remote sensing

Article Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study for Dongting Lake Wetlands in China

Juliane Huth 1,*, Ursula Gessner 1, Igor Klein 1 , Hervé Yesou 2, Xijun Lai 3, Natascha Oppelt 4 and Claudia Kuenzer 1,5 1 German Aerospace Center (DLR), Earth Observation Center (EOC), German Remote Sensing Data Center (DFD), 82234 Wessling, Germany; [email protected] (U.G.); [email protected] (I.K.); [email protected] (C.K.) 2 Strasbourg University, ICube-SERTIT, Parc d’Innovation, 300 Boulevard Sébastien Brant, 67412 Illkirch-Graffenstaden, France; [email protected] 3 Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China; [email protected] 4 Christian-Albrechts-University Kiel, Institute for Geography, 24098 Kiel, Germany; [email protected] 5 Chair of Remote Sensing, Institute for Geography and Geology, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany * Correspondence: [email protected]



Received: 8 April 2020; Accepted: 26 May 2020; Published: 29 May 2020


Abstract: In China, freshwater is an increasingly scarce resource and wetlands are under great pressure. This study focuses on China’s second largest freshwater lake in the middle reaches of the Yangtze River—the Dongting Lake—and its surrounding wetlands, which are declared a protected Ramsar site. The Dongting Lake area is also a research region of focus within the Sino-European Dragon Programme, aiming for the international collaboration of Earth Observation researchers. ESA’s Copernicus Programme enables comprehensive monitoring with area-wide coverage, which is especially advantageous for large wetlands that are difficult to access during floods. The first year completely covered by Sentinel-1 SAR satellite data was 2016, which is used here to focus on Dongting Lake’s wetland dynamics. The well-established, threshold-based approach and the high spatio-temporal resolution of Sentinel-1 imagery enabled the generation of monthly surface water maps and the analysis of the inundation frequency at a 10 m resolution. The maximum extent of the Dongting Lake derived from Sentinel-1 occurred in July 2016, at 2465 km2, indicating an extreme flood year. The minimum size of the lake was detected in October, at 1331 km2. Time series analysis reveals detailed inundation patterns and small-scale structures within the lake that were not known from previous studies. Sentinel-1 also proves to be capable of mapping the wetland management practices for Dongting Lake polders and dykes. For validation, the lake extent and inundation duration derived from the Sentinel-1 data were compared with excerpts from the Global WaterPack (frequently derived by the German Aerospace Center, DLR), high-resolution optical data, and in situ water level data, which showed very good agreement for the period studied. The mean monthly extent of the lake in 2016 from Sentinel-1 was 1798 km2, which is consistent with the Global WaterPack, deviating by only 4%. In summary, the presented analysis of the complete annual time series of the Sentinel-1 data provides information on the monthly behavior of water expansion, which is of interest and relevance to local authorities involved in water resource management tasks in the region, as well as to wetland conservationists concerned with the Ramsar site wetlands of Dongting Lake and to local researchers.

Keywords: Earth observation; SAR; Sentinel–1; time series; Dongting Lake; water dynamics; floodpath lake; Ramsar Convention on Wetlands

Remote Sens. 2020, 12, 1761; doi:10.3390/rs12111761 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1761 2 of 21

1. Introduction Wetlands are among the most productive and diverse ecosystems on earth, providing a large part of our fresh water [1] and supplying a variety of ecosystem services that contribute to human well-being, including the provision of fish and fiber, water supply, water treatment, climate regulation, flood control, coastal protection and recreation [2]. The mapping and monitoring of wetlands is a crucial endeavor for environmental assessments, resource management, and policy making. Synthetic Aperture Radar (SAR) satellite data are a highly suited data source for mapping surface water and wetlands due to the inherent characteristics of microwaves, such as the specular reflection on water surfaces [3]. SAR sensors are active systems that are independent of solar illumination, and microwaves penetrate most clouds, water vapor and rain, so the data acquisition is independent of climate or weather. Although a number of studies on water surface mapping based on optical data exist [4–9], SAR-based studies outnumber these. Space-borne SAR sensors suited for surface water detection and monitoring include the European ENVISAT ASAR (European Environmental Satellite, Advanced Synthetic Aperture Radar; however, only operational until 2012), the Canadian Radarsat-1/2, the Japanese ALOS Palsar (Advanced Land Observing Satellite-Phased Array type L-band Synthetic Aperture Radar), the Italian COSMO-SkyMed, the German TerraSAR-X/TanDEM-X, and the latest SAR satellites launched by the European Copernicus Program, Sentinel-1A and B. Sentinel-1 satellite data with simultaneously high spatial and high temporal resolution are of great advantage for the area-wide detection of the water surface changes of large ecosystems exposed to frequent inundation. SAR data have been employed extensively for the mapping of stable inland water bodies [10–12], the monitoring of floods [13–15], the assessment of natural inundation patterns in wetlands [16–19], and the analyses of managed wetlands such as irrigated rice fields [20–22]. Various studies have been published on the analyses of one or a few multi-temporal scenes [19,23–28], and some studies have also addressed extensive time series analyses, such as Kuenzer et al. [18,29]. In previous studies, all available SAR polarizations, modes and frequencies were used, whereas Mahdavi et al. [30] explicitly recommended the use of HH(horizontal transmit and horizontal receive) polarization and ascending mode for wetland mapping. In addition, the inter-annual and intra-annual inundation patterns of several lake environments worldwide change very fast [8]. The mapping of large floodpath lakes [31], such as Dongting Lake in China, was recently conducted with time series information based on high temporal resolution MODIS data at low spatial resolution [32–38]. Further studies were conducted additionally incorporating Envisat ASAR [39,40] and altimetry data [32]. Although previous studies on Dongting Lake led to comprehensive time series analyses on the extent of the lake, almost all of them reported limitations due to the low spatial resolution of the available data [33–36,38,40]. Wu and Liu [36] also reported on the complex spectral conditions of the lake water when only optical data were used. Wang et al. [35], Hu et al. [40], and Wang and Yesou [31] clearly point out limitations for the collection of ground truth information, which result from the dynamics of the lake extent and water levels. Since cloud cover also impedes the analysis of optical data, Xing et al. [38] and Wang and Yesou [31] propose the use of active SAR sensors. For this reason, the first analysis of the complete Sentinel-1 time series of the year 2016 is presented here to assess Dongting Lake’s monthly inundation patterns at high spatial and temporal resolution.

2. Data and Methodology

2.1. Study Area—Dongting Lake in China The Dongting Lake area—rich in water resources and biodiversity but also considerably transformed by agricultural activities, tree plantations, and urban settlement development—is one of the most important ecosystems in China (Figure1). The area that extends from 28 ◦30’–29◦50’ North and 112◦00’–113◦20’ East is located in the center of the Yangtze River watershed and north of Hunan Province (Figure2). Dongting Lake—the second largest floodpath lake in China—plays, jointly with Remote Sens. 2020, 12, 1761 3 of 21

Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 22 Poyang Lake, a crucial role in the accommodation of Yangtze flood waters, with a maximum water depthjointly of up with to 35 Poyang m and average Lake, a depths crucial of role 6–7 in m the [41 ]. accommodation Before the 1990s, of Yangtze intensive flood land watersreclamation, with to a gainmaximum new agricultural water depth production of up to ground, 35 meters and and strong average sedimentation, depths of 6– following7 meters [41] deforestation. Before the 1990s, along the upperintensive reaches land ofreclamation the Yangtze, to gain resulted new agricultural in a rapid decrease production in theground, size ofand the strong lake. sedimentation, From 1820 to 1990,following half of the deforestation lake area disappeared along the upper [42]. reaches After one of ofthe the Yangtze, most severe resulted floods in a rapid in 1998, decrease much ofin the landsize reclaimed of the had lake. been From inundated 1820 to 1990 again, ha [lf33 of]. Inthe this lake study, area disappeared the dyke line [42] of. Dongting After one Lake’s of the main most dikedsevere area publishedfloods in 1998 by Yang, much et al.of the [43 ]land is used reclaimed as the extenthad been for allinundated lake analyses again (Figure[33]. In 2this—large study, body the of waterdyke marked line of Dongting in blue). Lake’s The diked main area diked comprises area published 3281 km by2 Yangand includeset al. [43]several is used poldersas the extent such for as Gongshuangshaall lake analyses in South (Figure Dongting 2—large Lake. body of water marked in blue). The diked area comprises 3281 km2 Theand includes polders withinseveral mainpolders dykes such areas Gongshuangsha included in this in study South because Dongting they Lake. are potentially used as artificial wetlands,The poldera wetlands within type main included dykes are in the included wetland in classification this study because scheme they of the are Ramsar potentially Convention, used as whichartificial provides wetlands, a global a wetland framework type for included intergovernmental in the wetland cooperation classification on scheme wetland of issues the Ramsar [1,44]. DongtingConvention Lake, includes which provides three Ramsar a global wetland framework sites for of international intergovernmental importance—No. cooperation on 551—East wetland issues [1,44]. Dongting Lake includes three Ramsar wetland sites of international importance—No. Dongting Lake Wetland Nature Reserve declared in 1992; and No. 1151—South Dongting Wetland 551—East Dongting Lake Wetland Nature Reserve declared in 1992; and No. 1151—South Dongting and Waterfowl Nature Reserve, and No. 1154—West Dongting Lake Nature Reserve, both declared in Wetland and Waterfowl Nature Reserve, and No. 1154—West Dongting Lake Nature Reserve, both 2002—all including natural wetlands that are important for millions of migratory birds and home to declared in 2002—all including natural wetlands that are important for millions of migratory birds endemic and rare species of birds, fish, mammals, and reptiles [45–49]. and home to endemic and rare species of birds, fish, mammals, and reptiles [45–49].

(a) (b)

(c) (d)

(e) (f)

FigureFigure 1. Dongting 1. Dongting Lake fieldLake visit field impressions—high visit impressions— dykeshigh dykes (a); wetland (a); wetland vegetation, vegetation, reeds, and reeds, poplar and trees,poplar partly trees, managed partly area managed (b); large area shipping (b); large tra shippingffic near traffic Yueyang near ( cYueyang); poplar ( plantationc); poplar plantation on elevated on groundelevated due to dropped ground due leaves to (e dropped); mixed wetland leaves ( vegetation,e); mixed wetlandpartly flooded vegetation, area ( d,f partly)—pictures flooded/source: area J. Huth;(d,f C.)— Kuenzer.pictures/source: J. Huth; C. Kuenzer.

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FigureFigure 2. The 2. The Dongting Dongting Lake Lake regionregion (large map) map) with with the the location location of three of three Ramsar Ramsar Wetland Wetland sites, sites, GlobalGlobal Urban Urban Footprint Footprint data data [50] [50] representing represent informationing information on settlements, on settlements and, and the locationthe location of Dongting of LakeDongting in the middle Lake in of the the middl Yangtzee of the River Yangtze catchment River catchment (small map). (small map).

2.2. Sentinel-1 Time Series Data This study uses SAR data from Sentinel-1A, launched on April 3, 2014, to assess the flood situation. The mission provides frequent high spatial resolution SAR data according to the acquisition scheme every 5–10 days [51]. The used standard product is in Ground Range Detected (GRD) format and contains amplitude information in dual-polarized default operation mode VH-VV (vertically transmitted and horizontally received (VH) and vertically transmitted and received (VV)). The GRD data are delivered at 10 m pixel spacing, after applying a 4 by 1 multi-looking processing [52]. The lake is largely covered by one scene and fully covered by two scenes (approx. 170 km by 280 km) from one ascending satellite orbit. The year 2016 marked the first complete year of coverage over the Dongting Lake region, including 18 acquisition dates unevenly distributed over the year (summarized in Table1). Remote Sens. 2020, 12, 1761 5 of 21

Table 1. Sentinel-1 imagery study details; 36 scenes were available over 18 acquisition dates.

Month of the Year Sentinel 1 Data Set Identifier Date of Acquisition Coverages Per Month January S1A_IW_GRDH_1SDV_20160117T103452_20160117T103517_009533_00DD94_C366 2016/1/17 1 S1A_IW_GRDH_1SDV_20160117T103517_20160117T103551_009533_00DD94_08EC February S1A_IW_GRDH_1SDV_20160210T103452_20160210T103517_009883_00E7C2_3BFB 2016/2/10 1 S1A_IW_GRDH_1SDV_20160210T103517_20160210T103542_009883_00E7C2_A023 March S1A_IW_GRDH_1SDV_20160305T103452_20160305T103517_010233_00F1DB_1F27 2016/3/5 2 S1A_IW_GRDH_1SDV_20160305T103517_20160305T103550_010233_00F1DB_104C S1A_IW_GRDH_1SDV_20160329T103453_20160329T103518_010583_00FBDA_0546 2016/3/29 S1A_IW_GRDH_1SDV_20160329T103518_20160329T103543_010583_00FBDA_4354 April S1A_IW_GRDH_1SDV_20160422T103453_20160422T103518_010933_010659_3621 2016/4/22 1 S1A_IW_GRDH_1SDV_20160422T103518_20160422T103543_010933_010659_CD91 May S1A_IW_GRDH_1SDV_20160516T103457_20160516T103522_011283_011177_3CB5 2016/5/16 1 S1A_IW_GRDH_1SDV_20160516T103522_20160516T103547_011283_011177_4CEE June S1A_IW_GRDH_1SDV_20160609T103459_20160609T103524_011633_011CB0_5A05 2016/6/9 1 S1A_IW_GRDH_1SDV_20160609T103524_20160609T103549_011633_011CB0_EC47 July S1A_IW_GRDH_1SDV_20160703T103500_20160703T103525_011983_0127C5_F17D 2016/7/3 1 S1A_IW_GRDH_1SDV_20160703T103525_20160703T103550_011983_0127C5_F841 August S1A_IW_GRDH_1SDV_20160820T103503_20160820T103528_012683_013ED7_7852 2016/8/20 1 S1A_IW_GRDH_1SDV_20160820T103528_20160820T103553_012683_013ED7_B6C8 September S1A_IW_GRDH_1SDV_20160925T103504_20160925T103529_013208_015034_6396 2016/9/25 1 S1A_IW_GRDH_1SDV_20160925T103529_20160925T103554_013208_015034_7BD5 October S1A_IW_GRDH_1SDV_20161007T103504_20161007T103529_013383_0155AE_9A8A 2016/10/7 3 S1A_IW_GRDH_1SDV_20161007T103529_20161007T103554_013383_0155AE_F802 S1A_IW_GRDH_1SDV_20161019T103504_20161019T103529_013558_015B44_26C2 2016/10/19 S1A_IW_GRDH_1SDV_20161019T103529_20161019T103554_013558_015B44_648B S1A_IW_GRDH_1SDV_20161031T103504_20161031T103529_013733_01609E_A40B 2016/10/31 S1A_IW_GRDH_1SDV_20161031T103529_20161031T103554_013733_01609E_93DC Remote Sens. 2020, 12, 1761 6 of 21

Table 1. Cont.

Month of the Year Sentinel 1 Data Set Identifier Date of Acquisition Coverages Per Month November S1A_IW_GRDH_1SDV_20161112T103504_20161112T103529_013908_01662D_487A 2016/11/12 2 S1A_IW_GRDH_1SDV_20161112T103529_20161112T103554_013908_01662D_1128 S1A_IW_GRDH_1SDV_20161124T103504_20161124T103529_014083_016B78_A299 2016/11/24 S1A_IW_GRDH_1SDV_20161124T103529_20161124T103554_014083_016B78_B103 December S1A_IW_GRDH_1SDV_20161206T103503_20161206T103528_014258_0170F8_7A65 2016/12/6 3 S1A_IW_GRDH_1SDV_20161206T103528_20161206T103553_014258_0170F8_705A S1A_IW_GRDH_1SDV_20161218T103503_20161218T103528_014433_01767A_3107 2016/12/18 S1A_IW_GRDH_1SDV_20161218T103528_20161218T103553_014433_01767A_0F07 S1A_IW_GRDH_1SDV_20161230T103503_20161230T103528_014608_017BF2_2FE1 2016/12/30 S1A_IW_GRDH_1SDV_20161230T103528_20161230T103553_014608_017BF2_F74E Remote Sens. 2020, 12, 1761 7 of 21

2.3. SAR Image Processing Graph processing within ESA’s open source software SNAP (SeNtinel’s Application Platform) was utilized for pre-processing the Sentinel-1 GRD data. Within the graph, the VH amplitude band data were selected as mentioned in Ottinger et al. [53], as well as the implementation of geolocation and terrain corrections with the SRTM 1 sec data, and backscatter calibration to result in georeferenced radiometrically corrected datasets by creating comparable backscatter values.

2.4. Surface Water Extraction The applied algorithm takes advantage of the fact that water surfaces usually act as specular reflectors for incoming microwave radiation. Therefore, the microwaves that are incident on the water surface are reflected away from the SAR sensor, and the return pulse can be very weak; the backscatter from water surfaces is zero or very low. Hence, smooth, undisturbed water surfaces appear as black surfaces in radar data [54–56]. Exceptions are very rough water surfaces, which might lead to a return pulse, as wellRemote as Sens. water 2020, 12 surfaces,, x FOR PEER REVIEW which are mixed with vegetation (e.g., reed grasses on8 of 22 lakes). The principlebackscatter utilizedfrom water is thesurfaces diff erentiation is zero or very of low. water Hence, from smooth, land undisturbed surfaces based water on surfaces two empirically tested andappear chosen as thresholds black surfaces applied in radar to data radiometrically [54–56]. Exceptions corrected are very andrough median-filtered water surfaces, which SAR amplitude data (P1), andmight on lead subsequent to a return pulse, morphological as well as water operations. surfaces, which The sameare mixed thresholds with vegetation are applied (e.g., reed to all data sets grasses on lakes). in the performed calculations. A first, higher threshold of 20 db defines all pixels clearly identifiable The principle utilized is the differentiation of water− from land surfaces based on two as land (P3),empirically while tested a second, and chosen lower thresholds threshold applied (P2) to radiometrically of 30 db defines corrected pixels and medi clearlyan-filtered identifiable as − water. TheSAR pixel amplitude values data falling (P1), inand between on subsequent the two morphological thresholds operations. are classified The same in thresholds a second are step based on applied to all data sets in the performed calculations. A first, higher threshold of −20 db defines all morphologicalpixels clearly operators, identifiable which as land perform (P3), while dilation a second, and lower closing threshold operations (P2) of −30 atdb thedefines land–water pixels border, which resultclearly inP6, identifiable followed as water. by a The cleaning pixel values operation falling in for between very smallthe twoartifacts thresholds (see are classified Figure3 in). a The outputs of the calculationsecond step are based binary on morphological water/no operators, water products which perform called dilation watermasks. and closing operations Detailed at information the on the methodologicalland–water border steps, which of the result applied in P6, procedurefollowed by a can cleaning be found operation in [for18 very,57] small and artifacts on method (see validation Figure 3). The outputs of the calculation are binary water/no water products called watermasks. in [58], whichDetailed resulted information in overall on the methodological accuracy values steps betweenof the applied 85% procedure and 96%. can be found in [18,57] and on method validation in [58], which resulted in overall accuracy values between 85% and 96%. 2.5. Inundation Analyses of the Sentinel-1 Lake Water Extent for 2016 2.5. Inundation Analyses of the Sentinel-1 Lake Water Extent for 2016 Based on the 36 temporally unevenly distributed watermasks in 2016, the maximum water extent Based on the 36 temporally unevenly distributed watermasks in 2016, the maximum water in each monthextent of in 2016 each was month calculated. of 2016 was The calculated. maximum The maximumextent was extent chosen was to chosen prevent to prevent the underestimation the of the waterunderestimation surface extents of the and water water surface surface extents dynamics and water (compared surface dynamics to mean (compared values). to mean In additionvalues). to these monthly watermasks, an inundation frequency map was created, summarizing for In addition to these monthly watermasks, an inundation frequency map was created, each pixelsummarizing how often itfor is each covered pixel how by water often it in is thecovered 12 monthly by water in water the 12 extent monthly maps. water Moreover,extent maps. the monthly maps of maximumMoreover, the water monthly extent maps were of maximum used to water produce extent a were map used of tothe produce beginning a map of of the the inundation, representingbeginning the month of the ofinundation, the year representing in which the the inundationmonth of the startedyear in which at each the locationinundation within started theat study area. The workfloweach location of all the within conducted the study processingarea. The workflow steps of is all presented the conducted inFigure processing3. steps is presented in Figure 3.

FigureFigure 3. Workflow 3. Workflow of theof the conducted conducted processing processing steps steps (adapted (adapted from [18] from). [18]).

3. Results

3.1. Duration and Frequency of Inundation from Sentinel-1 Time Series Figure 4 shows the time series of Dongting Lake's monthly maximum water extent in 2016. Normally, the growth of the water surface towards the annual flooding period is reported from May to August [59]. In accordance with Yuan et al. [60], who described the year 2016 as an exceptional

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3. Results

3.1. Duration and Frequency of Inundation from Sentinel-1 Time Series Figure4 shows the time series of Dongting Lake’s monthly maximum water extent in 2016. Normally, the growth of the water surface towards the annual flooding period is reported from May to August [59]. In accordance with Yuan et al. [60], who described the year 2016 as an exceptional flood year, it can be visually observed on the monthly maps that the 2016 flooding period had already begun inRemote April. Sens. Thus, 2020, 12, the x FOR maximum PEER REVIEW water surface was reached in July, and the minimum,9 of 22 in October. The area offlood the year East, itDongting can be visually Nature observed Reserve on the (Ramsar monthly nomenclature, maps that the 2016 see Figure flooding2) period is the had most dynamic part of thealready lake. begun The in southern April. Thus, part the ofmaximum the lake water also surface faced was water reached surface in July, extent and the changesminimum, overin the year, whereas,October. the western The area part of the of East Dongting Dongting Lake Nature was Reserve comparably (Ramsar nomenclature, stable. Figure see 5Figure shows 2) is the the increase in water extentmost dynamic per month part of for the the lake. complete The southern lake part area. of the lake Within also fac theed totalwater areasurface of extent the dyke,changes continuous over the year, whereas, the western part of Dongting Lake was comparably stable. Figure 5 shows swellingthe of increase the water in water coverage extent per from month 43% for in the January complete up lake to area. a first Within maximum the total area of of 69% the indyke, May and to a second maximumcontinuous swelling of 75% of in the July water can coverage be observed. from 43% From in January August up to onwards, a first maximum the water of 69% area in decreased within 3 monthsMay and toto aa minimumsecond maximum of 41% of total75% in coverage July can be in observed. October, From while August it increased onwards, slightly the water in November and againarea by decr 10%eased by within December. 3 months to a minimum of 41% total coverage in October, while it increased slightly in November and again by 10% by December.

Figure 4.FigureDongting 4. Dongting Lake Lake water water surface surface extentextent (blue) (blue) of offully fully diked diked area— area—thethe 12 sub-figures 12 sub-figures show the show the monthlymonthly maximum maximum maps maps from from January January to to December,December, 2016 2016..

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80% 70% 60% 50% 40%

tage tage of diked area 30%

percen 20% 10% 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 5. Dongting Lake monthly maximum water surface extent in 2016 as a percentage of the fully diked area. Figure 5. Dongting Lake monthly maximum water surface extent in 2016 as a percentage of the fully diked area. Figure6 displays the inundation frequency map. The dark blue areas, the so-called permanent water body, are definedFigure as 6 the displays water the surface inundation where frequency the water map. was presentThe dark in blue all observations—thisareas, the so-called permanent involves the mainwater river body, branches are defined that as flow the inwater the surface lake and where the the stable water parts was ofpresent the lake. in all Inobservations 2016, —this this involved 28%involves of the the diked main area river of branches Dongting that Lake, flow i.e., in the about lake 920 and km the2. stable Areas part of greens of the tone lake. are In 2016, this 2 covered with waterinvolve ford 1–11 28% months. of the diked Proportions area of of Dongting 9% or 8% Lake, of the i.e. area, about were 920 water-covered km . Areas of in 1green or tone are covered with water for 1–11 months. Proportions of 9% or 8% of the area were water-covered in 1 or 2 months in 2016, respectively, whereas 3% to 6% of the total area was water-covered for 3 to 9 months. 2 months in 2016, respectively, whereas 3% to 6% of the total area was water-covered for 3 to 9 A share of 18% of the diked area was not covered by water during the year 2016 (grey colored area). months. A share of 18% of the diked area was not covered by water during the year 2016 (grey The zoomed in maps in Figure6a–d reveal the potential of the 10 m spatial resolution of the colored area). Sentinel-1 data. Wetland structures can be assessed in high detail—e.g., drainage areas, other man-made The zoomed in maps in Figure 6a–d reveal the potential of the 10 meter spatial resolution of the structures, and areasSentinel potentially-1 data. Wetland used for structures agriculture can (e.g., be reedassessed and grassland,in high detail Figure—e.g.6a)., drainage Due to its areas, other shape, agriculturalman land-made can structures, be visually and identified areas potentially and also used distinguished for agriculture from (e.g. other, reed rectangular and grassland, areas Figure 6a). demarcated as potentialDue to its aquaculture shape, agricultural ponds or land other can artificial be visually water reservoirsidentified and(Figure also6c). distinguished In addition, from other the details in Figurerectangular6a,d show areas linear demarcated structures as pwithinotential the aquaculture lake area, whichponds areor other dykes artific or channels.ial water reservoirs In Figure6b, permanently(Figure 6c). water-coveredIn addition, theareas details within in Figure South 6a, Dongtingd show linear Lake structures Nature Reservewithin the can lake be area, which distinguished fromare dykes temporally or channels. flooded, In Figure unstructured, 6b, permanent approximately water- naturalcovered wetlandsareas within with South natural Dongting Lake channels. The areaNature of the Reserve non-permanent can be distinguished waterbodies from of thetemporally Ramsar wetlandflooded, siteunstructured, (1151), as shownapproximate in natural Figure6b, was coveredwetlands with with water natural for morechannels than. The half area of 2016. of the This non is-permanent comparable waterbodies to the areas of nearthe Ramsar and wetland within East Dongtingsite (1151), Lake asWetland shownNature in Figure Reserve 6b, was (551), covered as displayed with wate in Figurer for more6d. than half of 2016. This is comparable to the areas near and within East Dongting Lake Wetland Nature Reserve (551), as 3.2. Start of Flooddisplayed and Inundation in Figure in Dongting6d. Wetlands Figure7 displays the start of inundation. Permanent bodies of water are shown in black. Areas that 3.2. Start of Flood and Inundation in Dongting Wetlands were dry all year are shown in light grey. The other colors represent the months of the beginning of inundation from JanuaryFigure to 7 December.displays the start of inundation. Permanent bodies of water are shown in black. Areas The light blue-coloredthat were dry areas all in year the northern are shown part in of light the lake grey. were The flooded other in colors January represent 2016. They the months are of the fed by the river junctionbeginning that of flowsinundation from thefrom main January river to stem December. of the Yangtze to the northeast of Dongting Lake. The green andThe yellow light blue areas-colored were floodedareas in inthe February northern part and of March. the lake The were Li Riverflooded feeds in January water 2016. They into West Dongtingare fed Lake by (Figurethe river7a), junction which that also flows caused from the the inundation main river of stem embankments of the Yangtze in February to the northeast of Dongting Lake. The green and yellow areas were flooded in February and March. The Li River feeds (see the green areas in Figure7c). Large parts of the area first flooded in April (orange, e.g., Figure7b) water into West Dongting Lake (Figure 7a), which also caused the inundation of embankments in are embanked or elevated, e.g., leaves and other natural material naturally elevate the ground (Figure1). February (see the green areas in Figure 7c). Large parts of the area first flooded in April (orange, e.g., Due to the chessboard structure (also confirmed by local knowledge), it can be seen that the orange Figure 7b) are embanked or elevated, e.g., leaves and other natural material naturally elevate the areas in Figure7c are used for agriculture. From literature, it is known that rice is cultivated in the ground (Figure 1). Due to the chessboard structure (also confirmed by local knowledge), it can be Dongting Lakeseen region that [61 the]. orange Rice requires areas in extensiveFigure 7c are field used flooding for agriculture. before it From is planted. literature In, addition,it is known that rice the rice plant needsis cultivated temperatures in the between Dongting 17 and Lake 33 region◦C to grow[61]. [Rice61]. requiresConsidering extensive the average field flooding monthly before it is temperatures inplanted. the region In [62 addition,], April is the the rice first plant month needs of the temperatures year with su ffi betweencient temperatures 17 and 33 (17°C ◦ toC) grow [61]. to start rice cultivation.Considering In Figure the average7d, rectangular monthly temperatur structureses are in the mixed region with [62] unstructured, April is the first areas month that of the year were flooded in February, whereas some rectangular fields were inundated—maybe by activated sluice gates—in January, April, May, and June. In Figure7b, the structure indicates a natural wetland where Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 22

Remotewith Sens.sufficient2020, 12 temperatures, 1761 (17 °C) to start rice cultivation. In Figure 7d, rectangular structures10 are of 21 mixed with unstructured areas that were flooded in February, whereas some rectangular fields were inundated—maybe by activated sluice gates—in January, April, May, and June. In Figure 7b, the thestructure water seems indicates to have a natural spread wetland uncontrollably—from where the water March seems to to July—possibly have spread onlyuncontrollably interrupted—from by the altitudeMarch dito ff Julyerences—possibly of the riveronly interruptedislands. This by is the where altitude the Ramsar differences wetland of the site riverSouth islands. Dongting This Lake is Wetlandwhere the and Ramsar Water Fowl wetland Nature site Reserve South Dongtingis located. Lake Wetland and Water Fowl Nature Reserve is located.

FigureFigure 6. 6.Inundation Inundation frequencyfrequency mapmap forfor 2016 at Dongting Lake Lake displaying displaying the the duration duration of of inundation inundation inin months months (top) (top) with with detail detail mapsmaps ((aa––dd).).

Remote Sens. 2020, 12, 1761 11 of 21 Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 22

Figure 7. Month of the start of inundation at Dongting Lake in 2016 (top) with detail maps (a–d). Figure 7. Month of the start of inundation at Dongting Lake in 2016 (top) with detail maps (a–d). For further quantification, five zones were identified in the study area according to Yang et al. [43] For further quantification, five zones were identified in the study area according to Yang et al. and visual interpretation (Table2). It was determined which share of a designated zone was flooded [43] and visual interpretation (Table 2). It was determined which share of a designated zone was first in which month (Figure8). flooded first in which month (Figure 8).

Table 2. List of zones of different use within Dongting Lake.

Zone Name Description of Use Source

Remote Sens. 2020, 12, 1761 12 of 21

Table 2. List of zones of different use within Dongting Lake.

Zone Name Description of Use Source 1 Chengxi agricultural use adapted from [43]

Remote2 Sens. 2020, 1 Gongshuangsha2, x FOR PEER REVIEW mixed use adapted from [43] 13 of 22 3 Zhongzhou mixed use adapted from [43] 41 Chengxi East Dongting Lakeagricultural natural zone use digitized,adapted from own [43] definition 5 South Dongting Lake natural zone digitized, own definition 2 Gongshuangsha mixed use adapted from [43] 3 Zhongzhou mixed use adapted from [43] In January,4 February,East Dongting and March,Lake the inundatednatural zone area of Zonedigitized, 1 increased own bydef aboutinition 7% to 13% each month.5 In April,South more Dongting than Lake one third ofnatural the zone zone was additionallydigitized, flooded. own definition The floods of the remaining monthsIn January, increased February by, and less March, than 2%the inundated per month area and of wereZone negligible.1 increased by This about may 7% mean to 13% that most of the flooding,each month. most In April, likely more irrigation, than one occurred third of the in zone April. was A additionally similar dispersion flooded. The behavior floods of of the the water remaining months increased by less than 2% per month and were negligible. This may mean that can be observed in Zone 2, with the largest floods taking place in February (13%) and April (17%). most of the flooding, most likely irrigation, occurred in April. A similar dispersion behavior of the By contrast,water can in Zonebe observed 3, where in Zone the 2, newly with the flooded largest floods area increasedtaking place by in February 15% and (1 22%3%) and in JanuaryApril and February,(17%). respectively, By contrast, thein Zone increase 3, where was the only newl betweeny flooded 6%area and increased 7% in by March 15% and and 22% April, in January before rising again byand 11% February, in May. respectively, In Zone the 4, aincrease quarter was of only the between area was 6% flooded and 7% in March January and and April, another before fifth in February.rising The again flood by 11% pattern in May. of ZoneIn Zone 4 is4, a more quarter di ffiof cultthe area to describewas flooded and in isJanuary most and similar another to thatfifth of Zone 2, within February February. beingThe flood the pattern month of with Zone the 4 is largest more difficult increase. to describe Zone 4and and is most Zone similar 5 show to that an additionalof Zone 2, with February being the month with the largest increase. Zone 4 and Zone 5 show an small peakadditional of newly small flooded peak of newly area inflooded July, whicharea in July, can which be related can be to relat theed Yangtze to the Yangtze River River flood flood in July 2016 (personalin July observation 2016 (personal by the observation author onby the site). author on site).

FigureFigure 8. Percentage 8. Percentage increase increase of of inundation inundation per month month for for five five embankment embankment zones zonesaccording according to [43]. to [43]. The increaseThe increase is shown is shown as a as percentage a percentage of of the the areaarea of the the respective respective zone. zone.

DifferentDifferent patterns patterns can can be be a measurea measureof of floodflood intensit intensityy as asan anexpression expression of water of watermanagement management practices;practices for; example, for example, managed managed and and natural natural wetlands wetlands need need to be to analyzed be analyzed in a further in a further study, study, supported by local information that unfortunately was not available here. supported by local information that unfortunately was not available here. 4. Validation 4. Validation In order to confirm the validity of the lake water extents derived from the Sentinel-1 time series, Inthe order DLR to Global confirm WaterPack the validity time of series the lake product water [8,63] extents—hereinafter derived called from simply the Sentinel-1 Global time series,WaterPack the DLR— Globalhigh spatial WaterPack resolution time data, water series level product data from [8, 63the]—hereinafter Chenglingji Hydrological called simply station Global

Remote Sens. 2020, 12, 1761 13 of 21

WaterPack—highRemote Sens. 2020, 1 spatial2, x FOR PEER resolution REVIEW data, water level data from the Chenglingji Hydrological14 of station 22 at Dongting Lake, and detailed in situ knowledge from field campaigns were used. The authors have a detailedat Dongting knowledge Lake, and of detailed the region, in situ asknowledge they have from been field cooperating campaigns were inthe used. Sino-European The authors have Dragon a detailed knowledge of the region, as they have been cooperating in the Sino-European Dragon Programme for more than 10 years. The first author has repeatedly been to the study area for field Programme for more than 10 years. The first author has repeatedly been to the study area for field observations (in 2010, 2012, and 2016) and discussions with scientists, conservationists (e.g., at the observations (in 2010, 2012, and 2016) and discussions with scientists, conservationists (e.g., at the DongtingDongting Lake Lake Station Station for for Wetland Wetland Ecosystem Ecosystem Research), Research), and local hydrological hydrological managers managers (e.g. (e.g.,, at at the Yueyangthe Yueyang Hydrological Hydrological Station). Station).

4.1.4.1. Relationship Relationship between between the the Sentinel-1 Sentinel-1 Lake Water Water Extent Extent and and Global Global WaterPack WaterPack TheThe Global Global WaterPack WaterPack is is a a datasetdataset with with 250 250 m meter spatial spatial and and dailydaily temporal resolution resolutions,s, based based on theon entire the entire MODIS MODIS time time series series archive archive since since 2000, 2000, whichwhich is is utilized utilized to togenerate generate information information on the on the surfacesurface water water coverage coverage of of lakes, lakes, reservoirs, reservoirs, and and wetlands wetlands and and their their dynamics dynamics worldwide worldwide [8,63]. [8,63]. Technically,Technically, the the dynamic dynamic thresholds thresholds forfor daily water water classification classification are are calculated calculated for each for eachMODIS MODIS tile tile basedbased on selectedon selected training training information information from stable stable waterbody waterbody information information.. Subsequently, Subsequently, the daily the daily watermask time series is interpolated for cloud and no data gaps. The approach proved to be able to watermask time series is interpolated for cloud and no data gaps. The approach proved to be able to capture the seasonal water coverage of lakes and reservoirs as well as flooding events [64]. capture theA direct seasonal comparison water coverageof the Sentinel of lakes-1-derived and reservoirs dataset with as well the Globalas flooding WaterPack events for [64 2016]. is shownA direct in Figure comparison 9. The ofcourse the Sentinel-1-derivedof the inundation at datasetDongting with Lake the, derived Global from WaterPack the Sentinel for 2016-1 time is shown in Figureseries9 of. Thethe lake course water of theextent, inundation can be described at Dongting as multimodal Lake, derived (Figure from 9). The the maximum Sentinel-1 occurs time seriesin of the lakeJuly 2016 water with extent, a water can extent be described of 2465 km as2 multimodal. The secondary (Figure maximum9). The has maximum a value of occurs2275 km in2 Julyand 2016 withoccurs a water in May extent 2016. of 2465A third km but2. The much secondary lower maximum maximum can hasbe found a value in ofFebruary 2275 km at2 1714and km occurs2. The in May 2016.smallest A third lake but water much extent lower occurs maximum in October can 2016 be, at found 1331 km in2 February. at 1714 km2. The smallest lake water extent occurs in October 2016, at 1331 km2.

3000 Sentinel-1 2500 Global Water Pack 2000

1500

Lake water water [km²]Lakeextent 1000

500 1 2 3 4 5 6 7 8 9 10 11 12 Months of the year

FigureFigure 9. 9.Dongting Dongting LakeLake water extent extentss from from Sentinel Sentinel-1-1 and and Global Global Water Water Pack.Pack.

In comparisonIn comparison with with the the GlobalGlobal WaterPack time time series, series, there there is good is good agreement agreement and a and similar a similar behaviorbehavior during during the the year. year. The Themean mean monthly lake lake water water extent extent for for2016 2016 from from Sentinel Sentinel-1-1 is 1798 is km 17982, km2, andand it is it 1723 is 1723 km km2 from2 from the the Global Global WaterWater Pack. As canAs can be seen be seen in Figure in Figure9, in 9 January,, in January, June, June, and and September, September the, the Sentinel–1 Sentinel– water1 water extent extent is smalleris thansmaller that derived than that from derived the data from record the data of therecord Global of the Water Global Pack. Water The Pack. diff erenceThe difference values arevalues130 are km 2 in 2 2 2 − January,−130 km126 in km January,2 in June, −126 and km in381 June, km 2 andin September−381 km in 2016. September The best 2016. agreement The best agreement is shown in is April 2 2 shown− in April and August, with− differences2 of +20 km and2 +28 km . The largest deviations occur in and August, with differences of +20 km and 2+28 km . The2 largest deviations occur in May and May and December, with differences of2 +423 km and +4052 km . The largest positive deviation (+423 2 December,km2) of the with two di datasetsfferences inof relation+423 kmto theand mean+405 water km extent. The in largest 2016 of positive1798 km2 deviation(from Sentinel (+423-1) is km ) of 2 the two+23.5% datasets, whereas in the relation largest to negative the mean deviation water extent(−381 km in2) 2016 from of the 1798 mean km is −(from21.2%. Sentinel-1) is +23.5%, 2 whereasThe the largest differencenegative deviation between the ( 381Sentinel km -)1 from lake the water mean extent is and21.2%. the Global WaterPack − − occursThe largest between di thefference two maximum between values the Sentinel-1 of May 2016 lake (Sentinel water extent-1: 2275 and km2; the Global Global WaterPack: WaterPack 1852 occurs between the two maximum values of May 2016 (Sentinel-1: 2275 km2; Global WaterPack: 1852 km2).

Figure 10 shows exemplary locations where Sentinel-1 is able to detect water areas that were not Remote Sens. 2020, 12, 1761 14 of 21 Remote Sens. 2020, 12, x FOR PEER REVIEW 15 of 22 detectedkm2). withinFigure 10 the shows Global exemplary WaterPack—these locations where areas Sentinel are marked-1 is able in to green. detect Figurewater areas 10a that shows were mainly small-scalenot detected structured within water the Global areas, WaterPack which can—these only areas be found are marked in the in Sentinel-1 green. Figure lake 10a water shows extent. mainly small-scale structured water areas, which can only be found in the Sentinel-1 lake water In addition,Remote Sens. the 20 western20, 12, x FOR partPEER REVIEW of Zone 2 (Figure8) is shown in Figure 10b, in which15 narrow of 22 river channelsextent. are In exclusively addition, the detected western bypart Sentinel-1. of Zone 2 The(Figure figure 8) is also shown shows in Figure that deviations 10b, in which only narrow occur in the borderriverkm zones channels2). Figure between 10 are shows exclusivelywater exemplary and land. detected locations Given by where Sentinel the fact Sentinel- that1. The- the1 is figure Globalable to also detect WaterPack shows water that areas has deviations athat 250 were m resolution, only occur in the border zones between water and land. Given the fact that the Global WaterPack has a whereasnot our detected study within presented the Global here WaterPack has a 10— mthese spatial areas resolution, are marked and in green. since Fig ourure results 10a shows are validated 250mainly meter smallresolution,-scale structuredwhereas our water study areas, presented which can here only has be a found10 meter in thespatial Sentinel resolution-1 lake , waterand since withour inextent. situresults and In are addition, higher validated spatialthe westernwith resolution in partsitu ofand Zone opticalhigher 2 (Figure spatial data, 8) it resolutionisis shown postulated in optical Figure that data10b, the ,in it which timeis postulated seriesnarrow presented that herethe isriverthe time most channelsseries accurate present are exclusivelyed flood here dynamics is detectedthe most byanalysis accurate Sentinel publishedflood-1. The dynamics figure to date.also analysis shows published that deviations to date. only occur in the border zones between water and land. Given the fact that the Global WaterPack has a 250 meter resolution, whereas our study presented here has a 10 meter spatial resolution, and since our results are validated with in situ and higher spatial resolution optical data, it is postulated that the time series presented here is the most accurate flood dynamics analysis published to date.

FigureFigure 10. Di 10.ff erenceDifference map map of Dongting of Dongting Lake waterLake water extent extent from Sentinel-1 from Sentinel (nearest-1 (nearest neighbor neighbor resampling to 250resampling m MODIS to spatial 250 m MODIS resolution) spatial and resolution) Global WaterPack and Global forWaterPack May 2016 for (left). May 2016 Figure (left) 10. a,bFigure show. 10a details and 10b show details in polders. in polders.Figure 10. Difference map of Dongting Lake water extent from Sentinel-1 (nearest neighbor resampling to 250 m MODIS spatial resolution) and Global WaterPack for May 2016 (left). Figure. 10a 4.2. Relationship between Sentinel-1 Lake Water Extent and Water Level Measurements 4.2. Relationshipand 10b between show details Sentinel-1 in polders. Lake Water Extent and Water Level Measurements WaterWater level level measurements measurements from from Chenglingji,Chenglingji, the the main main hydrological hydrological station station of Dongting of Dongting Lake, Lake, were4.2. used. Relationship The station between is Sentinellocated- 1near Lake the Water outflow/inflow Extent and Water to/from Level Measurementsthe main stem of the Yangtze River were used. The station is located near the outflow/inflow to/from the main stem of the Yangtze River near YueyangWater level City measurements in the northeast from of Chenglingji the lake. , Wathe termain level hydrological data published station ofby Dongting the Yangtze Lake, River near Yueyang City in the northeast of the lake. Water level data published by the Yangtze River Commissionwere used. The (Chinese station name: is located Changjiang near the outflow/inflow Water Resource to/from Commission the main stem (CWRC)) of the Yangtze are used River in this Commissionstudynear toYueyang (Chinese compare City name:them in the withChangjiangnortheast information of the Water lake. generated Resource Water level fromCommission data remote published (CWRC)) sensing by the data. Yangtze are used Water River in level this study to comparemeasurementsCommission them with are (Chinese available information name: from Changjiang generated June 1 to Water fromAugust Resource remote 1, 2016. Commission sensing In Figure data. (CWRC))11, Water these are levelalmost used measurements daily in this water are availablelevelstudy data from to are June compare displayed 1 to Augustthem for withthe 1, main 2016. information flood In Figure months generated 11 at, these Dongting from almost remote Lake daily sensing(see water4.1.) data. and level compared Water data level are to displayed the measurements are available from June 1 to August 1, 2016. In Figure 11, these almost daily water for thelake main water flood extent monthss based aton Dongting the satellite Lake observations (see 4.1.) of and June compared 9, July 3, and to theAugust lake 20. water Even extents though based level data are displayed for the main flood months at Dongting Lake (see 4.1.) and compared to the we cannot quantitatively compare the water level data series for the whole year 2016, the relative on the satellitelake water observations extents based on of the June satellite 9, July observations 3, and August of June 20.9, July Even 3, and though August we 20. cannot Even though quantitatively compareincreaseswe the cannot waterof the quantitatively water level extent data compare series and water for the the waterlevel whole arelevel in yeardata good series 2016, agreement. for the the relative whole year increases 2016, the of relative the water extent and waterincreases level of are thein water good extent agreement. and water level are in good agreement. 35 2500 Water level CRWC 35 2500 34 Water level CRWC 2450 Water level MWR 33 34 2450 2400 Water level MWR 33 Water extent from2400 32 2350 WaterSentinel-1 extent from 32 2350 31 Sentinel-1 2300 31 2300 30 2250 30 2250

Water[m]level 29 2200

Water[m]level 29 2200 28 2150 28 2150 water [km²]Lakeextent Lake water water [km²]Lakeextent 27 2100 27 2100 30-May30-May 19-Jun19-Jun 9-Jul9-Jul 29-Jul29-Jul 18-Aug18-Aug Date in 2016 Date in 2016 Figure 11. Real-time water regime observations at the hydrological station Chenglingji during the flood season of 2016. Source: MWR and Changjiang Water Resources Commission (CWRC)—combined with the water extent derived from Sentinel-1. Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 22

Figure 11. Real-time water regime observations at the hydrological station Chenglingji during the

Remote Sens.flood2020 , season12, 1761 of 2016. Source: MWR and Changjiang Water Resources Commission 15 of 21 (CWRC)—combined with the water extent derived from Sentinel-1.

InIn addition, addition, the the extreme extreme water water level level information information reported by by the the Chinese Chinese Ministry Ministry of of Water Water ResourcesResources (MWR) (MWR) [65 [65]] is is also also linked linked to to water water dispersion.dispersion. The The maximum maximum water water level level was was measured measured at at 34.4734.47 m onm on July July 8 at 8 Chenglingjiat Chenglingji Station Station (Figure (Figure 11 ),11 which), which is compatibleis compatible with with the the maximum maximum water water area of Julyarea 3of derived July 3 derived from the from EO the data. EO data.

4.3.4.3. Relationship Relationship between between the the Sentinel-1 Sentinel-1 Lake Lake WaterWater ExtentExtent and Sentinel Sentinel-2-2 TheThe only only cloud-free cloud-free Sentinel-2 Sentinel scene-2 scene (from (from 29 December December 2016), 29, 2016) which, which has only has one only day one diff erence day difference to Sentinel-1 (from December 30, 2016), was used for indirect validation at a high spatial to Sentinel-1 (from 30 December 2016), was used for indirect validation at a high spatial resolution of resolution of 10 meters. The Normalized Difference Water Index (NDWI) was calculated from the 10 m. The Normalized Difference Water Index (NDWI) was calculated from the green and near infrared green and near infrared bands, and a watermask was generated. The NDWI was developed by Mc bands, and a watermask was generated. The NDWI was developed by Mc Feeters [66] to define open Feeters [66] to define open water areas. Figure 12 shows the very good agreement between the water areas. Figure 12 shows the very good agreement between the watermasks of Sentinel-1 and watermasks of Sentinel-1 and Sentinel-2. The green areas in Figure 12a-d are water areas that were Sentinel-2. The green areas in Figure 12a–d are water areas that were detected only by Sentinel-1, detected only by Sentinel-1, because the sediment content in the water is too high for it to be detected becauseas water the in sediment Sentinel-2 content. Further in de theviations water occur is too only high in fora few it tothin be linear detected features as water and at in the Sentinel-2. land– Furtherwater deviationsborder. occur only in a few thin linear features and at the land–water border.

FigureFigure 12. 12.Di Differencefference maps maps of of the the water water extentextent ofof DongtingDongting Lake Lake from from Sentinel Sentinel-1,-1, recorded recorded on on 30 30 DecemberDecember 2016, 2016, and and the the Normalized Normalized Di Differencefference WaterWater IndexIndex ( (NDWI)NDWI) calculated calculated from from the the Sentinel Sentinel-2-2 datadata recorded recorded on on 29 29 December December 2016—left: 2016—left:overview overview map of the Sentinel Sentinel-2-2 data data set set in in false false color color band band combination,combination right, right (a –(ad–):d): di differencefference maps. maps.

5. Discussion5. Discussion InIn this this study, study, Sentinel-1 Sentinel-1 data data were were used used toto mapmap thethe monthly inundation inundation patterns patterns of of the the large large DongtingDongting Lake Lake in unprecedented in unprecedented detail, detail, not seen not seenin any in previous any previous work. work.Challenges Challenges have been have reported been aboutreported cloud about cover cloud and cover the complex and the com spectralplex spectral conditions conditions of lake of waterlake water when when only only optical optical data data are usedare [ 36used,38]. [36,38] A large. A share large of share the conducted of the conducted analyses analyses have used have MODIS used data MODIS and data reported and limitationsreported duelimitations to low spatial due to resolution, low spatial while resolution comprehensive, while comprehensive information wasinformation derived was from derived the high from temporal the resolutionhigh temporal [33,35, 36 resolution,38,40,67]. [33,35,36,38,40,67] Compared to that,. Compared the following to that, advantages the following of Sentinel-1, advantages used here, of canSentinel be clearly-1, used highlighted: here, can i)be the clearly high highlighted: spatial resolution i) the high of the spatial Sentinel-1 resolution data (10of the m) Sentinel that allows-1 data for a spatially(10 meter high-details) that allows analysis for of a surfacespatially water high and-detail related analys wetlandis of surface patterns, water ii) the and temporal related wetlandresolution of the Sentinel-1 time series allows for monthly wetland assessments, and iii) the Sentinel-1 lake water extent provides gap-free information from an active sensor, which is also available during cloudy periods. However, further aspects need to be discussed in the next sections. Remote Sens. 2020, 12, 1761 16 of 21

5.1. Data Availability In the first year of full coverage of the Sentinel-1 data (2016), Dongting Lake was recorded, on average, every 20 days, with at least one acquisition per month. This number of available Sentinel-1 scenes exceeds the availability of previous SAR data; for example, the Envisat ASAR satellite had a regular repeat cycle of 35 days in the Dongting Lake region before 2012 [68], as used by Ding and Li [39]. In addition, the two satellites available today—Sentinel-1 A and B—will most likely enable acquisitions with a 5 day repetition rate, as is currently being realized over Europe, [51]. Given the results of the analyses presented, which are very promising for 2016, this will lead to improved results for Dongting Lake in subsequent years. Especially large and dynamically flooded wetlands are difficult to access for field investigations, as described for Dongting by Wang and Yesou [31], Hu et al. [40], and Wang et al. [35]. Due to large floods in 2016 that were observed by the author on site, field data were not acquired and indirect validation was applied here instead.

5.2. Spatio-Temporal Dynamics As shown in Figure 10, Sentinel-1 is exclusively able to detect small-scale water areas. To complement the flood frequency maps of Dongting Lake from the MODIS data sources previously presented by Huang et al. [33], Hu et al. [40], and Wu and Liu [36], Sentinel-1 data can provide additional spatial information. This allows the differentiation of details related to natural wetland structures, like potential water bird habitats located at dynamically changing small lakes and ponds, as well as human-made wetland structures, such as narrow river channels (Figure6a), small inundated agricultural areas, and aquaculture ponds (Figure6c). In comparison with the time series data set Global WaterPack, although it has a much lower temporal resolution, the inundation frequency derived with Sentinel-1 shows very good agreement in the flood pattern. However, as seen in the differences in Figure 10, the daily data from Global WaterPack may reveal flooded areas that cannot be detected by Sentinel-1 during very dynamic flooding periods due to the higher temporal resolution. Combining the advantages of both data sources for flood mapping is a future task that could lead to further improved monitoring results.

5.3. Monitoring of Dynamics of Large Wetlands Unusually heavy rain during the last El Niño event in 2015–2016 led to very rapid water propagation at Dongting Lake in 2016 that induced the most significant floods after 1998, 1999, and 2003 [60,69]. According to the Global WaterPack, 2016 emerges as the flood year with the third largest monthly maximum extent of Dongting Lake since mid-2003 (2289 km2 in July 2016), which is in good accordance with the Sentinel-1 lake water extent (2465 km2). In such extreme situations, the advantages of using Sentinel-1—the detailed determination of water expansion paired with wide area coverage—were not available before. Therefore, the results of this study are of interest to local authorities such as the Yueyang Hydrological Station (which was visited by the authors), who are concerned with flood warnings and flood impact assessment, and to authorities involved in estimating crop losses, health monitoring related to water-borne diseases such as schistosomiasis, the effects of sand dredging, and the navigability of the lake. Moreover, monthly water extent maps from Sentinel-1 have been applied to detect the onset of flood and inundation processes. This information is useful to distinguish between the water management practices applied to different parts of the lake. Furthermore, the lake is an important wintering habitat for thousands of migratory birds such as the white-napped crane, Oriental stork, and lesser white fronted goose [46,70,71]. Information on water dynamics at landing, staging, and feeding places is of high relevance for conservationists such as the Dongting Lake Station for Wetland Ecosystem Research near the Ramsar site in the north of the lake. Remote Sens. 2020, 12, 1761 17 of 21

5.4. Method and Validation A stable and proven method for water detection from SAR satellite data was adapted for this study and used for the data processing of a Sentinel-1 time series. It has already been used and validated in other regions such as Vietnam [18,57]. Martinis et al. [58] investigated and validated the method in detail in comparison to other water detection algorithms. The overall accuracy was reported to be between 86% and 99%. Since the collection of on-site ground truth data at the water–land border of Dongting Lake was hindered by floods in summer 2016, the time series had to be validated indirectly. The water extent was compared to the Global WaterPack dataset. The Sentinel-1 mean value of the monthly water expansion deviated by only 4% from this product. The water level at the Chenglingji water level station showed a similar tendency during the summer flood; the highest water level in 2016 was measured on July 8 at 34.47 m, close to the largest water extent of Dongting Lake, which was derived on July 3. The Sentinel-1 watermask was furthermore compared with the NDWI calculated from the Sentinel-2 data of a cloud-free date. They also show very good agreement.

5.5. Potential and limitations For the studied wetland ecosystem—the large Yangtze floodpath lake Dongting in China—the Sentinel–1 time series delivered convincing results. Some limitations and potential sources of error must still be discussed here. As already published in the comparative validation study by Martinis et al. [58] with reference to further water detection approaches, submerged vegetation has a strong influence on the classification result. The roughness of the surface induced by vegetation alters the backscatter signal of the surface, and water is not detected reliably by the applied method. This effect can make it underestimate surface water in wetland ecosystems like Dongting Lake and can be counteracted by the use of quad-pol SAR data in subsequent studies. However, quad-pol data with high temporal frequency and high spatial resolution are not available to date because Sentinel-1 data are not (regularly) acquired in this mode, so another source would have to be used, like Radarsat data. Since Sentinel-1 provides dual-pol data in VV/VH polarizations, VV can contribute to the additional detection of submerged vegetation. Sensors that operate in the L-band can also make a major contribution due to the longer wavelength compared to the Sentinel-1 C-band but are not yet available at the required temporal resolution. Submerged vegetation must be detected to complement the results presented, but this is not the focus of this paper. As concluded by Mahdavi et al. [30] and Gstaiger et al. [57], HH polarization and the ascending mode have the greatest potential for water detection. The data used here are collected in ascending orbit, but unfortunately, the HH polarization is not acquired by Sentinel-1. Furthermore, the wind also influences the surface roughness and thus the quality of the surface water detection, which can lead to an underestimation of the surface water, but, as known from Gstaiger et al. [57] and Brisco et al. [3], the C-band is less sensitive to wind-induced surface roughness. Nevertheless, the advantages of using SAR data, such as independence from clouds, far outweigh the potential difficulties.

6. Summary and Conclusions This study presents the first high-resolution SAR time series monitoring of the very large and highly dynamic wetlands of Dongting Lake in China, which have not previously been monitored in such detail. The focus was on the detection and analysis of the spatio-temporal dynamics of the water surface extent of the Dongting Lake, an important Ramsar wetland site and a focus research region of the Sino-European Dragon Programme. The Sentinel-1 SAR time series—with a high spatial resolution (10 m), area-wide coverage, and relatively high temporal resolution (one to three acquisitions per month)—allows for the analysis of monthly water expansion. A well-established and tested threshold-based approach was used, which has proved to produce convincing results. The main findings are as follows: i) Sentinel-1 is exclusively capable of mapping the small-scale water areas and Remote Sens. 2020, 12, 1761 18 of 21 structures of Dongting Lake wetlands; ii) with the used Sentinel-1 time series, it is possible to map the wetland management practices of lake polders—for example, agricultural use was distinguished from natural flooding; and iii) the maximum extent of the lake was detected in July 2016 at 2465 km2, and the minimum water surface of 1331 km2 occurred in October 2016. The Sentinel-1 water extent of Dongting Lake is consistent with the DLR Global WaterPack, as the monthly mean value deviates by only 4%, thus providing good agreement with an available independent data set. Large, dynamically changing wetlands, such as Dongting Lake, can be better analyzed to manage and plan for the future as a functioning wetland ecosystem. The spatio-temporal resolution of the results from the complete annual collection of the Sentinel-1 data provides information products that are of interest and relevance to local authorities involved in the management of water resources in the region. Detailed information on the monthly behavior of water expansion is applicable for flood impact assessment, lake navigability analyses, health monitoring (schistosomiasis risk), and crop loss estimation. The results also support the work of wetland conservationists in evaluating nature conservation concepts such as the Ramsar Convention. This has been confirmed during numerous talks with local authorities. Furthermore, the presented methodology can be applied to other large and highly dynamic wetlands, such as neighboring Poyang Lake, and is expected to provide results of comparable quality.

Author Contributions: J.H. Sentinel-1 data processing, data analyses, method, writing of the first version of the manuscript; U.G. data analyses support, commenting and work on successive versions of the manuscript; C.K. supervision, commenting and work on the successive versions of the manuscript; I.K. processing of the DLR Global WaterPack; X.L. provision of local knowledge and commenting of final manuscript; H.Y. project LI, commenting of manuscript; N.O. commenting of final manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. It was carried out in the framework of the ESA-MOST Dragon 4 Cooperation Program–within the project “New Earth Observations Tools for Water Resource and Quality Monitoring in Yangtze Wetlands and Lakes”–project ID.32442. Acknowledgments: The authors like to thank the reviewers for the valuable comments. The authors like to thank the European Space Agency (ESA) for the provision of Sentinel-1 data. And the authors like to thank David Marshall for the final language check. Conflicts of Interest: No potential conflict of interest was reported by the authors.

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