it produces better results in wetlands [52]. Since the use of ascending and descending tracks in flat basins such as Lake Tuz gave consistently similar results, a single track was preferred in this study [53]. The SBAS technique is a DInSAR approach that uses a large number of SAR acquisi- Remote Sens. 2021, 13, 2701 tions, based the increase in the quantity of existing data sets and the developments6 of in 19 interferometric processes, thus minimizing the potential for negative data features such as decorrelation, atmospheric propagation delay, and topographic errors. SBAS uses sin- gularvalue value decomposition decomposition (SVD) (SVD) to connect to connect independent independent interferograms interferograms in time, in correctingtime, correct- for ingatmospheric for atmospheric and topographic and topographic effects. TUTGAeffects. TUTGA points in points the region in the and region salt and pans salt in thepans lake in thewere lake used were asdistributed used as distributed scatterer (DS)scatterer candidates. (DS) candidates. These DS candidatesThese DS candidates add an advantage add an advantageto the study to areathe study compared area compared to other lakes.to othe Interferogramr lakes. Interferogram pairs were pairs generated were generated with a with0.45 coherencea 0.45 coherence parameter parameter threshold threshold and then and removed then removed to the topographic to the topographic phase using phase the usingshuttle the radar shuttle topographic radar topographic mission (SRTM) mission height (SRTM) as theheight topographic as the topographic reference data,reference then data,the Wavelet then the functionality Wavelet functionality atmospheric atmospheric correction correction was applied. was The applied. interferogram The interfero- stack gramwas unwrapped stack was unwrapped and the slant and range the slant was range defined was as defined multi-look. as multi-look. Ground control Ground points con- trolwere points selected were to refineselected the to orbits refine and the remove orbits and possible remove phase possible ramps phase from ramps the unwrapped from the unwrappedphase stack. phase Temporal stack. coherence, Temporal calculated coherence, on calculated the random on motion the random of scatterers motion within of scat- a terersresolution within cell, a resolution was considered cell, was as one considered univariate as exponentialone univariate function exponential of time. function It ranged of time.from 0It (withoutranged from reliable 0 (without information) reliable to 1in (reliableformation) information to 1 (reliable without information noise). A without thresh- noise).old was A usedthreshold to ensure was used that theto ensure selected that SAR the pixelsselected give SAR reliable pixels andgive consistent reliable and results. con- sistentTherefore, results. reliable Therefore, SAR pixels reliable were SAR selected pixels by were setting selected a threshold by setting value a threshold of 0.45 to value remove of 0.45unreliable to remove pixels unreliable from the SBASpixels results from the derived SBAS from results the derived Sentinel-1 from data the [54 Sentinel-1,55]. The SBAS data [54,55].inversion The kernel SBAS wasinversion then applied kernel was to determine then applied the to initial determine displacement the initial results displacement and the resultsresidual and topography. the residual In the topography. second inversion In the stage,second the inversion SBAS inversion stage, the kernel SBAS was inversion applied kernelto generate was applied the displacement to generate time the series. displaceme By applyingnt time the series. geometric By applying correction, the thegeometric results correction,were transformed the results on thewere earth transformed coordinate on system the earth (see coordinate Figure2). system (see Figure 2). InIn order to reduce the temporal decorrelat decorrelationion and increase the number of coherent samples,samples, SAR images werewere selectedselected accordingaccording to to their their spatial spatial and and temporal temporal baselines. baselines. As As a aresult, result, a totala total number number of 128of 128 interferograms interferograms were were generated, generated, and and time time series series was producedwas pro- ducedfrom all from images all images in the statedin the period,stated period, using a using perpendicular a perpendicular baseline baseline below 100below meters 100 me- and terstemporal and temporal baseline baseline below 60 below days (Figure60 days3 ).(Figure 3).

Figure 3. Temporal (x-axis) and perpendicular (y-axis) baseline of the Sentinel-1 acquisitions (red points). Individual SBAS interferogram pairs are shown by blue lines between every two differ- ent points.

To analyze the potential of the SBAS methodology for water level monitoring in the Lake Tuz environment, Sentinel-2 images were used to understand the dynamics of the salt lake components and their impact on the SBAS measurements. Four Sentinel-2 images with 10 m and 20 m resolutions were selected to represent seasonal variations: 2 February, 17 April, 10 August and 29 October, 2018. The water withdrawal periods and salt harvesting were also taken into consideration. Remote Sens. 2021, 13, x FOR PEER REVIEW 7 of 19

Figure 3. Temporal (x-axis) and perpendicular (y-axis) baseline of the Sentinel-1 acquisitions (red points). Individual SBAS interferogram pairs are shown by blue lines between every two different points.

To analyze the potential of the SBAS methodology for water level monitoring in the Lake Tuz environment, Sentinel-2 images were used to understand the dynamics of the salt lake components and their impact on the SBAS measurements. Four Sentinel-2 images with 10 m and 20 m resolutions were selected to represent seasonal variations: 2 February, 17 April, 10 August and 29 October, 2018. The water withdrawal periods and salt harvest-

Remote Sens. 2021, 13, 2701 ing were also taken into consideration. 7 of 19

2.3. Field Surveys As shown in highlighted part of Figure 1, field work was conducted in the northeast 2.3. Field Surveys region of Lake Tuz, which has reflective features characteristic of the entire lake [56–58]. The fieldAs shownsurveys in were highlighted carried part out ofin FigureOctober,1, field when work the was region conducted was in inthe the driest northeast season; February,region of when Lake Tuz,the region which was has reflectiveat its wettest; features and characteristic in the transition of the months entire lakebetween [56– 58these]. twoThe seasons: field surveys April wereand July. carried Thus, out optimum in October, re whenference the data region were was obtained in the driest by performing season; February, when the region was at its wettest; and in the transition months between these measurements with levelling and GPS for comparison with the SBAS results when the two seasons: April and July. Thus, optimum reference data were obtained by performing water was at a minimum and maximum level. measurements with levelling and GPS for comparison with the SBAS results when the waterSpectroradiometer was at a minimum measurements and maximum were level. carried out to better classify the satellite im- ages andSpectroradiometer to provide the correlations measurements between were carried the ground out to betterand satellite classify data the satellite simultaneously images withand the to providesatellite the pass correlations time on 17April between 2018, the ground when information and satellite dataon all simultaneously the salt lake compo- with nentsthe satellitecould be pass obtained. time on 17The April spectra 2018, of when the informationsalt lake components on all the salt were lake componentsobtained by a handheldcould be spectroradiometer, obtained. The spectra an of Analytical the salt lake Spectral components Device were (ASDInc., obtained Boulder by a handheld CO, USA) withspectroradiometer, a spectral range anof Analytical325–1075 nm, Spectral which Device were used (ASDInc., as ground-truth Boulder CO, reference USA) with spectra a forspectral temporal range Sentinel-2 of 325–1075 image nm, classification. which were Reflectance used as ground-truth calibration reference was done spectra with white for referencetemporal material, Sentinel-2 and image spectral classification. measurements Reflectance were conducted calibration at was 15 done sample with points white for eachreference class, material,enabling andthe extraction spectral measurements of the average were radiance conducted of the at 15components sample points for forthe each wave- lengthclass, in enabling the range the extractionof 325–1075 of thenm. average At each radiance sample of point, the components the temperature for the wavelengthand moisture ofin the the soil range was of also 325–1075 recorded nm. with At each a KCB-300 sample Portable point, the Soil temperature Survey Instrument and moisture and of a themois- soil was also recorded with a KCB-300 Portable Soil Survey Instrument and a moisture ture PH meter. Figure 4 shows the mean spectral reflectance curve and the wavelength PH meter. Figure4 shows the mean spectral reflectance curve and the wavelength ranges ranges corresponding to the Sentinel-2 bands. The graph shows Sentinel-2 bands (exclud- corresponding to the Sentinel-2 bands. The graph shows Sentinel-2 bands (excluding ing SWIR bands) with 10m and 20m spatial resolution covering the wavelength range of SWIR bands) with 10 m and 20 m spatial resolution covering the wavelength range of the thespectroradiometer. spectroradiometer.

FigureFigure 4. Spectral 4. Spectral curves curves of ofthe the salt salt lake lake components components from from ASD, ASD, andand spatialspatial resolutionresolution of of each each of of the the spectral spectral bands bands used used fromfrom Sentinel-2. Sentinel-2.

The precise geometric levelling technique was performed in four field surveys to investigate the potential of the SBAS method used in determining the lake volume dy- namics. The geometric levelling sought to obtain control data with mm precision. The loop is a line measuring approximately 15 km, 8.8 km of which is inside Lake Tuz and 6.2 km outside. While these surveys were being made, 2 measurements with 2 different rods were made in same each point with only one topographer and the average was taken. In the measurements made in this lake, where the slope is very low, there was very little difference between the 2-point measurements that were averaged. Geometric levelling measurement results, consisting of north-south and east-west directions, were obtained for a total of 250 points were recorded among 356 benchmarks. Thanks to GPS/Leveling Remote Sens. 2021, 13, x FOR PEER REVIEW 8 of 19

The precise geometric levelling technique was performed in four field surveys to in- vestigate the potential of the SBAS method used in determining the lake volume dynam- ics. The geometric levelling sought to obtain control data with mm precision. The loop is a line measuring approximately 15 km, 8.8 km of which is inside Lake Tuz and 6.2 km outside. While these surveys were being made, 2 measurements with 2 different rods were made in same each point with only one topographer and the average was taken. In the Remote Sens. 2021, 13, 2701 measurements made in this lake, where the slope is very low, there was very little differ-8 of 19 ence between the 2-point measurements that were averaged. Geometric levelling meas- urement results, consisting of north-south and east-west directions, were obtained for a total of 250 points were recorded among 356 benchmarks. Thanks to GPS/Leveling meas- measurements, the positions of each rod measurements were recorded. Measurements urements, the positions of each rod measurements were recorded. Measurements were were made from the same places in subsequent field surveys. In addition, safeguarded and made from the same places in subsequent field surveys. In addition, safeguarded and sta- stable benchmarks were taken as reference outside the lake. As a result of this technique, ble benchmarks were taken as reference outside the lake. As a result of this technique, the the network was closed with a 2 cm error limit below the tolerance limit of 5.8 cm. Figure5 network was closed with a 2 cm error limit below the tolerance limit of 5.8 cm. Figure 5 shows two images taken from the area close to the shore of the lake. In addition, it was shows two images taken from the area close to the shore of the lake. In addition, it was seen that it would be more appropriate and faster to take geometric leveling measurements seen that it would be more appropriate and faster to take geometric leveling measure- with a barcoded invar rod. The water-covered area in the image on the left turned into ments with a barcoded invar rod. The water-covered area in the image on the left turned a salt-covered area in the dry period due to evaporation, as can be seen in the image on into a salt-covered area in the dry period due to evaporation, as can be seen in the image the right. on the right.

FigureFigure 5.5. ExamplesExamples ofof levelling measurements during during wet wet and and dry dry season season (The (The left left picture picture shows shows a a 9 cm water depth; the right picture shows the salt surface.). 9 cm water depth; the right picture shows the salt surface.).

3.3. ResultsResults 3.1.3.1. AnalysisAnalysis ofof Sentinel-1Sentinel-1 SBAS Measurement TheThe LOSLOS directiondirection velocity map, derived derived from from 128 128 interferograms interferograms with with SBAS, SBAS, is is shownshown inin Figure 66a.a. Pixels Pixels ha havingving a acoherence coherence value value less less than than 0.45 0.45 are aremasked masked out in out the in thefinal final displacement displacement map, map, which which mainly mainly sows sows water water and andmoist moist saline saline lands. lands. Velocities Velocities in inthe the positive positive direction direction are areshown shown in green in green with a with maximum a maximum of 2.75 ofcm/year, 2.75 cm/year, while veloci- while velocitiesties in the in negative the negative direction direction are shown are shownin red with in red a minimum with a minimum of −2.75 ofcm/year.−2.75 cm/year.Signifi- Significantcant surface surface variation variation was evident was evident around around Lake Tuz, Lake especially Tuz, especially in the inmiddle the middle of the lake of the lakeand andwhere where the thesalt saltpans pans are arelocated. located. Althou Althoughgh there there were were a limited a limited number number of ofsamples samples from the inner part of the lake, there were enough to see surface variation trends due to salt-related activities. In general, one of the most difficult challenges of InSAR in wetland monitoring is decorrelation, due to the complex interaction of microwaves, the morphology of the canopy

and the water. However, in the context of salt lakes, the high salt content of the water makes interferometry measurements more stable. The photographs taken in the field in Figure6c show the main surface characteristics of the lake. The first surface, shown in photograph P1, had the highest volume of salt density suspended in water, which led to the limited information from the area. At the P2 point, it is seen that salt masses are collected and much information was obtained, as can be clearly seen in the area with a water height of about 10 cm. The dry soil and pure salt surfaces represented by photographs P3 and P4 respectively, were characterized by high SBAS measurements due to the high amount Remote Sens. 2021, 13, x FOR PEER REVIEW 9 of 19

from the inner part of the lake, there were enough to see surface variation trends due to salt-related activities. In general, one of the most difficult challenges of InSAR in wetland monitoring is decorrelation, due to the complex interaction of microwaves, the morphology of the can- opy and the water. However, in the context of salt lakes, the high salt content of the water makes interferometry measurements more stable. The photographs taken in the field in Figure 6c show the main surface characteristics of the lake. The first surface, shown in photograph P1, had the highest volume of salt density suspended in water, which led to the limited information from the area. At the P2 point, it is seen that salt masses are col- Remote Sens. 2021, 13, 2701 9 of 19 lected and much information was obtained, as can be clearly seen in the area with a water height of about 10 cm. The dry soil and pure salt surfaces represented by photographs P3 and P4 respectively, were characterized by high SBAS measurements due to the high ofamount stable of information. stable information. Figure6b Figure shows 6b the sh spatialows the distribution spatial distribution of the standard of the deviations standard ofdeviations the SBAS of measurements. the SBAS measurements. While the standardWhile the deviationstandard isdeviation high in is the high lake, in whichthe lake, has awhich dynamic has structure,a dynamic thestructure, salt pans the in salt the pans lake in are the the lake opposite, are theand opposite, have highand have reflectivity. high Althoughreflectivity. the Although maximum the standardmaximum deviation standard ofdeviation the measurement of the measurement reached 4.88reached cm/year, 4.88 60%cm/year, of the 60% measurements of the measurements had a standard had a deviationstandard deviation less than 1less cm/year, than 1 highlightingcm/year, high- the reliabilitylighting the of reliability the measurements. of the measurements.

FigureFigure 6.6. SBAS LOS LOS direction direction velocity velocity map map for for the the 2017–2018 2017–2018 period. period. The The final final map map is presented is presented byby cuttingcutting Lake Tuz and its surroundings surroundings (approximately (approximately 1900 1900 km km2).2 Positive). Positive velocities velocities (in (ingreen) green) representrepresent thethe motionmotion of the ground toward the the satel satellite,lite, while while negative negative velocities velocities (in (in red) red) represent represent motionmotion away from the satellite satellite ( (aa).). The The spatial spatial distribution distribution of ofthe the standard standard deviation deviation of the of theSBAS SBAS measurementsmeasurements ((bb).). Photographs taken around around Lake Lake Tu Tuzz which which have have different different structures structures ( (cc).). 3.2. The Influence of Temporal Variations of the Salt Lake Components on the SBAS Results To understand the seasonal transition and influence of the salt industry on the salt lake components (water, salt, vegetation, moist soil, and dry soil), Sentinel-2 images were classified with a Support Vector Machine (SVM) classifier. The SVM classifier was chosen due to its simplicity and high performance with few samples in the wetland environment [59–61]. In the classification process with the SVM, the radial basis function kernel was preferred, and optimum parameter values were determined by the cross- validation method. In the field surveys carried out in different parts of the lake, class information about the region was collected by hand GPS and spectroradiometer; half of this was used as training data at the classification stage the other half was used as ground truth data. The classification performance of the SVM models obtained was calculated using the test data sets. According to the 100 ground truth data, the overall accuracy of the SVM Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 19

3.2. The influence of Temporal Variations of the Salt Lake Components on the SBAS Results To understand the seasonal transition and influence of the salt industry on the salt lake components (water, salt, vegetation, moist soil, and dry soil), Sentinel-2 images were classified with a Support Vector Machine (SVM) classifier. The SVM classifier was chosen due to its simplicity and high performance with few samples in the wetland environment [59–61]. In the classification process with the SVM, the radial basis function kernel was preferred, and optimum parameter values were determined by the cross-validation method. In the field surveys carried out in different parts of the lake, class information about the region was collected by hand GPS and spectroradiometer; half of this was used Remote Sens. 2021as, 13 training, 2701 data at the classification stage the other half was used as ground truth data. 10 of 19 The classification performance of the SVM models obtained was calculated using the test data sets. According to the 100 ground truth data, the overall accuracy of the SVM classi- fier was 88.7%, 87.9%, 86.2%, and 87.5% for the February, April, August, and October im- classifier was 88.7%, 87.9%, 86.2%, and 87.5% for the February, April, August, and October ages, respectively.images, Figure respectively. 7 shows the Figure SVM7 shows classification the SVM results classification and the results corresponding and the corresponding coherence imagescoherence derived imagesfrom the derived closest from possible the closest interferogram possible interferogram pairs. It was pairs.observed It was observed that August wasthat almost August completely was almost dry completely with no rainfall dry with (see no Figure rainfall 7). (see This Figure month7). Thissaw month saw the lowest occurrencethe lowest of water occurrence at the ofsite. water It continued at the site. like It continued that until likeOctober, that until when October, Lake when Lake Tuz started to beTuz refilled started with to bewater. refilled In Fe withbruary, water. water In February, (~40 cm water depth) (~40 was cm the depth) dominant was the dominant component of thecomponent salt lake. ofThe the mean salt lake. water The level mean and water water-covered level and water-covered area decreased area until decreased until March, after whichMarch, the afterlake whichentered the the lake dry entered period the again. dry periodThe consistency again. The between consistency the between the classification resultsclassification and the coherence results and maps the coherence can easily maps be seen: can easily there be is seen:high therecoherence is high in coherence in the salt and dry soilthe saltclasses; and drythis soildecreases classes; to this moderate decreases coherence to moderate values coherence in vegetation values in and vegetation and moist soil. The freshwatermoist soil. Theclass, freshwater as expected, class, shows as expected, the lowest shows coherence the lowest values. coherence values.

Figure 7.Figure(a,b) Sentinel-2 7. Sentinel-2 classified classified images images and the and corresponding the corresponding Sentinel-1 Sentinel-1 coherence coherence images derivedimages derived from the seasonal data. Coherencefrom the maps seasonal of shortest data. time-basedCoherence interferogramsmaps of shortest containing time-based selected interferogra Sentinel-2ms images. containing selected Sentinel-2 images. Overall, apart from water, the mean coherence values for each component are sufficient Overall, apart(>0.45) from for water, SBAS the based mean monitoring coherence (Figure values8). for The each vegetation component class are has suffi- values close to cient (>0.45) for moistSBAS soilbased and monitoring shows medium (Figur reflectivity.e 8). The vegetation It can be seen class that has the values dry soil close and salt classes show high reflectivity, whereas the water class shows low reflectivity. It was observed that in August the lake was almost completely dry, and this lasted until October. The lake started to fill with water after October and reached maximum occupancy levels in February. After this month, the lake stayed within its natural boundaries until March, after which it entered a dry period again. The coherence values of all interferogram pairs in these four periods were examined separately based on LULC classes. The values between February and March, when the lake was in its natural boundaries, are given in Figure8a. The coherence values between March and August, when the lake water started to decrease and entered the dry period, are shown in Figure8b. The August to October values, when Lake Tuz was completely dry and salt production and evaporation were high, are given in Remote Sens. 2021, 13, x FOR PEER REVIEW 11 of 19

to moist soil and shows medium reflectivity. It can be seen that the dry soil and salt classes show high reflectivity, whereas the water class shows low reflectivity. It was observed that in August the lake was almost completely dry, and this lasted until October. The lake started to fill with water after October and reached maximum occupancy levels in Febru- ary. After this month, the lake stayed within its natural boundaries until March, after which it entered a dry period again. The coherence values of all interferogram pairs in these four periods were examined separately based on LULC classes. The values between February and March, when the lake was in its natural boundaries, are given in Figure 8a. Remote Sens. 2021, 13, 2701 The coherence values between March and August, when the lake water started to decrease11 of 19 and entered the dry period, are shown in Figure 8b. The August to October values, when Lake Tuz was completely dry and salt production and evaporation were high, are given Figurein Figure8c. Finally,8c. Finally, the the October October to Februaryto February values, values, when when the the lake lake started started to to fill fill with with the the onsetonset of of rains rains and and reached reached its its maximum maximum levels, levels, are are shown shown in in Figure Figure8d. 8d.

FigureFigure 8. 8.Boxplot Boxplot ofof allall coherencecoherence valuesvalues ofof differentdifferent LULCLULC classesclasses forfor fourfour differentdifferent seasons.seasons. EachEach featurefeature is is describeddescribed byby itsits classes and different different seasons. seasons. Each Each table table contains contains the the class class names names and and statisticalstatistical info. info.

FigureFigure8 shows8 shows the the reactions reactions of the of coherence the cohere valuesnce values of the classes of the in classes seasonal in changes.seasonal Thechanges. water classThe water remained class below remained 0.4 in below the winter 0.4 seasonin the winter and high season coherence and high values coherence seemed tovalues increase seemed in the to summerincrease in season, the summer when theseason, amount when of the salt amount in the of lake salt increased in the lake and in- saltcreased formations and salt appeared formations on appeared the surface. on Thethe surface. water class The alsowater decreased class also with decreased increasing with precipitation.increasing precipitation. It can be seen It can that be the seen vegetation that the classvegetation values class remained values stable remained at 0.4–0.5 stable in at each0.4–0.5 season. in each The season. reason The for reason this is for that this the is that region the hasregion weak has salty weak flora salty and flora sparse and sparse salt- resistantsalt-resistant vegetation vegetation that isthat not is affected not affected by seasonal by seasonal changes. changes. It was It observedwas observed that thethat salt the classsalt class declined declined inversely inversely with with the increase the increase in evaporation in evaporation from from March March to October. to October. It can It becan said be said that that the saltthe salt class, class, which which increases increases with with the the decrease decrease of of evaporation, evaporation, is is the the class class mostmost affectedaffected byby evaporation. evaporation. SimilarSimilar toto thethe saltsalt class,class,the the moist moist soil soil class class changes changes with with thethe effect effect of of evaporation. evaporation. The The dry dry soil soil class class started started to to decline decline after after August August with with increasing increasing rains. There was an increase until August as precipitation decreased, and the dry soil class was at its highest level during this period. Additionally, to understand the impact of weather conditions on the coherence values, Figure9 shows the monthly mean coherence values with monthly precipitation and evapo- ration, taken from four nearby meteorology stations: Aksaray, Cihanbeyli, Eskil, and Kulu. (see Figure1). Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 19

rains. There was an increase until August as precipitation decreased, and the dry soil class was at its highest level during this period. Additionally, to understand the impact of weather conditions on the coherence val- Remote Sens. 2021, 13, 2701 ues, Figure 9 shows the monthly mean coherence values with monthly precipitation12 of and 19 evaporation, taken from four nearby meteorology stations: Aksaray, Cihanbeyli, Eskil, and Kulu. (see Figure 1).

Coherence Evaporation (mm/year) Precipitation (mm/year) 0.8 0.76 350 0.72 0.7 0.65 0.64 0.64 0.62 300 0.58 0.6 0.6 0.56 0.56 0.52 250 0.5 0.47 200 0.4 150 0.3 100 0.2 0.1 50 0 0 coh 123456789101112mm month

FigureFigure 9.9.Average Average monthlymonthly precipitation,precipitation, evapotranspiration and coherence. Meteorological Meteorological data data were obtained by mean value of Kulu, Cihanbeyli, Eskil and S. Kochisar stations around the lake. were obtained by mean value of Kulu, Cihanbeyli, Eskil and S. Kochisar stations around the lake.

InIn July, July, August, August, and and October, October, when when there there was was almost almost no no rain, rain, a a decrease decrease in in coherence coherence valuesvalues waswas observedobserved withwith thethe increase increase of of evaporation. evaporation. FromFrom November November toto March, March, when when evaporationevaporation waswas almostalmost absent,absent, inverselyinversely proportionalproportional behaviorbehavior betweenbetween precipitation precipitation datadata andand coherencecoherence data data was was observed. observed. EvenEven thoughthoughthere there are are temporal temporal relations relations among among thethe coherence, coherence, precipitation precipitation and and evaporation evaporation (Figure (Figure9 ),9), the the meteorological meteorological effects effects on on the the temporaltemporal decorrelation decorrelation can can be be ignored ignored due due to to the the presence presence of of dense dense scatters scatters satisfying satisfying the the coherencecoherence condition condition (>0.45). (>0.45). ThisThis showsshows thatthat thethe weatherweather conditionsconditions hadhad almost almost no no effect effect onon thethe C-bandC-band SBASSBAS resultsresults forfor thethe studystudy area,area, whichwhich shouldshould bebe greatergreater onon thethe shorter shorter wavelength-basedwavelength-based SBAS SBAS measurements. measurements. AlthoughAlthough thethe summersummer monthsmonths areare dry,dry, therethere isisno no a a major major change change in in soil soil moisture moisture becausebecause the the lake lake is is fed fed byby undergroundunderground water water sources. sources. In In the the field field surveys, surveys, it it was was observed observed thatthat thethe toptop layerlayer ofof the the soil soil was was dry dry when when the the moisture moisture rate rate was was about about 40% 40% at at 1.5–2 1.5–2 cm cm belowbelow the the soil. soil. The The moisture moisture content content of of soil soil samples samples taken taken from from different different parts parts of Lake of Lake Tuz wasTuz calculated.was calculated. A negative A negative linear linear correlation correlation was was observed observed between between the the backscattering backscatter- valueing value of Sentinel-1 of Sentinel-1 images images and and soil soil moisture. moisture. Additionally, Additionally, increases increases in in soil soil moisture moisture contentcontent result result in in a a decrease decrease in in coherence coherence values. values. Along Along with with surface surface roughness, roughness, this this feature fea- isture a disadvantage is a disadvantage in moist in moist areas, areas, such such as Lake as Lake Tuz, whereTuz, where evaporation evaporation occurs occurs due todue the to saltthe concentration.salt concentration. AA briefbrief evaluationevaluation ofof thethe coherencecoherence resultsresults cancan bebe found found in in Figure Figure 10 10,, which which shows shows thethe totaltotal areaarea ofof the the assigned assigned classes classes for for each each Sentinel-2 Sentinel-2 acquisition acquisitiondate. date. InIn thethe figure,figure, colorscolors distinguish distinguish the the salt salt lake lake components, components, while while the the darker darker shade shade of of each each color color describes describes thethe totaltotal area area with with a a coherence coherence value value of of greater greater than than 0.45 0.45 within within each each component. component. These These datadata producedproduced for four four different different periods periods also also show show how how these these data data were were affected affected by sea- by seasonalsonal changes. changes. It is seen that approximately 90% of the salt and dry soil classes, 70% of the moist soil, 65% of the vegetation classes, and 25% of the water class can be used for SBAS-based volume dynamics monitoring. Relationships between the meteorological data in Figures9 and 10 were examined and a countertrend was observed. It was determined that evaporation mostly affects the salt class, while the class least affected by evaporation is dry soil. Precipitation mostly affects the water class negatively. It was observed that the reliable data of the water class, which was 27.20% in the dry season, decreased to 19.52% despite the increase in total water area due to precipitation. Remote Sens. 2021, 13, 2701 13 of 19 Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 19

Ha

FigureFigure 10. The 10. totalThe totalarea areaof each of eachof the of Lake the Lake Tuz Tuzcompon componentsents in time in time (see(see Figure Figure 7). 7The). The areas areas with with a a coherencecoherence value value greater greater than than 0.45 0.45 within within these these classes classes are are shown shown in a in darker a darker color. color.

4.It Discussionsis seen that approximately 90% of the salt and dry soil classes, 70% of the moist soil, 65% Theof the joint vegetation analysis classes, of SBAS and and 25% auxiliary of the datawater (in class situ) can results be used provides for SBAS-based further insight volumeinto dynamics how the activities monitoring. on site have an impact on the measurements from SBAS. RelationshipsFor this purpose, between SBAS the meteorological and levelling measurementsdata in Figure 9 were and Figure firstly compared10 were exam- in order inedto and investigate a countertrend the accuracy was observed. and reliability It was determined of the water that level evaporation determination. mostly Afterwards, affects the saltthe class, deformation while the results class withinleast affected the salt by pans evaporation were analyzed is dry to soil. understand Precipitation the potentialmostly of affectsSentinel-1 the water to class seasonally negatively. monitor It was the observ salt paned volume that the dynamics. reliable data The of results, the water obtained class, in a whichcoordinated was 27.20% manner in the withdry season, GPS/leveling decreased measurements to 19.52% despite and the the SBAS increase results in georeferenced total wa- ter areaaccording due to toprecipitation. GPS measurements, were matched. The displacement results were projected onto the vertical unit vector using the incident angle (33.86◦). The SBAS results were 4. Discussionsthen calibrated using the TUTGA points obtained and found homogeneously in the study area.The joint Thus, analysis the SBAS of SBAS results and were auxiliary converted data into (in levellingsitu) results information provides and further compared insight with into thehow levelling the activities information on site have obtained an impact in the on field the work. measurements A total of from 50 points SBAS. were selected inFor the this field purpose, survey SBAS area withand levelling water along meas theurements levelling were line. firstly A 20-meter compared diameter in order buffer to investigatewas applied the toaccuracy each levelling and reliability point. Theof the mean water SBAS level result determination. in this area Afterwards, was compared the deformationwith the levelling results measurements. within the salt Thepans total were SBAS analyzed area usedto understand across the the 50 levellingpotential pointsof 2 Sentinel-1was 1275 to seasonally m . Figure monitor 11 compares the salt the pan water volume level dynamics. information The obtainedresults, obtained by the levelling in a coordinatedmeasurements manner in fourwith differentGPS/leveling seasons meas withurements water level and information the SBAS results obtained georefer- from SBAS. encedThe according same points to GPS are usedmeasurements, every season, were and matched. points withThe displacement a water level ofresults zero were not projectedincluded onto in the the vertical process. unit The vector standard using deviation the incident values angle obtained (33.86°). from The the SBAS SBAS results accuracy werecomparisons then calibrated in February,using the April,TUTGA August points andobtained October and were found calculated homogeneously as 0.67, in 0.80, the 0.84, studyand area. 0.95, Thus, respectively. the SBAS results were converted into levelling information and com- pared withA lowerthe levelling correlation information is observed obtained between in the the field two work. sets of A data total in of February, 50 points whenwere the water level reached a depth of 25 cm. This value increases as the water level decreases and selected in the field survey area with water along the levelling line. A 20-meter diameter the salt content in the water increases. When determining the water level at a depth of buffer was applied to each levelling point. The mean SBAS result in this area was com- 25 cm, deviations of up to 7 cm can be seen as a result of SBAS. This difference decreases pared with the levelling measurements. The total SBAS area used across the 50 levelling to 2–3 cm at 15 cm water depth, and to 0–1 cm at a depth of 8 cm. In addition, statistical points was 1275 m2. Figure 11 compares the water level information obtained by the lev- analyses show that the root mean square error (RMSE) decreases as the water level of the elling measurements in four different seasons with water level information obtained from lake decreases. RMSE values were calculated as 2.85 mm in February, 2.5 mm in April, SBAS. The same points are used every season, and points with a water level of zero were 1.5 mm in July, and 0.5 mm in October (when the water level was the lowest). The salt class not included in the process. The standard deviation values obtained from the SBAS accu- is one of the major dynamic classes among the components of a salt lake. To determine the racy comparisons in February, April, August and October were calculated as 0.67, 0.80, volume dynamics in the salt class, the common salt class areas seen in the classifications of 0.84, and 0.95, respectively. the lake in February, April, August, and October were selected for this study.

Remote Sens. 2021, 13, 2701 14 of 19 Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 19

Figure 11. Regression analysis between water levels derived by SBAS that exported vertical units Figure 11. Regression analysis between water levels derived by SBAS that exported vertical units and levelling. and levelling.

A lowerAfter correlation examining is the observed SBAS measurements between the correspondingtwo sets of data to in water February, level changes, when the SBAS- waterbased level surface reached changes a depth within of 25 cm. the saltThis pans value were increases analyzed. as the One water of the level most decreases important and com- the saltponents content of a in salt the lake water is salt, increases. which contributesWhen determining to the country’s the water economy. level atIt a candepth be seenof 25 that cm, saltdeviations formation, of up caused to 7 cm by thecan effectbe seen of evaporation,as a result of startsSBAS. in This April difference and lasts decreases until September. to 2–3 Subsequently,cm at 15 cm water with thedepth, rains, and a collapseto 0–1 cm is observed,at a depth depending of 8 cm. In on addition, the dissolution statistical of the analysessalt. Toshow calculate that the the root salt mean reserves square in Lake error Tuz, (RMSE)the (thickness) decreases as x (area)the water x (density) level of formula the lakewas decreases. used [ 48RMSE]. As values shown were in Figure calculated1, there as 2.85 are threemm in active February, salt pans 2.5 mm in the in April, lake. 1.5 There mmare in July, negative and and0.5 mm positive in October movements (when in the saltpans water duelevel to was the the formation lowest). movements The salt class of salt. is oneIn of order the tomajor make dynamic a more classes accurate among salt yield the components calculation, of DS a candidatessalt lake. To with determine coherence the volumevalues greater dynamics than in 0.6the insalt the class, vertical the co unitmmon were salt determined, class areas andseen their in the average classifica- values tionswere of the calculated. lake in February, Since these April, DS candidatesAugust, and were October determined were selected in the monthsfor this beforestudy. the salt harvestAfter examining period, they the were SBAS not measurements exposed to any corresponding external factors. to Thewater values level obtained changes, from SBAS-basedhere were surface calculated changes as salt within thickness, the thesalt totalpans areas were of analyzed. the salt fields, One andof the salt most density im- were portantincluded components in the process, of a salt and lake the is salt salt, yield which was contributes calculated. to The the salt country’s yield was economy. calculated It by can includingbe seen that the salt values formation, obtained caused from hereby the as salteffect thickness, of evaporation, the total starts areas in of April the salt and fields, lastsand until the September. salt density. Subsequently, with the rains, a collapse is observed, depending on the dissolutionYavsan of salt the pansalt. isTo located calculate to the the salt west reserves of the in lake Lake and Tuz, has the an (thickness) operating capacityx (area) of x (density)approximately formula 8.5was km used2. The [48]. salt As thicknessshown in wasFigure determined 1, there are as three 7.69 cmactive using salt the pans mean in thevalue lake. of There the SBAS are negative results obtained and positive in approximately movements5.2 in kmsaltpans2 of this due area, to the and formation the salt yield movementscalculation of salt. was In made, order giving to make a result a more of 1.438accurate million salt yield tons (Figurecalculation, 12). DS candidates with coherence values greater than 0.6 in the vertical unit were determined, and their av- erage values were calculated. Since these DS candidates were determined in the months

Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 19 Remote Sens. 2021, 13, x FOR PEER REVIEW 15 of 19

before the salt harvest period, they were not exposed to any external factors. The values obtained from here were calculated as salt thickness, the total areas of the salt fields, and salt density were included in the process, and the salt yield was calculated. The salt yield was calculated by including the values obtained from here as salt thickness, the total areas of the salt fields, and the salt density. Yavsan salt pan is located to the west of the lake and has an operating capacity of 2 Remote Sens. 2021,approximately13, 2701 8.5 km2. The salt thickness was determined as 7.69 cm using the mean 15 of 19 2 value of the SBAS results obtained in approximately 5.2 km2 of this area, and the salt yield calculation was made, giving a result of 1.438 million tons (Figure 12).

Figure 12. Salt movements obtained by SBAS results. The location of the salt pan is shown on the Figure 12. FigureSalt movements 12. Salt movements obtained by obtained SBAS results. by SBAS The locationresults. Th ofe the location salt pan of is the shown salt pan on the is shown LOS directions on the SBAS map LOS directions SBAS map in the left image, while the change in the salt height in this salt pan is in the left image,LOS directions while the SBAS change map in thein the salt left height image, in this while salt th pane change is graphically in the salt displayed height onin this the rightsalt pan (Calculations is were graphically displayed on the right (Calculations were made by converting to vertical units). made by convertinggraphically to displayed vertical units). on the right (Calculations were made by converting to vertical units).

Kayacık salt panKayacık is located salt panto the is east located of the to thelake east and of has the an lake operating and has capacity an operating of capacity of 2 2 approximately approximately11 km2. Salt thickness 11 km .was Salt determined thickness was as determined7.51 cm using as 7.51the mean cm using value the of mean value of 2 2 the SBAS resultsthe obtained SBAS results in approximately obtained in approximately 4.8 km2 of this 4.8area km andof athis salt areayield and calculation a salt yield calculation was made, givingwas a made,result givingof 1.838 a million result of tons 1.838 (Figure million 13). tons (Figure 13).

Figure 13. Salt movements obtained by SBAS results. The location of the salt pan is shown on the Figure 13. Salt movements obtained by SBAS results. The location of the salt pan is shown on the Figure 13. LOSSalt movementsdirections SBAS obtained map by in SBAS the left results. image, The while location the change of the salt in panthe salt is shown height on in the this LOS salt directions pan is SBAS map LOS directions SBAS map in the left image, while the change in the salt height in this salt pan is in the left image,graphically while displayed the change on in the the right salt height (Calculations in this salt were pan made is graphically by converting displayed to vertical on the rightunits). (Calculations were graphically displayed on the right (Calculations were made by converting to vertical units). made by converting to vertical units). Kaldırım salt pan is located to the north of the lake and has an operating capacity of Kaldırım salt panKaldırım is located salt to pan the is north located of tothe the lake north and of has the an lake operating and has capacity an operating of capacity of approximately 12 km2. Total salt thickness was determined as 7.81 cm using the mean approximately approximately12 km2. Total salt 12 kmthickness2. Total was salt thicknessdetermined was as determined 7.81 cm using as 7.81 the cmmean using the mean value of the SBAS results obtained in approximately 6.1 km2 of this area, and a salt yield value of the SBASvalue results of the obtained SBAS results in approximately obtained in approximately 6.1 km2 of this 6.1 area, km and2 of thisa salt area, yield and a salt yield calculation was made, giving a result of 1.95 million tons (Figure 14). calculation wascalculation made, giving was a made,result givingof 1.95 amillion result oftons 1.95 (Figure million 14). tons (Figure 14).

Remote Sens. 2021, 13, 2701 16 of 19 Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 19

Figure 14. FigureSalt movements 14. Salt movements obtained by obtained SBAS results. by SBAS The results. location Th ofe the location salt pan of isthe shown salt pan on theis shown LOS direction on the SBAS map in the left image,LOS direction while the SBAS change map in in the the salt left height image, in thiswhile salt the pan change is graphically in the salt displayed height in on this the salt right pan (Calculations is were made by convertinggraphically to displayed vertical units).4. on the Conclusions.right (Calculations were made by converting to vertical units).4. Con- clusions. 5. Conclusions In this study, theIn potential this study, of thefreely potential available of freely interferometric available interferometric stacks of Sentinel-1 stacks data, of Sentinel-1 data, coupled with SBAScoupled methodology, with SBAS was methodology, evaluated wasas a evaluatedway to monitor as a way volume to monitor dynamics volume dynamics in a salt lake environment.in a salt lake To environment. achieve this To, the achieve interferometric this, the interferometric stacking technique stacking was technique was applied to 34 C-bandapplied VV to polarized 34 C-band images VV polarized taken over images Lake taken Tuz overin an Lake annual Tuz period. in an annual SBAS period. SBAS measurement points,measurement namely points,distributed namely scattere distributedrs which scatterers could be which attributed could to be salt attributed lake to salt lake activities were monitoredactivities were to understand monitored tothe understand water-surface the water-surfacemovement. These movement. SBAS-based These SBAS-based surface movementssurface were movements in line with were in insitu line measurements with in situ measurements of water level of change. water levelThe change. The regression analysisregression showed analysis a good showed fit, with a good an accuracy fit, with an of accuracy 1–5 cm ofbetween 1–5 cm betweenthe SBAS- the SBAS-based based and the inand situ the measurements in situ measurements which exported which vertical exported units, vertical underlining units, underlining the poten- the potential tial of freely availableof freely Sentinel-1 available data Sentinel-1 to detect data surface to detect water surface level water and levelsalt lake and saltcompo- lake components. nents. Three saltThree pans salt in pansthe lake in the were lake investigated were investigated to examine to examine the themovements movements of ofsalt, salt, clearly one clearly one of theof themost most important important components components of salt of saltlakes. lakes. It was It was seen seen that that the thethickness thickness of the salt of the salt emergingemerging under under the effect the effect of evap of evaporation,oration, starting starting in inApril, April, reached reached approxi- approximately 4 cm. The amount of salt production was calculated using the thickness of salt, and revealed an mately 4 cm. The amount of salt production was calculated using the thickness of salt, and average of 5 million tons throughout the lake. This figure coincides with the number given revealed an average of 5 million tons throughout the lake. This figure coincides with the by the salt businesses. number given by the salt businesses. The impact of seasonal changes on the SBAS measurements was analyzed, showing The impact of seasonal changes on the SBAS measurements was analyzed, showing coherence with the methodological data and Sentinel-2 images. A negative correlation was coherence with the methodological data and Sentinel-2 images. A negative correlation found between evapotranspiration and coherence values, which determines the quality was found betweenof the evapotranspiration measurements. Even and though coherence the variancevalues, which of coherence determines values the was qual- high due to the ity of the measurements.high evapotranspiration Even though the from variance late August of coherence to the end values of October, was high the meandue to coherence over the high evapotranspirationthe lake was always from late higher August than to 0.7, the excluding end of October, pure water the surfaces. mean coherence The Sentinel-2 images over the lake waswere always then usedhigher to studythan 0.7, the excluding relationship pure between water the surfaces. quality The of SBAS Sentinel-2 measurements and images were thendynamic used to salt study lake the components relationship (water, between vegetation, the quality moist of soil,SBAS dry measure- soil, and salt) in the ments and dynamiccontext salt of lake coherence components measurements. (water, vegetation, In the summer moist season, soil, dry although soil, and the salt) highest decrease in the context ofdue coherence to evaporation measurements. was in the In salt the and summer moist soil season, classes, although this value the did highest not fall below 0.7 in decrease due tothe evaporation salt class, orwas below in the 0.5 salt in theand moist moist soil soil class. classes, On thethis othervalue hand, did not in thefall water class, it below 0.7 in thereached salt class, higher or below values 0.5 with in thethe increasemoist soil of saltclass. content On the in other water hand, following in the evaporation. water class, it reachedAs higher a final values remark, with it should the increase be emphasized of salt content that the in SBASwater methodfollowing can be used for evaporation. observation of the volume dynamics of salt lakes as there is no vegetation and they have As a final highlyremark, reflective it should classes. be emphasized It was seen that in the the SBAS study method that in can order be toused get for good results in observation of thewetlands, volume the dynamics interferogram of salt pairslakes toas bethere used is inno thevege SBAStation process and they were have a perpendicular highly reflectivebaseline classes. belowIt was 100seen meters in the andstudy a temporalthat in order baseline to get below good 30results days. in In wet- addition, it was lands, the interferogramobserved pairs thatsufficient to be used data in the can SBAS be obtained process towere extract a perpendicular information, base- even in the water line below 100 metersclass due and to a itstemporal salt content. baseline The below SBAS 30 method days. In has addition, proved it suitablewas observed for monitoring the that sufficient datavolume can dynamicsbe obtained of to components, extract information, such as water even levelin the and water salt class movements, due to in salt lakes. its salt content. The SBAS method has proved suitable for monitoring the volume dynam- ics of components, such as water level and salt movements, in salt lakes. The NASA–ISRO

Remote Sens. 2021, 13, 2701 17 of 19

The NASA–ISRO Synthetic Aperture Radar (NISAR) mission, which will provide data to its users in both the L band and the S band, will make an important contribution to volume dynamics and water level for future works.

Author Contributions: Conceptualization, N.M.; methodology, B.B.B. and E.E.; software, B.B.B.; validation, B.B.B., E.E. and N.M.; formal analysis, B.B.B.; investigation, B.B.B. and E.E.; resources, B.B.B.; data curation, B.B.B.; writing—original draft preparation, B.B.B.; writing—review and editing, E.E. and N.M.; visuali-zation, B.B.B.; supervision, E.E. and N.M.; project administration, N.M. All authors have read and agreed to the published version of the manuscript. Funding: ITU BAP Project number MGA-2017-40803. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable. Acknowledgments: The authors would like to express their thanks to Istanbul Technical University (ITU), Scientific Research Project Funding for their financial support to ITU BAP Project number MGA- 2017-40803.Also thank the European Space Agency (ESA) for providing SAR images (Sentinel-1) and grateful to Harris Geospatial Solutions for providing the SARscape software. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Messager, M.L.; Lehner, B.; Grill, G.; Nedeva, I.; Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 2016, 7, 13603. [CrossRef] 2. Pedersen, E.; Weisner, S.E.; Johansson, M. Wetland areas’ direct contributions to residents’ well-being entitle them to high cultural ecosystem values. Sci. Total Environ. 2019, 646, 1315–1326. [CrossRef] 3. Canisius, F.; Brisco, B.; Murnaghan, K.; Van Der Kooij, M.; Keizer, E. SAR backscatter and InSAR coherence for monitoring wetland extent, flood pulse and vegetation: A study of the Amazon lowland. Remote Sens. 2019, 11, 720. [CrossRef] 4. Tiner, R.W. Wetlands: An Overview. In Remote Sensing of Wetlands: Applications and Advances, 1st ed.; Tiner, R.W., Lang, M.W., Klemas, V.V., Eds.; CRC Press: Boca Raton, FL, USA, 2015; pp. 3–18. 5. Ding, X.; Li, X. Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 894–901. [CrossRef] 6. Hu, S.; Niu, Z.; Chen, Y.; Li, L.; Zhang, H. Global wetlands: Potential distribution, wetland loss, and status. Sci. Total Environ. 2017, 586, 319–327. [CrossRef] 7. Rezaeianzadeh, M.; Kalin, L.; Hantush, M.M. An Integrated Approach for Modeling Wetland Water Level: Application to a Headwater Wetland in Coastal Alabama, USA. Water 2018, 10, 879. [CrossRef] 8. Van der Valk, A.G.; Volin, J.C.; Wetzel, P.R. Predicted changes in interannual water level fluctuations due to climate change and its implications for the vegetation of the Florida Everglades. Environ. Manag. 2015, 55, 799–806. [CrossRef][PubMed] 9. Liao, H.; Wdowinski, S.; Li, S. Regional-scale hydrological monitoring of wetlands with Sentinel-1 InSAR observations: Case study of the South Florida Everglades. Remote Sens. Environ. 2020, 251, 112051. [CrossRef] 10. Hong, S.H.; Wdowinski, S.; Kim, S.W. Evaluation of TerraSAR-X observations for wetland InSAR application. IEEE Trans. Geosci. Remote Sens. 2010, 48, 864–873. [CrossRef] 11. Schwatke, C.; Dettmering, D.; Bosch, W.; Seitz, F. DAHITI-an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry. Hydrol. Earth Syst. Sci. 2015, 19, 4345–4364. [CrossRef] 12. Yagmur, N.; Bilgilioglu, B.B.; Dervisoglu, A.; Musaoglu, N.; Tanik, A. Long and short–term assessment of surface area changes in saline and freshwater lakes via remote sensing. Water Environ. J. 2020, 35, 107–122. [CrossRef] 13. Woodcock, C.E.; Allen, R.; Anderson, M.; Belward, A.; Bindschadler, R.; Cohen, W.B.; Gao, F.; Goward, S.N.; Helder, D.; Helmer, E.; et al. Free access to Landsat imagery. Sci. VOL 2008, 320, 1011. [CrossRef][PubMed] 14. Copernicus. Copernicus in Detail. Available online: https://www.copernicus.eu/en/about-copernicus/copernicus-detail (accessed on 10 February 2021). 15. Calò, F.; Abdikan, S.; Görüm, T.; Pepe, A.; Kiliç, H.; Balik ¸Sanli,F. The space-borne SBAS-DInSAR technique as a supporting tool for sustainable urban policies: The case of Istanbul Megacity, Turkey. Remote Sens. 2015, 7, 16519–16536. [CrossRef] 16. Halicioglu, K.; Erten, E.; Rossi, C. Monitoring deformations of Istanbul metro line stations through Sentinel-1 and levelling observations. Environ. Earth Sci. 2021, 80, 361. [CrossRef] 17. Bioresita, F.; Puissant, A.; Stumpf, A.; Malet, J.P. A method for automatic and rapid mapping of water surfaces from Sentinel-1 imagery. Remote Sens. 2018, 10, 217. [CrossRef] 18. Imamoglu, M.; Kahraman, F.; Cakir, Z.; Sanli, F.B. Ground deformation analysis of Bolvadin (W. Turkey) by means of Multi- Temporal InSAR techniques and Sentinel-1 data. Remote Sens. 2019, 11, 1069. [CrossRef] Remote Sens. 2021, 13, 2701 18 of 19

19. Rotta, L.H.S.; Alcantara, E.; Park, E.; Negri, R.G.; Lin, Y.N.; Bernardo, N.; Mendes, T.S.G.; Souza Filho, C.R. The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil. Int. J. Appl. Earth Obs. Geoinf. 2020, 90, 102119. [CrossRef] 20. Orhan, O.; Oliver-Cabrera, T.; Wdowinski, S.; Yalvac, S.; Yakar, M. Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study. Sensors 2021, 21, 774. [CrossRef] 21. Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 2012, 120, 25–36. [CrossRef] 22. Astola, H.; Häme, T.; Sirro, L.; Molinier, M.; Kilpi, J. Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens. Environ. 2019, 223, 257–273. [CrossRef] 23. D’Odorico, P.; Gonsamo, A.; Damm, A.; Schaepman, M.E. Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity. IEEE Trans. Geosci. Remote. Sens. 2013, 51, 1336–1348. [CrossRef] 24. A24 Hedley, J.D.; Roelfsema, C.; Brando, V.; Giardino, C.; Kutser, T.; Phinn, S.; Mumby, P.J.; Barrilero, O.; Laporte, J.; Koetz, B. Coral reef applications of Sentinel-2: Coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8. Remote. Sens. Environ. 2018, 216, 598–614. [CrossRef] 25. Veloso, A.; Mermoz, S.; Bouvet, A.; Le Toan, T.; Planells, M.; Dejoux, J.F.; Ceschia, E. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 2017, 199, 415–426. [CrossRef] 26. Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The first wetland inventory map of newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the google earth engine cloud computing platform. Remote Sens. 2019, 11, 43. [CrossRef] 27. Slagter, B.; Tsendbazar, N.E.; Vollrath, A.; Reiche, J. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. Int. J. Appl. Earth Obs. Geoinf. 2020, 86, 102009. [CrossRef] 28. Shi, X.; Yang, C.; Zhang, L.; Jiang, H.; Liao, M.; Zhang, L.; Liu, X. Mapping and characterizing displacements of active loess slopes along the upstream Yellow River with multi-temporal InSAR datasets. Sci. Total Environ. 2019, 674, 200–210. [CrossRef] 29. Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20. [CrossRef] 30. Erten, E.; Rossi, C. The worsening impacts of land reclamation assessed with Sentinel-1: The Rize (Turkey) test case. Int. J. Appl. Earth Obs. Geoinf. 2019, 74, 57–64. [CrossRef] 31. Du, Z.; Ge, L.; Ng, A.H.M.; Zhu, Q.; Horgan, F.G.; Zhang, Q. Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach. Sci. Total Environ. 2020, 717, 137125. [CrossRef][PubMed] 32. Selvakumaran, S.; Rossi, C.; Barton, E.; Middleton, C.R. Interferometric Synthetic Aperture Radar (InSAR) in the Context of Bridge Monitoring. In Advances in Remote Sensing for Infrastructure Monitoring, 1st ed.; Singhroy, V., Ed.; Springer: Cham, Germany, 2020; pp. 183–209. 33. Ciampalini, A.; Solari, L.; Giannecchini, R.; Galanti, Y.; Moretti, S. Evaluation of subsidence induced by long-lasting buildings load using InSAR technique and geotechnical data: The case study of a Freight Terminal (Tuscany, Italy). Int. J. Appl. Earth Obs. Geoinf. 2019, 82, 101925. [CrossRef] 34. Xie, C.; Xu, J.; Shao, Y.; Cui, B.; Goel, K.; Zhang, Y.; Yuan, M. Long term detection of water depth changes of coastal wetlands in the Yellow River Delta based on distributed scatterer interferometry. Remote Sens. Environ. 2015, 164, 238–253. [CrossRef] 35. Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383. [CrossRef] 36. Osmano˘glu,B.; Sunar, F.; Wdowinski, S.; Cabral-Cano, E. Time series analysis of InSAR data: Methods and trends. ISPRS J. Photogramm. Remote Sens. 2016, 115, 90–102. [CrossRef] 37. Orhan, O. Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey. Environ. Monit. Assess. 2021, 193, 174. [CrossRef][PubMed] 38. Caló, F.; Notti, D.; Galve, J.P.; Abdikan, S.; Görüm, T.; Pepe, A.; Balik ¸Sanli, F. Dinsar-Based detection of land subsidence and correlation with groundwater depletion in Konya Plain, Turkey. Remote Sens. 2017, 9, 83. [CrossRef] 39. Chen, Z.; White, L.; Banks, S.; Behnamian, A.; Montpetit, B.; Pasher, J.; Duffe, J.; Bernard, D. Characterizing marsh wetlands in the Great Lakes Basin with C-band InSAR observations. Remote Sens. Environ. 2020, 242, 111750. [CrossRef] 40. Xiang, W.; Zhang, R.; Liu, G.; Wang, X.; Mao, W.; Zhang, B.; Bao, J.; Cai, J.; Fu, Y. Extraction and analysis of saline soil deformation in the Qarhan Salt Lake region (in Qinghai, China) by the sentinel SBAS-InSAR technique. Geod. Geodyn. 2021. Available online: https://www.sciencedirect.com/science/article/pii/S1674984720300859 (accessed on 5 January 2021). 41. Hong, S.H.; Wdowinski, S.; Kim, S.W.; Won, J.S. Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR). Remote Sens. Environ. 2010, 114, 2436–2447. [CrossRef] 42. Kaya, Z. Potential natural heritage sites in Turkey. In World Heritage, 1st ed.; Rössle, M., Ed.; DL Services: Sint Pieters Leeuw, Belgium, 2016; Volume 80, pp. 84–89. 43. Ekercin, S.; Ormeci, C. Estimating soil salinity using satellite remote sensing data and real-time field sampling. Environ. Eng. Sci. 2008, 25, 981–988. [CrossRef] 44. Orhan, O.; Dadaser-Celik, F.; Ekercin, S. Investigating land surface temperature changes using Landsat-5 data and real-time infrared thermometer measurements at Konya closed basin in Turkey. Int. J. Eng. Geosci. 2019, 4, 16–27. Remote Sens. 2021, 13, 2701 19 of 19

45. Kilic, O.; Kilic, A.M. Salt crust mineralogy and geochemical evolution of the salt lake (Tuz Gölü), Turkey. Sci. Res. Essays 2010, 5, 1317–1324. 46. Ozfidan-Konakci, C.; Uzilday, B.; Ozgur, R.; Yildiztugay, E.; Sekmen, A.H.; Turkan, I. Halophytes as a source of salt tolerance genes and mechanisms: A case study for the Salt Lake area, Turkey. Funct. Plant Biol. 2016, 43, 575–589. [CrossRef] 47. TVKGM. Lake Tuz Special Protection Area Management Plan 2014–2018. Available online: https://webdosya.csb.gov.tr/db/ tabiat/editordosya/tuz%20golu-4(2).pdf (accessed on 8 February 2021). 48. Köylü, M.K. Salt Lake’s Financial Investment Value and Its Contribution to Economic Growth. Int. J. Acad. Value Stud. 2017, 3, 127–137. 49. Chen, Q.; Liu, X.; Zhang, Y.; Zhao, J.; Xu, Q.; Yang, Y.; Liu, G. A nonlinear inversion of InSAR-observed coseismic surface deformation for estimating variable fault dips in the 2008 Wenchuan earthquake. Int. J. Appl. Earth Obs. Geoinf. 2019, 76, 179–192. [CrossRef] 50. Zhou, L.; Guo, J.; Hu, J.; Li, J.; Xu, Y.; Pan, Y.; Shi, M. Wuhan surface subsidence analysis in 2015–2016 based on Sentinel-1a data by SBAS-InSAR. Remote Sens. 2017, 9, 982. [CrossRef] 51. ESA. User Guides Introduction. Available online: https://earth.esa.int/web/sentinel/user-guides (accessed on 10 February 2021). 52. Hong, S.H.; Wdowinski, S. Evaluation of the quad-polarimetric Radarsat-2 observations for the wetland InSAR application. Can. J. Remote Sens. 2012, 37, 484–492. [CrossRef] 53. Aslan, G.; Cakir, Z.; Lasserre, C.; Renard, F. Investigating subsidence in the Bursa Plain, Turkey, using ascending and descending Sentinel-1 satellite data. Remote Sens. 2019, 11, 85. [CrossRef] 54. Tampuu, T.; Praks, J.; Uiboupin, R.; Kull, A. Long term interferometric temporal coherence and DInSAR phase in Northern Peatlands. Remote Sens. 2020, 12, 1566. [CrossRef] 55. Wegmüller, U.; Magnard, C.; Werner, C.; Strozzi, T.; Caduff, R.; Manconi, A. Methods to avoid being affected by non-zero closure phase in InSAR time series analysis in a multi-reference stack. Procedia Comput. Sci. 2021, 181, 511–518. [CrossRef] 56. Ormeci, C.; Ekercin, S. An assessment of water reserve changes in Salt Lake, Turkey, through multi–temporal Landsat imagery and real–time ground surveys. Hydrol. Process. Int. J. 2007, 21, 1424–1435. [CrossRef] 57. Ekercin, S.; Örmeci, C. Evaluating climate change effects on water and salt resources in Salt Lake, Turkey using multitemporal SPOT imagery. Environ. Monit. Assess. 2010, 163, 361–368. [CrossRef][PubMed] 58. Orhan, O.; Yalvaç, S.; Ekercin, S. Investigation of climate change impact on Salt Lake by statistical methods. Int. J. Environ. Geoinformatics 2017, 4, 54–62. [CrossRef] 59. Sica, Y.V.; Quintana, R.D.; Radeloff, V.C.; Gavier-Pizarro, G.I. Wetland loss due to land use change in the Lower Paraná River Delta, Argentina. Sci. Total Environ. 2016, 568, 967–978. [CrossRef][PubMed] 60. Judah, A.; Hu, B. The integration of multi-source remotely-sensed data in support of the classification of wetlands. Remote Sens. 2019, 11, 1537. [CrossRef] 61. Pande-Chhetri, R.; Abd-Elrahman, A.; Liu, T.; Morton, J.; Wilhelm, V.L. Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery. Eur. J. Remote Sens. 2017, 50, 564–576. [CrossRef]