water

Article Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Ruimeng Wang 1, Haoming Xia 1,2,* , Yaochen Qin 1,2 , Wenhui Niu 1, Li Pan 1, Rumeng Li 1, Xiaoyang Zhao 1, Xiqing Bian 1 and Pinde Fu 1,2 1 College of Environment and Planning, Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, China Meteorological Administration Henan Key Laboratory of Agrometeorological · Support and Applied Technique, Henan University, Kaifeng 475001, China; [email protected] (R.W.); [email protected] (Y.Q.); [email protected] (W.N.); [email protected] (L.P.); [email protected] (R.L.); [email protected] (X.Z.); [email protected] (X.B.); [email protected] (P.F.) 2 Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475001, China * Correspondence: [email protected]; Tel.: +86-371-2388-1858



Received: 11 September 2020; Accepted: 23 October 2020; Published: 27 October 2020


Abstract: The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Keywords: surface water; Yellow River Basin; Hetao Plain; Google Earth Engine; Landsat images; climate change

1. Introduction As an important part of the land–water cycle, surface water resources play an important role in promoting national economic development and maintaining the balance of terrestrial and aquatic ecosystems, agriculture, and the ecological environment [1]. In terms of global climate change, all kinds of water resources show obvious characteristics of temporal and spatial differentiation [2–5]. It is necessary to strengthen the dynamic monitoring and investigation of water resources, especially for arid and semi-arid areas. The Yellow River Basin is an important ecological barrier and economic

Water 2020, 12, 3010; doi:10.3390/w12113010 www.mdpi.com/journal/water Water 2020, 12, 3010 2 of 21 area in China. Ecological protection and high-quality development of the Yellow River Basin have become national strategies [6]. The Hetao Plain, as one of the main areas of the Yellow River Basin, is an important commercial grain base in China. Irrigation directly consumed groundwater and surface water resources. In the process of the gradual development of agriculture, the contradiction between the supply and demand for water has become increasingly prominent. Monitoring the dynamic change of water resources in the Hetao Plain and improving the efficiency of water resources are very important to the economic development and agricultural production in the Hetao Plain. In the past hundred years, due to the intensification of human activities and global climate change, surface water resources have generally experienced problems such as a sharp decline in the water volume, deterioration of the water quality, and area shrinkage, which have changed the form of the surface water hydrological cycle and hydrological dynamics [2,3]. These problems have caused huge losses to the life and agricultural production of the residents in the Hetao Plain and greatly affected the development of the local economy. Problems such as the shrinkage of the surface water area, fragmentation of the water landscape, and deterioration of the ecological environment have aroused widespread concern from all walks of life [7]. The dynamic monitoring of surface water is a key issue in research on resources and environmental change [8,9]. The timely and accurate acquisition of surface water change information is an important foundation for water resource protection, utilization, and sustainable development. Remote sensing images data have the characteristics of wide coverage, high revisit frequency, rich information, and low cost; these with different time-space spectra are used in land use and land cover mapping [10]. The satellite data commonly used for water mapping and change monitoring include the Advanced Very High-Resolution Radiometer (AVHRR) [11], Moderate Resolution Imaging Spectroradiometer (MODIS) [12–15], Systeme Probatoire d’Observation de la Terre (SPOT) [16], Visible Infrared Imaging Radiometer Suite Visible Infrared Imaging Radiometer (VIIRS) [17], Landsat [4,8,15,18–25], and Sentinel [26,27], among others. Landsat images data are characterized by long time series (1972 to present), high spatial resolution, strong spectral consistency, and free access; Landsat images data represent the main data source for monitoring the dynamic changes of water bodies with long time series [21,28–30]. Previous studies have frequently selected a single remote sensing image in a year or in different years at the same time to represent the surface water area and interannual variation [8,9,31,32]. Due to the strong interannual and intra-annual variation characteristics of surface water [8,9], an annual or interannual single image, which was selected to characterize the area and time series of surface water, will lead to inaccuracies of the surface water body area and water body number [31,33–36]. Therefore, the use of all existing long-term sequence images is essential for accurately characterizing the interannual and intra-annual changes in surface water. Surface water extraction algorithms based on remote sensing data can be divided into two categories: machine learning algorithms and traditional algorithms [37]. One type is machine learning algorithms, including Support Vector Machine (SVM) [38], Random Forest (RF) [39], Decision Tree (DT) [40], Deep Learning (DL) [41], etc. Machine learning algorithms require professional knowledge on sample selection and algorithm training, and it is difficult to quickly map large regional scales such as the world and a country [42]. Traditional algorithms based on a combination of bands, including the single-band method and multi-band method, can be divided into the inter-spectral relation method and water index method [43]. The multi-band method can make full use of the spectral difference between water and non-water, and it is better than the single-band method in water extraction [44]. The water index method is a commonly used multi-band extraction method, which is combined with other remote sensing indexes to select the threshold of significant difference between a water body and a non-water body. It has high accuracy in water extraction. At the same time, this method has the advantages of rapid extraction, high accuracy, simplicity, robustness, and good repeatability when extracting large areas of water [9,25,34,42,45–48]. Since 2008, Petabyte-Scale Landsat images have been shared free of charge, making it possible for researchers to use all available Landsat images to better capture surface water change information. Water 2020, 12, 3010 3 of 21

At the same time, the ability of cloud computing has been significantly improved in the recent years,Water 2020 and, 12 it, x has FOR shown PEER REVIEW great application potential in large-scale land cover mapping. For example,3 of 22 the Google Earth Engine (GEE), with high-performance parallel computing capabilities, massive remote Google Earth Engine (GEE), with high-performance parallel computing capabilities, massive remote sensing, and geospatial data, is free to use [25,49]. It facilitates the use of all images archived on sensing, and geospatial data, is free to use [25,49]. It facilitates the use of all images archived on the the platform for global and national land cover remote sensing mapping and land cover change platform for global and national land cover remote sensing mapping and land cover change monitoring [10,34–36,50–54]. Based on the GEE platform, 7534 remote sensing images of Landsat monitoring [10,34–36,50–54]. Based on the GEE platform, 7534 remote sensing images of Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+), and Landsat Operational Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+), and Landsat Operational Land Land Imager (OLI) captured from 1989 to 2019 were obtained by JavaScript programming Imager (OLI) captured from 1989 to 2019 were obtained by JavaScript programming operation. The operation. The method of combining the modified Normalized Difference Water Index (mNDWI), method of combining the modified Normalized Difference Water Index (mNDWI), Enhanced Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) was used Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) was used to extract the to extract the accuracy of surface water extraction. The temporal and spatial distribution pattern accuracy of surface water extraction. The temporal and spatial distribution pattern of surface water of surface water in the Hetao Plain in the recent 31 years was obtained, and four water types were in the Hetao Plain in the recent 31 years was obtained, and four water types were generated based generated based on the water body frequency: maximum water body, seasonal water body, permanent on the water body frequency: maximum water body, seasonal water body, permanent water body, water body, and annual average water body [23,55,56]. The interannual variation trend of the surface and annual average water body [23,55,56]. The interannual variation trend of the surface water body water body area and water body number of four water types was analyzed. Finally, the relationship area and water body number of four water types was analyzed. Finally, the relationship between the between the changing trend of surface water and the major driven factors was discussed. changing trend of surface water and the major driven factors was discussed. 2. Data Sets 2. Data Sets 2.1. Study Area 2.1. Study Area The Hetao Plain is located in the middle reaches of the Yellow River Basin (40◦ to 41◦180 N, The Hetao Plain is located in the middle reaches of the Yellow River Basin (40° to 41°18′ N, 106°21′ 106◦210 to 111◦500 E), north of the Great Wall and south of the Yinshan Mountains, from Helan Mountain into 111°50 the west′ E), and north east of tothe Hohhot Great Wall in the and east south (Figure of the1 ).Yinshan The Hetao Mountains, Plain isfrom formed Helan by Mountain the alluvium in the ofwest the and Yellow east River,to Hohhot and in some the ea ofst its (Figure tributaries, 1). The extending Hetao Plain from is formed east to by west the along alluvium the Yellowof the Yellow River, withRiver, a and length some of aboutof its tributaries, 591.6 km, a extending width of 20–90from ea kmst fromto west north along to the south, Yellow and River, an area with of 23,575 a length km of2, areabout fanned 591.6 out km, in a anwidth arc. of The 20–90 administrative km from north departments to south, and belong an area to Alxa of 23,575 League, km Bayannaoer,2, are fanned Erdos,out in Baotou,an arc. The and administrative Hohhot in the departments Inner Mongolia belong Autonomous to Alxa League, Region, Bayannaoer, and the geomorphology Erdos, Baotou, ofand the Hohhot Hetao Plainin the is Inner mainly Mongolia alluvial Autonomous Plain, the average Region, slope and is th 1.8e ºgeomorphology(Figure S1). The of elevation the Hetao of Plain the study is mainly area isalluvial high inPlain, the westthe average and low slope in the is 1.8 east.º (Figure The Hetao S1). The Plain elevation is located of the in an study arid area and is semi-arid high in the region. west Itand is hotlow andin the humid east. inThe the Hetao summer, Plain rainy is located and hot in an in thearid same and semi-arid period, and region. cold andIt is hot dry and in the humid winter, in andthe summer, there is a rainy large temperatureand hot in the di ffsameerence period, between and the cold day and and dry night. in the From winter, 1989 and to 2019, there the is annuala large temperature difference between the day and night. From 1989 to 2019, the annual average temperature average temperature is 8.9 ◦C, and the temperature change decreases gradually from west to east. Theis 8.9 annual °C, and cumulative the temperature precipitation change is decreases 272 mm, andgrad theually trend from increases west to from east. westThe toannual east. cumulative The lowest annualprecipitation cumulative is 272 precipitation mm, and the was trend 173 mmincreases in 1991, from and west the highestto east. annualThe lowest cumulative annual precipitation cumulative valueprecipitation of 454 mmwas occurred173 mm in in 1991, 2012. and Due the to highest a large amountannual cumulative of evaporation precipitation and lack value of precipitation, of 454 mm theoccurred agricultural in 2012. water Due consumptionto a large amount in the of Hetaoevaporation Plain mainly and lack comes of precipitation, from the Yellow the agricultural River, which water is a typicalconsumption irrigated in agriculturalthe Hetao Plain region. mainly The maincomes crops from are the spring Yellow wheat, River, corn, which and is sunflower. a typical irrigated agricultural region. The main crops are spring wheat, corn, and sunflower.

Figure 1. Geographic location and digital elevation model (DEM) of the HetaoHetao Plain.Plain.

Water 2020, 12, 3010 4 of 21

2.2. DataWater Sources 2020, 12, x FOR PEER REVIEW 4 of 22

2.2.1.2.2. Landsat Data Sources Data

Based2.2.1. Landsat on the Data surface reflection data set of Landsat TM, Landsat ETM+, and Landsat OLI on the GEE platform (https://earthengine.google.org/), a total of 7534 images of the study area were obtained (1Based January on the 1989 surface to 31 reflection December data 2019) set of (Figure Landsat2a). TM, According Landsat ETM+, to the Earthand Landsat Resources OLI Satelliteon the GEE platform (https://earthengine.google.org/), a total of 7534 images of the study area were obtained Global Reference System (WRS-2), a total of nine images can cover the Hetao Plain. The image data (January 1, 1989 to December 31, 2019) (Figure 2a). According to the Earth Resources Satellite Global covered by each scene are relatively balanced (Figure2c), and the distribution of images in each Reference System (WRS-2), a total of nine images can cover the Hetao Plain. The image data covered monthby is each also scene relatively are relatively balanced balanced (Figure (Figure2d). 2c), Landsat and the TM distribution and Landsat of images ETM in+ eachsurface month reflectance is also data arerelatively corrected balanced by the (Figure Landsat 2d). Landsat Ecosystem TM and Interference Landsat ETM+ Adaptive surface Processing reflectance data System are corrected (LEDAPS) algorithm.by the TheLandsat Landsat Ecosystem OLI surface Interference reflectance Adaptive data Processing use the Landsat System Surface(LEDAPS) Reflectance algorithm. Coding The (LASRC)Landsat algorithm OLI surface for atmospheric reflectance data correction use the Land [57,58sat]. Surface At the Reflectance same time, Coding these data (LASRC) sets alsoalgorithm use the cloud,for shadow, atmospheric water, correction and snow [57,58]. masks At generated the same time, by the these code data based sets onalso the use Function the cloud, of shadow, Mask algorithm water, (CFMASK)and snow in the masks Surface generated Reflectance by the code (SR) collectionbased on th one Function GEE as badof Mask observations algorithm and(CFMASK) masked in before the the waterSurface detection Reflectance [59,60 (SR)] (Figure collection2b). The on numberGEE as ofbad images observations obtained and from masked di fferent before Landsat the water sensors in 1989–2019detection (Figure[59,60] (Figure2e) varies 2b). greatlyThe number between of images years. obtained The numberfrom different of images Landsat obtained sensors in since 1989– the launch2019 of Landsat(Figure 2e) ETM varies+ in greatly 1999 and between Landsat years. OLI The in 2013number has of increased, images obtained but only since Landsat the launch ETM+ ofwas Landsat ETM+ in 1999 and Landsat OLI in 2013 has increased, but only Landsat ETM+ was available available in 2012. in 2012.

Figure 2. Cont.

Water 2020, 12, 3010 5 of 21

Water 2020, 12, x FOR PEER REVIEW 5 of 22

FigureFigure 2. Landsat2. Landsat images images of of the the study study area area from from 1989 1989 toto 2019:2019: (a) Spatial distribution distribution of of the the number number of all Landsatof all Landsat images; images; (b) spatial(b) spatial distribution distribution of of the the number number of of high-quality high-quality Landsat Landsat images; images; (c) spatial (c) spatial distributiondistribution of theof the number number of of Landsat Landsat imagesimages in each each map; map; (d (d) seasonal) seasonal distribution distribution of the of number the number of of LandsatLandsat images; images; (e )(e the) the number number of of images images obtainedobtained from from different different Landsat Landsat sensors. sensors.

2.2.2.2.2.2. Climate Climate Data Data TheThe annual annual mean mean temperature temperature and and annual annual cumulative cumulative precipitation precipitation werewere calculated based based on on the 3-hourthe 3-hour Global Global Land DataLand AssimilationData Assimilation System System (GLDAS), (GLDAS), for GLDASfor GLDAS fusion fusion satellite satellite and and ground-based ground- observationbased observation data products; data using products; advanced using surface advanced modeling surface and modeling data assimilation and data technology, assimilation the best surfacetechnology, state and the flux best field surface can state be generated and flux field [61]. can GLDAS-2 be generated includes [61]. the GLDAS-2 two datasets includes of the GLDAS-2.0 two (1989–1999datasets years)of GLDAS-2.0 and GLDAS-2.1 (1989–1999 (2000–2019 years) and GLDAS-2.1 years). (2000–2019 years).

2.2.3.2.2.3. Sentinel-2 Sentinel-2 Multispectral Multispectral Instruments Instruments (MSI) Images Images Data Data Sentinel-2Sentinel-2 MSI MSI is a is wide-scan, a wide-scan, high-resolution, high-resolution, multi-spectral multi-spectral earth earth observation observation sensor sensor for carryingfor carrying out Copernicus land monitoring research, it has been successfully applied for mapping and out Copernicus land monitoring research, it has been successfully applied for mapping and monitoring monitoring vegetation, soil, and water cover [26,27]. In the study, Sentinel-2 MSI images were vegetation, soil, and water cover [26,27]. In the study, Sentinel-2 MSI images were collected from collected from July 1, 2019 to July 31, 2019. The accuracy of the maximum surface water body area 1 July 2019 to 31 July 2019. The accuracy of the maximum surface water body area extracted based on extracted based on Landsat images in the same period was verified. In total, 140 Sentinel-2 MSI Landsatimages images were required in the same to cover period thiswas study verified. area. In total, 140 Sentinel-2 MSI images were required to cover this study area. 2.2.4. Globeland 30 Data 2.2.4. Globeland 30 Data The accuracy of the maximum surface water area extracted from Landsat images was verified, ensuringThe accuracy a uniform of the distribution maximum of surfaceverification water points area in extracted water and from non-water Landsat bodies. images We wasused verified,the ensuring2010 aGlobal uniform distribution30-meter land of verification cover pointsremote in watersensing and data non-water product bodies. (GlobeLand30) We used the 2010 Global(http://www.globallandcover.com) 30-meter land cover remote sensing for the dataclassification product of (GlobeLand30) data. The surface (http: cover//www.globallandcover. was divided into comwater) for thebodies classification and non-water of data. bodies, The 1500 surface random cover poin wasts of water divided bodies into and water non-water bodies bodies and non-water were bodies, 1500 random points of water bodies and non-water bodies were generated in the Hetao Plain, and a total of 3000 verification points were uniformly distributed in the study area [62,63] (Figure3). Water 2020, 12, x FOR PEER REVIEW 6 of 22

Watergenerated2020, 12 in, 3010 the Hetao Plain, and a total of 3000 verification points were uniformly distributed in6 of the 21 study area [62,63] (Figure 3).

FigureFigure 3.3. VerificationVerification pointspoints ofof thethe waterwater bodiesbodies andand non-waternon-water bodies.bodies. 3. Research Methods of Surface Water 3. Research Methods of Surface Water 3.1. Surface Water Extraction Algorithm 3.1. Surface Water Extraction Algorithm The commonly used water extraction algorithms can be divided into two categories: machine The commonly used water extraction algorithms can be divided into two categories：machine learning algorithms (SVM [38], RF [39], DT [40], DL [41], etc.) and traditional algorithms (single-band learning algorithms (SVM [38], RF [39], DT [40], DL [41], etc.) and traditional algorithms (single-band method and multi-band method (the inter-spectral relation method and water index method)) [43,44]. method and multi-band method (the inter-spectral relation method and water index method)) [43,44]. Previous studies have shown that there is a significant difference in the response of the different Previous studies have shown that there is a significant difference in the response of the different reflectivity of remote sensing images to water bodies and non-water bodies, and water bodies can be reflectivity of remote sensing images to water bodies and non-water bodies, and water bodies can be effectively extracted by using the index of different spectral combinations and their thresholds [64–67]. effectively extracted by using the index of different spectral combinations and their thresholds [64– In this paper, the water body index is employed (modified normalized difference water index (mNDWI, 67]. In this paper, the water body index is employed (modified normalized difference water index Equation (1)), normalized differential vegetation index (NDVI, Equation (2)), and enhanced vegetation (mNDWI, Equation (1)), normalized differential vegetation index (NDVI, Equation (2)), and enhanced index (EVI, Equation (3)). The existing water algorithm is selected and verified, when the spectral vegetation index (EVI, Equation (3)). The existing water algorithm is selected and verified, when the index of the pixel satisfies mNDWI-NDVI > 0 and EVI < 0.1 or mNDWI-EVI > 0 and EVI < 0.1, the pixels spectral index of the pixel satisfies mNDWI-NDVI > 0 and EVI < 0.1 or mNDWI-EVI > 0 and EVI < 0.1, are water body; otherwise, they are non-water body (Equation (4)) [23,55,56]. The verification results the pixels are water body; otherwise, they are non-water body (Equation (4)) [23,55,56]. The of the algorithm in the study area are shown in Figure4. verification results of the algorithm in the study area are shown in Figure 4.   ρρρgreen − ρswir ()green swir11 mNDWImNDWI== −  (1) ρρ+ (1) ()ρgreengreen+ ρ swir1swir1 ()ρρ− (ρ nirρ red ) NDVI = nir red NDVI = ()ρρ−+ (2)(2) (ρnirnir+ ρred ) ρρρnir −ρred EVI = 2.5 nir− red (3) EVI=× 2.5× ρ + 6 ρ 7.5 ρ + 1 (3) nirρρ+×67.51× red − −× × ρblue + ( nir red blue ) 1, EVI < 0.1 and (mNDWI > EVI or mNDWI > NDVI) Water = < (4) 1， EVI 0.1 and0, (mNDWI>EVIOther conditions or mNDWI>NDVI) Water =  (4) 0， Other conditions Here, ρred, ρgreen, ρblue, ρnir, and ρswir1 are the reflectance of the red band (0.63–0.69 µm), green band (0.52–0.6 µm), blue band (0.45–0.52 µm), near-infrared band 1 (0.77–0.9 µm), and shortwave infrared Here, ρ , ρ , ρ , ρ , and ρ are the reflectance of the red band (0.63–0.69 band 1 (1.55–1.75red µm),g respectively.reen blue nir swir1 μm), green band (0.52–0.6 μm), blue band (0.45–0.52 μm), near-infrared band 1 (0.77–0.9 μm), and shortwave infrared band 1 (1.55–1.75 μm), respectively.

Water 2020, 12, 3010 7 of 21 Water 2020, 12, x FOR PEER REVIEW 7 of 21

Figure 4. Cont.

Water 2020, 12, 3010 8 of 21 Water 2020, 12, x FOR PEER REVIEW 8 of 21

Figure 4. Distribution of the water body and non-water body: (a) Landsat Operational Land Imager (OLI) image (false color composite); (b) in the image, blue is the water body and white is the non-water Figure 4. Distribution of the water body and non-water body: (a) Landsat Operational Land Imager body; (c) spatial distribution of the Enhanced Vegetation Index (EVI) value; (d) pixels scatter diagram (OLI) image (false color composite); (b) in the image, blue is the water body and white is the non- between (modfied Normalized Difference Water Index (mNDWI) > Normalized Difference Vegetation water body; (c) spatial distribution of the Enhanced Vegetation Index (EVI) value; (d) pixels scatter Index (NDVI)) and EVI;(e) pixels that satisfy the conditions of ((mNDWI > NDVI) vs. (EVI < 0.1)) are diagram between (modfied Normalized Difference Water Index (mNDWI) > Normalized Difference water bodies, marked with red scattered points; (f) the red part of the image corresponds to the red Vegetation Index (NDVI)) and EVI; (e) pixels that satisfy the conditions of ((mNDWI >NDVI)vs scatter in the (e) diagram, indicating the water body; (g) pixels scatter diagram between (mNDWI > EVI) (EVI<0.1)) are water bodies, marked with red scattered points; (f) the red part of the image and EVI; (h) pixels that satisfy the conditions of ((mNDWI > EVI) vs. (EVI < 0.1)) are water bodies, corresponds to the red scatter in the (e) diagram, indicating the water body; (g) pixels scatter diagram marked with red scattered points; (i) the red part of the image corresponds to the red scatter in the (h) between (mNDWI > EVI) and EVI; (h) pixels that satisfy the conditions of ((mNDWI >EVI)vs (EVI<0.1)) diagram, indicating the water body. are water bodies, marked with red scattered points; (i) the red part of the image corresponds to the 3.2. Calculationred scatter in Method the (h) of diagram, Maximum, indicating Seasonal, the Permanent,water body.and Annual Average Water Body According to the water frequency (WF) (Equation (5)), the surface water extent was divided into 3.2. Calculation Method of Maximum, Seasonal, Permanent, and Annual Average Water Body four indicators: (1) the maximum extent of the water body in a year is called the maximum water body (WF According0.25); (2) the to the seasonal water extentfrequency of the (WF water) (Equation body, that (5)), is, the surface difference water between extent the was maximum divided andinto ≥ permanentfour indicators: water (1) body, the maximum is called the extent seasonal of the water water body body (0.25 in a yearWF

3.3. Surface Water Area and Dynamic Degree

Water 2020, 12, 3010 9 of 21

3.3. Surface Water Area and Dynamic Degree

3.3.1. Surface Water Area The maximum, seasonal, and permanent water body area refers to the number of such pixels multiplied by the area of a single pixel (Equation (6)) [49]. The annual average water area is the product of the effective water pixels and the water frequency in one year.

XN 6 S = A pixelsize 10− (6) × × i=1

Here, S is the area of the maximum, seasonal, and permanent water body of different types (km2), N is the total number of pixels, A is the number of pixels, and pixelsize is the pixel area (m2).

3.3.2. Dynamic Degree of Surface Water The dynamic degree of surface water represents the change speed of the surface water body area and water body number in unit time (Equation (7)) [68]

sa s 1 K = − b 100% (7) sa × T ×

Here, K is the dynamic degree of surface water; Sa and Sb represent the area and number of the surface water body at the beginning and end of the study, respectively; and T is the research period, in years. The larger the K value, the faster the change rate of surface water in unit time.

3.4. Accuracy Evaluation Method In this study, Sentinel-2 MSI images with a 10 m spatial resolution were used to verify the surface water extraction results with a 30-m spatial resolution (Landsat). Firstly, to maintain a balanced distribution of water and non-water sample points, 1500 random points of water and non-water were generated based on GlobeLand30 water and non-water types. Secondly, 3000 verification points and the water extracted in this study were superimposed on Sentinel-2 MSI images. Visual discrimination of the accuracy of water extraction results and the accuracy evaluation was based on the producer accuracy, user accuracy, overall accuracy, and Kappa coefficient of the confusion matrix [35] (Figure3).

4. Result

4.1. Accuracy of Water Body Mapping Since the maximum, seasonal, permanent, and average water body area were obtained from the same method and images, the classification accuracies of these maps are comparable. Therefore, this study selects the maximum water body area as the verification of the accuracy of our results. As shown in Table1, the confusion matrix is based on the accurate evaluation of the Sentinel-2 MSI images and the largest water system map in the Hetao Plain in 2019. The overall accuracy was 95.9%, the producer accuracy was 93.24%, and the Kappa coefficient was 0.91, which indicates that the water extraction results have high accuracy.

Table 1. The confusion matrix was employed for an accurate evaluation of this study.

Sentinel-2 MSI Water Body Map (2019) Water No Water Total User Accuracy (%) Water 952 55 1007 94.54% No-Water 69 1924 1993 96.54% Total 1021 1979 3000 Overall Accuracy = 95.9% Producer Accuracy (%) 93.24% 97.22% Kappa Coefficient = 0.91 Water 2020, 12, 3010 10 of 21 Water 2020, 12, x FOR PEER REVIEW 10 of 22

4.2. Spatial Distribution of Surface Water 4.2. Spatial Distribution of Surface Water There are 1,430,000 and 830,000 water pixels in the frequency map of the water body in 2019 and There are 1,430,000 and 830,000 water pixels in the frequency map of the water body in 2019 and thethe frequency mapmap ofof the cumulative water body from 1989 to 2019, respectively. The pixels area with WF 0.25 accounted for 3.17% and 5.46% of the total area of the study area, respectively (Figure5a,b). WF ≥≥ 0.25 accounted for 3.17% and 5.46% of the total area of the study area, respectively (Figure 5a, Thereb). There is a greatis a great difference difference in the in spatial the spatial distribution distribution of water of pixelswater with pixels di ffwierentth different frequencies frequencies between 2019 and 1989–2019, where the number of water pixels with WF 0.75 accounted for 51.97% and between 2019 and 1989–2019, where the number of water pixels with≥ WF ≥ 0.75 accounted for 51.97% 32.99%and 32.99% of the of total the total number number of pixels, of pixels, respectively respective (Figurely (Figure5c,d). 5c,d). These These water water pixels pixels represent represent the main the rivers,main rivers, large lakes, large and lakes, reservoirs and reservoirs that make that up themake study up area the and study can area maintain and watercan maintain throughout water the year, which are the main sources of surface water. The number of water pixels with 0.25 WF < 0.75 throughout the year, which are the main sources of surface water. The number of water≤ pixels with accounts0.25 ≤ WF for < 0.75 48.03% accounts and 67.01% for 48.03% of the and total, 67.01% respectively. of the total, The water respectively. pixels are The the water edge pixels of the stream,are the pond,edge of and the large stream, water pond, body, and which large forms water the body, wet wh seasonich forms and the the dry wet season season with and thethe seasonaldry season change. with Forthe example,seasonal thechange. frequency For ofexample, the water the body frequency in the central of the area water of the body Wuliangsuhai in the central Lake area is close of tothe 1, whichWuliangsuhai means thatLake the is waterclose to level 1, which is relatively means deepthat the and water the water level bodyis relatively exists during deep and the the high water and lowbody water exists periods. during the There high are and more low water water bodies periods. in theThere edge are part more and water the frequencybodies in the is lower edge part than and 0.5, whichthe frequency indicates is thatlower the than water 0.5, level which is shallow indicates and that the the water water body level exists is shallow in the wetand seasonthe water and body may dryexists up in in the the wet dry season season. and Figure may5e dry shows up thein the number dry season. of water Figure pixels 5e observed shows the in eightnumber intervals of water of thepixels water observed body frequencyin eight intervals from 1989 of the to 2019.water Frombody 1989frequency to 2019, from the 1989 number to 2019. of water From pixels1989 to with 2019, a frequencythe number range of water of 0.95–1 pixels is with the largest. a frequency The number range of of 0.95–1 water is pixels the largest. in the frequency The number range of water of 0.25–0.35 pixels isin morethe frequency in 2008 and range 2012 of than 0.25–0.35 in other is more years, in and 2008 the and water 2012 frequency than in other in other years, regions and isthe roughly water thefrequency same. in other regions is roughly the same.

(a)

Figure 5. Cont.

Water 2020, 12, 3010 11 of 21

Water 2020, 12, x FOR PEER REVIEW 11 of 22

FigureFigure 5. 5. TheThe water water body body frequency frequency distri distributionbution in in the the Hetao Hetao Plain: Plain: (a ()a )Water Water body body frequency frequency distributiondistribution map map from from 1989 to 2019; (bb)) waterwater bodybody frequencyfrequency distribution distribution map map in in 2019; 2019; (c ()c number) number of ofpixels pixels with with a dia ffdifferenterent water water frequency frequency in the in the interval interval of 0.05 of 0.05 from from 1989 1989 to 2019; to 2019; (d) the (d number) the number of pixels of pixelswith awith different a different water frequency water frequency in the interval in the of interval 0.05 in 2019; of 0.05 (e) thein 2019; number (e) of the pixels number distributions of pixels of distributionsa different water of a different frequency water in the frequency interval of in 0.1 the from interval 1989 of to 0.1 2019. from 1989 to 2019.

4.3.4.3. Temporal Temporal Distribu Distributiontion of of Surface Surface Water Water FromFrom 1989 1989 to to 2019, 2019, the the maximum, maximum, seasonal, seasonal, and and annual annual average average water water body body area area exhibited exhibited a a downwarddownward trend, trend, while while the the permanent permanent water water body body area area displayed displayed an an upward upward trend, trend, as as shown shown in in Figure6. The maximum water body area is 816–1827 km 2, which is 26% to 65% of the average value Figure 6. The maximum water body area is 816–1827 km2, which is −−26% to 65% of the average value (1107 km2). The permanent water body area is 346–757 km2, which is −33% to 46% of the average value (519 km2), after 2001, the permanent water body area showed an upward trend and was

Water 2020, 12, 3010 12 of 21

(1107 km2). The permanent water body area is 346–757 km2, which is 33% to 46% of the average value − (519Water km2 2020), after, 12, x 2001, FOR PEER the permanent REVIEW water body area showed an upward trend and was positively12 of 22 correlated with annual cumulative precipitation (Figure S2, Table S1). The seasonal water body area is positively correlated with annual cumulative precipitation (Figure S2, Table S1). The seasonal water 298–1161Water 2020 km, 122,, x which FOR PEER is REVIEW49% to 97% of the average value (588 km2). The annual average water12 of body 22 body area is 298–1161− km2, which is −49% to 97% of the average value (588 km2). The annual average area is 598–1225 km2, which is 28% to 47% of the average value (831 km2). The surface water body positivelywater body correlated area is with598–1225km annual− cumulative2, which is prec−28%ipitation to 47% (Figure of the S2,average Table valueS1). The (831 seasonal km2). Thewater surface area in the Hetao Plain displays an overall downward trend, with the average annual water body area bodywater area body is 298–1161 area in kmthe2 ,Hetao which Plainis −49% displays to 97% ofan the overall average downward value (588 trend, km2). withThe annual the average average annual decreasing by 1.9 km2 every year. waterwater body body area area is decreasing598–1225km by2, which1.9 km is2 every−28% year.to 47% of the average value (831 km2). The surface water body area in the Hetao Plain displays an overall downward trend, with the average annual water body area decreasing by 1.9 km2 every year.

FigureFigure 6. 1989–2019 6. 1989–2019 interannual interannual variation variation trend trend of the of waterthe water body body area area of di offferent different water water body body types types (maximum,Figure(maximum, 6. 1989–2019 seasonal, seasonal, permanent,interannual perman variation andent, annualand annualtrend average) of average) the water in the in body Hetao the Hetaoarea Plain. of Plain. different water body types (maximum, seasonal, permanent, and annual average) in the Hetao Plain. TheThe amount amount of surface of surface water water was was divided divided into into the maximum,the maximum, seasonal, seasonal, and and permanent permanent water water bodies,bodies,The as shownasamount shown inof in Figuresurface Figure7 .water 7. From From was 19891989 divided to 2019 2019, into, the the the number maximum, number of ofmaximum seasonal, maximum and water waterpermanent bodies bodies was water was232,000– 232,000–635,000,bodies,635,000, as whichshown which isin −Figure40% is 40%to 7. 63%From to 63%of 1989 the of toaverage the 2019 average, the value number value (389,000). (389,000).of maximum The Thenumber water number bodiesof seasonal of seasonalwas 232,000– water water bodies − bodies635,000,was was 173,000–495,000, which 173,000–495,000, is −40% towhich 63% which isof − the39% is average to39% 74% to value of 74% the (389,000). ofaverage the average valueThe number (284,000). value (284,000).of seasonal The number Thewater number of bodies permanent of − permanentwaswater 173,000–495,000, bodies water was bodies 59,000–162,000, which was is 59,000–162,000, −39% to which 74% of is the which−45% average to is 50%45% value of to the(284,000). 50% average of the The value average number (108,000). valueof permanent (108,000).The number − Thewaterof number maximum bodies of maximum was water 59,000–162,000, bodies water bodies(R2 =which 0.3763), (R2 =is0.3763), − 45%seasonal to seasonal 50% water of the water bodies average bodies (R 2value = (R 0.2188),2 = (108,000).0.2188), and and Thepermanent permanent number water of maximum water bodies (R2 = 0.3763), seasonal water bodies (R2 = 0.2188), and permanent water waterbodies bodies (R2 (R =2 0.7058)= 0.7058) showed showed an anoverall overall upward upward trend trend over over the the 31 31years. years. bodies (R2 = 0.7058) showed an overall upward trend over the 31 years.

FigureFigureFigure 7. 7.1989–2019 7.1989–2019 1989–2019 interannual interannual interannual variationvariation variation trendtrend trend of the of nu numberthember number of of different di offf erentdifferent water water water types types types(maximum, (maximum, (maximum, seasonal,seasonal,seasonal, and and and permanent) permanent) permanent) in in the thein Hetaothe Hetao Hetao Plain. Plain. Plain.

TheTheThe number number number (a) (a) and(a) and and area area area (b) (b) distribution(b) distribution distribution ofof the of maximumthe maximum surface surface surface water water water at at different di atff erentdifferent spatial spatial spatial scales scales scales fromfromfrom 1989 1989 1989 to to 2019 to 2019 2019 are are are shown shown shown inin Figure in Figure 88.. Accordin According8. According to tog the to the sizethe size sizeof the of of themaximum the maximum maximum water water water body body bodyarea, the area,area, the 2 2 thewater water body body area area was was divided divided into into 10 categories. 10 categories. The The Thenumber number number of water of of water water bodies bodies bodies larger larger larger than 0.5than than km 0.5 0.5 was km km 2 was was85,85, 85, accounting accounting accounting for for for99.95% 99.95% 99.95% of ofthe of the total the total totalwater water water body body ar bodyea ar andea area and0.022% and 0.022% 0.022%of the of total the of the totalnumber total number of number water of water bodies. of water bodies. The number of water bodies less than 0.005 km2 was 377,803,2 accounting for 0.00008% of the total bodies.The Thenumber number of water of water bodies bodies less less than than 0.005 0.005 km km2 waswas 377,803, 377,803, accounting accounting for for 0.00008% 0.00008% of of the the total water body area and 96.37% of the total water body number. Therefore, the change of water body water body area and 96.37% of the total water body number. Therefore, the change of water body area in the Hetao Plain is mainly affected by large water bodies, while the change in the number of area in the Hetao Plain is mainly affected by large water bodies, while the change in the number of water bodies is mainly affected by small water bodies. water bodies is mainly affected by small water bodies.

Water 2020, 12, 3010 13 of 21 total water body area and 96.37% of the total water body number. Therefore, the change of water body area in the Hetao Plain is mainly affected by large water bodies, while the change in the number of water bodies is mainly affected by small water bodies. Water 2020, 12, x FOR PEER REVIEW 13 of 22

FigureFigure 8. 8.Distribution Distribution of of the the numbernumber (a) and area area ( (bb)) of of the the maximum maximum water water bodies bodies at atdifferent different spatial spatial scales from 1989 to 2019. scales from 1989 to 2019.

InIn this this study, study, the the dynamic dynamic attitudesattitudes ofof the ma maximumximum number number and and area area of of surface surface water water in insix six diffdifferenterent periods periods of of 1989–1994, 1989–1994, 1995–1999,1995–1999, 2000–2004, 2005–2009, 2005–2009, 2010–2014, 2010–2014, and and 2015–2019 2015–2019 were were compared (Tables 2 and 3). The maximum number of water bodies appeared in 2005–2009, with a compared (Tables2 and3). The maximum number of water bodies appeared in 2005–2009, with a value value of 464,241, and the minimum value appeared in 1989–1994, with a value of 300,169. The of 464,241, and the minimum value appeared in 1989–1994, with a value of 300,169. The maximum maximum water body area appeared in 1989–1994, and its value was 1246.471 km2, whilst the water body area appeared in 1989–1994, and its value was 1246.471 km2, whilst the minimum value minimum value appeared in 2000–2004, and its value was 935.649 km2. From 1989 to 2019, the appeared in 2000–2004, and its value was 935.649 km2. From 1989 to 2019, the maximum number of maximum number of water bodies exhibited an upward trend, with an overall dynamic change of water45.75%. bodies However, exhibited the an maximum upward trend,water withbody anarea overall showed dynamic a downward change oftrend, 45.75%. and However,the overall the maximum water body area showed a downward trend, and the overall dynamic change was 12.09%. dynamic change was −12.09%. −

TableTable 2. 2.The The dynamic dynamicattitude attitude ofof thethe maximummaximum water water body body numbers numbers from from 1989 1989 to to2019. 2019.

YearYear Number Number Number Number Change Change Dynamic Dynamic Index Index Overall Overall Dynamic Dynamic Change Change 1989–1994 300169 300,169 - - - - 1995–1999 328336 328,336 28167 28,167 9.38% 9.38% 2000–2004 403831 403,831 75495 75,495 22.99% 22.99% 45.75%45.75% 2005–2009 464241 464,241 6041 6041 14.96% 14.96% 2010–2014 420,587 43,654 9.4% 2010–2014 420587 −43654− −9.4%− 2015–2019 453,496 32,909 7.82% 2015–2019 453496 32909 7.82%

TableTable 3. 3.The The dynamic dynamic attitudeattitude of the maximu maximumm water water body body area area from from 1989 1989 to to2019. 2019.

YearYear AreaArea(km（km2）) Area ChangeChange Dynamic Dynamic Index Index Overall Overall Dynamic Dynamic Change Change 19891989–1994–1994 1246.471 1246.471 -- - - 1995–1999 1122.563 123.908 9.94% 1995–1999 1122.563 −−123.908 −−9.94% 2000–2004 935.649 186.914 16.65% 2000–2004 935.649 −−186.914 −−16.65% 12.09% 2005–2009 1070.912 135.263 14.46% −12.09%− 20052010–2014–2009 1070.912 1178.781 107.869135.263 14.46% 10.07% 20102015–2019–2014 1178.781 1060.578 107.869118.203 10.07%10.03% − − 2015–2019 1060.578 −118.203 −10.03%

Water 2020, 12, 3010 14 of 21 Water 2020, 12, x FOR PEER REVIEW 14 of 22

4.4. Influence Influence of of Drought Drought and Rain Rainyy Years on Surface Water Change Precipitation is one of the important factors affecting affecting the change in the surface water body area and water bodybody number.number. In In 2011 2011 and and 2012, 2012, the the annual annual cumulative cumulative precipitation precipitation in thein Hetaothe Hetao Plain Plain was was246 and246 454and mm, 454 respectively.mm, respectively. Compared Compared with the with 31-year the 31-year annual cumulativeannual cumulative precipitation precipitation of 272 mm, of 2722011 mm, had 2011 a lower had value a lower than value the annualthan the cumulative annual cumulative precipitation, precipitation, which is calledwhich ais dry called year, a dry and year, 2012 andhad a2012 higher had value a higher than value the annual than cumulativethe annual cumulative precipitation, precipitation, which is called which a rainy is called year. Ona rainy the whole, year. Onthe the area whole, and water the area body and number water body of surface number water of su inrface 2012 water was in larger 2012 thanwas larger those inthan 2011 those (Figure in 20119). (FigureThe number 9). The of number water bodies of water in 2012bodies was in 900,000,2012 was which 900,000, was which 340,000 was more 340,000 than more in 2011 than (560,000). in 2011 2 (560,000).The number The of waternumber bodies of water less than bodies 0.005 less km thanaccounted 0.005 km for285.3% accounted of the total.for 85.3% Precipitation of the total. is an Precipitationimportant reason is an for important the formation reason of for small the waterformation bodies, of andsmall the water amount bodies, of precipitation and the amount directly of 2 2 precipitationaffects the number directly of affects water bodies.the number The waterof wate bodyr bodies. area inThe 2012 water was body 1827 area km in, which 2012 was was 1827 1011 km km2, 2 2 whichmore than was that1011 in km 20112 more (816 than km that). Water in 2011 body (816 areas km of2). moreWater than body 50 areas km accountof more forthan 86.6% 50 km of2 theaccount total forwater 86.6% body ofarea, the total indicating water thatbody the area, change indicating in the surfacethat the water change body in areathe surface is mainly water affected body by area large is mainlysurface affected water bodies. by large surface water bodies.

Figure 9. Distribution of the number (a) and area (b) of the largest water body in a dry year (2011) Figure 9. Distribution of the number (a) and area (b) of the largest water body in a dry year (2011) and and wet year (2012). wet year (2012).

4.5. Influence Influence of of Climate Climate Change Change and and Hu Humanman Activities on Surface Water Change The drivers drivers of of open open surface surface water water dynamics dynamics include include both climate both climatechange and change human and activities human [63,69–71];activities [ here,63,69 –we71 ];used here, annual we cumulative used annual precipitation cumulative (AP) precipitation and annual (AP) average and temperature annual average (AT) astemperature measures of (AT) climatic as measures factors, and of climatic irrigation factors, as measures and irrigation of human as activities measures factors. of human The results activities of multiplefactors. Thelinear results regression of multiple analysis linear are regressionshown in Table analysis 4. Precipitation are shown in had Table statistically4. Precipitation significant had positivestatistically effects significant on the maximum positive ewaterffects body on the area maximum and water water body body number, area seasonal and water water body body number, area andseasonal water water body body number, area and and water permanent body number, water body and permanent area and water bodybody areanumber. and waterThe more body precipitation,number. The morethe larger precipitation, the water the body larger area the and water the higher body area the andwater the body higher number. the water The body temperature number. hadThe temperaturestatistically significant had statistically negative significant effects negativeon the maximum effects on thewater maximum body area water and body water area body and number,water body seasonal number, water seasonal body waterarea and body water area andbody water number, body and number, permanent and permanent water body water area body and waterarea and body water number. body number.A higher A temperature higher temperature will redu willce the reduce air pressure, the air pressure, reduce reducethe content the content of water of vaporwater vaporin the inair, the and air, andincrease increase evaporation, evaporation, thus thus increasing increasing the the demand demand for for agricultural agricultural water, water, resulting in a reduction of the surface water body area [23]. Figure 10 shows the overall upward trend of precipitation and temperature in the Hetao Plain in the past 31 years.

Water 2020, 12, 3010 15 of 21 resulting in a reduction of the surface water body area [23]. Figure 10 shows the overall upward trend of precipitation and temperature in the Hetao Plain in the past 31 years.

Table 4. Multiple linear regression analyses of the water body area and water body number with climatic and anthropogenic driving factors in the Hetao Plain. The six dependent variables are maximum water body area, maximum water body number, year-long water body area, year-long water body number, seasonal water body area, and seasonal water body number. AP and AT are the Hetao Plain annual cumulative precipitation and annual average temperature respectively, the three dependent variables are AP, AT, and Irrigation. R2 is the proportion of variance in the dependent variable, which can be explained by the selected explanatory variables. SEE is the standard error of the estimate. F and Sig. are the F-statistic and the p-value associated with it. For each linear regression, the variables were statistically significant (P < 0.05).

Maximum Seasonal Permanent Variables Area Number Area Number Area Number Coef. Coef. Coef. Coef. Coef. Coef. AP 2.899 0.976 2.016 0.752 0.785 0.225 AT Irrigation 8.155 6.269 1.864 − − − Constant 1164.863 126.219 674.052 79.172 489.671 46.479 Model summary R2 0.51 0.391 0.463 0.392 0.245 0.23 SEE 175 76 142 59 81 26 F 14.065 18.627 11.64 18.665 4.384 8.661 Water 2020, 12, x FOR PEER REVIEW 15 of 21 Sig. 0.000 0.000 0.000 0.000 0.022 0.006

Figure 10. The trend of annual cumulative precipitation and the annual average temperature in the FigureHetao Plain10. The from trend 1989 of to annual 2019. cumulative precipitation and the annual average temperature in the Hetao Plain from 1989 to 2019. Irrigation directly consumed groundwater and surface water resources during irrigation transportation as part of the evaporation or leakage loss, so irrigation had statistically significant Irrigation directly consumed groundwater and surface water resources during irrigation negative effects on the water body area and water body number. The irrigated area in the Hetao Plain transportation as part of the evaporation or leakage loss, so irrigation had statistically significant increased from 1989 (5161 km2) to 2019 (12,410.8 km2), and it showed a continuous rise at a rate of negative effects on the water body area and water body number. The irrigated area in the Hetao Plain 217.91 km2/year (Figure 11). The sown area in the Hetao Plain increased from 1989 (8725.8 km2) to increased from 1989 (5161km2) to 2019 (12410.8 km2), and it showed a continuous rise at a rate of 2019 (20,388.6 km2), and showed a continuous rise at a rate of 415.09 km2/year (Figure S3). Figure S6, 217.91 km2/year (Figure 11). The sown area in the Hetao Plain increased from 1989 (8725.8km2) to it can be seen that the irrigation area accounts for more than 50% of the sown area in Hetao Plain. 2019 (20388.6 km2), and showed a continuous rise at a rate of 415.09km2/year (Figure S3). Figure S6, The population and gross domestic product (GDP) of the Hetao Plain have increased rapidly in the past it can be seen that the irrigation area accounts for more than 50% of the sown area in Hetao Plain. The 31 years (Figure S4). Regarding the development of industrialization and urbanization, some surface population and gross domestic product (GDP) of the Hetao Plain have increased rapidly in the past water body areas have become building land (Figure S5). Due to the backward irrigation technology 31 years (Figure S4). Regarding the development of industrialization and urbanization, some surface and insufficient awareness of saving water in the Hetao Plain irrigated area, the surface water has water body areas have become building land (Figure S5). Due to the backward irrigation technology gradually decreased. The change of surface water in arid and semi-arid areas is a comprehensive and insufficient awareness of saving water in the Hetao Plain irrigated area, the surface water has reflection of the impact of climate change and human activities on regional water resources. gradually decreased. The change of surface water in arid and semi-arid areas is a comprehensive reflection of the impact of climate change and human activities on regional water resources.

Figure 11. The trend of an irrigation area in the Hetao Plain from 1989 to 2018.

Table 4. Multiple linear regression analyses of the water body area and water body number with climatic and anthropogenic driving factors in the Hetao Plain. The six dependent variables are maximum water body area, maximum water body number, year-long water body area, year-long water body number, seasonal water body area, and seasonal water body number. AP and AT are the Hetao Plain annual cumulative precipitation and annual average temperature respectively, the three dependent variables are AP, AT, and Irrigation. R2 is the proportion of variance in the dependent variable, which can be explained by the selected explanatory variables. SEE is the standard error of the estimate. F and Sig. are the F-statistic and the p-value associated with it. For each linear regression, the variables were statistically significant (P < 0.05).

Water 2020, 12, x FOR PEER REVIEW 15 of 21

Figure 10. The trend of annual cumulative precipitation and the annual average temperature in the Hetao Plain from 1989 to 2019.

Irrigation directly consumed groundwater and surface water resources during irrigation transportation as part of the evaporation or leakage loss, so irrigation had statistically significant negative effects on the water body area and water body number. The irrigated area in the Hetao Plain increased from 1989 (5161km2) to 2019 (12410.8 km2), and it showed a continuous rise at a rate of 217.91 km2/year (Figure 11). The sown area in the Hetao Plain increased from 1989 (8725.8km2) to 2019 (20388.6 km2), and showed a continuous rise at a rate of 415.09km2/year (Figure S3). Figure S6, it can be seen that the irrigation area accounts for more than 50% of the sown area in Hetao Plain. The population and gross domestic product (GDP) of the Hetao Plain have increased rapidly in the past 31 years (Figure S4). Regarding the development of industrialization and urbanization, some surface water body areas have become building land (Figure S5). Due to the backward irrigation technology and insufficient awareness of saving water in the Hetao Plain irrigated area, the surface water has Water 2020, 12, 3010 16 of 21 gradually decreased. The change of surface water in arid and semi-arid areas is a comprehensive reflection of the impact of climate change and human activities on regional water resources.

Figure 11. The trend of an irrigation area in the Hetao Plain from 1989 to 2018. Figure 11. The trend of an irrigation area in the Hetao Plain from 1989 to 2018. 5. Discussion Table 4. Multiple linear regression analyses of the water body area and water body number with 5.1. Comparison with the JRC Dataset climatic and anthropogenic driving factors in the Hetao Plain. The six dependent variables are maximumThis research water uses body the area, Yearly maximum Water Classificationwater body number, History year-long (V1.1) dataset water body from area, the Jointyear-long Research Centerwater (JRC) body of thenumber, European seasonal Commission water body [21 area,]. It and is composed seasonal water of 3,865,618 body number. Landsat AP TM, and LandsatAT are the ETM +, and LandsatHetao Plain OLI annual images cumulative captured precipitation on March and 16, a 1984nnual and average December temperature 31, 2018. respectively, The data the set three uses an expertdependent system tovariables classify are each AP pixel, AT, and to determine Irrigation. whetherR2 is the itproportion is a water of body, variance and in the the surface dependent water is finallyvariable, divided which into can a seasonal be explained water by bodythe selected and permanent explanatory water variables. body. SEE In is this the study,standard the error frequency of of waterthe estimate. bodies wasF and divided Sig. are the into F-statistic 11 intervals and the (> 0,p-va0.05,lue associated0.1, 0.15, with it.0.2, For each0.25, linear0.35, regression,0.45, 0.55, ≥ ≥ ≥ ≥ ≥ ≥ ≥ ≥ 0.65,the and variables0.75) were (Figure statistically 12). Di ffsignificanterent frequency (P < 0.05). thresholds correspond to the interannual variation ≥ ≥ of surface water. The number of permanent water pixels selected from 1989 to 2019 is consistent with the number of permanent water pixels in the JRC dataset.

5.2. Research Uncertainty This study used Landsat images obtained from three different sensors from 1989 to 2019. Due to the small difference in surface water extraction when the spectral indices of different sensors (Landsat TM/ETM/OLI) are used, this study did not compare the differences [72,73]. Although the frequency threshold of the water body can remove most of the noise when extracting the water body, it will cause part of the water body information to be deleted and the water body area to be underestimated. The mixed pixels at the edge of the water body are the main cause of the pixels classification error. The next step can be combined with higher resolution images (e.g., Sentinel-1 or 2 A/B, Gaofen images) for water body research. This study was located in arid and semi-arid areas. The interannual and intra-annual variation of surface water in the Hetao Plain is significant, but only the interannual variation of surface water is considered in this study, and the intra-annual variation of surface water is not described. It is of great significance to understand the law of continuous surface water change in arid and semi-arid areas (taking the Hetao Plain as an example) for the study of global change, which is worthy of attention in the future. Water 2020, 12, 3010 17 of 21

Water 2020, 12, x FOR PEER REVIEW 17 of 22

FigureFigure 12. 12.Water Water body body frequency frequency threshold thresholdselection; selection; (a) The number number of of water water pixels pixels with with a frequency a frequency >0,>0,0.05, ≥0.05, 0.1,≥0.1, ≥0.15,0.15, ≥0.2, ≥0.25,0.25, ≥0.35,0.35, ≥0.45,0.45, ≥0.55,0.55, ≥0.65,0.65, and and ≥0.750.75 in the in study the study area areafrom from 1989 1989to ≥ ≥ ≥ ≥ ≥ ≥ ≥ ≥ ≥ ≥ to 2019, respectively, and and the the number number of of permanent permanent wa waterter pixels pixels in in the the Joint Joint Research Research Center Center (JRC) (JRC) dataset;dataset; (b )(b Noise) Noise conditions conditions of of insets insets in in thethe 20192019 maximummaximum water water body body maps maps using using different different water water bodybody frequency frequency thresholds: thresholds: 0, 0, 0.05, 0.05, 0.1, 0.1, 0.15, 0.15, 0.2,0.2, andand 0.25.

6. Conclusions5.2 Research Uncertainty ThisThis study study investigated used Landsat the images spatial obtained and temporal from three changes different of sensors surface from water 1989 in to the 2019. Hetao Due Plain to fromthe 1989 small to difference 2019 based in surface on all water available extraction Landsat when TM, the ETM spectral+, and indices OLI of images different in thesensors GEE (Landsat platform. UsingTM/ theseETM/ data,OLI) theare used, area andthis numberstudy did of not di ffcomparerent watere the differences body range [72,73]. indexes Although were analyzed, the frequency as we investigatedthreshold of the the variability water body of watercan remove bodies most in the of pastthe noise 32 years when and extracting determined the water the changing body, it will trend. Thecause results part showed of the water that body the maximum, information seasonal, to be deleted and and annual the water average body water area to body be underestimated. area exhibited a downwardThe mixed trend pixels on at the the whole, edge of while the water the maximum, body are the seasonal, main cause and of permanent the pixels classification water body numbererror. The next step can be combined with higher resolution images (e.g., Sentinel-1 or 2 A/B, Gaofen displayed an upward trend overall. The change of surface water in arid and semi-arid areas is a images) for water body research. This study was located in arid and semi-arid areas. The interannual comprehensive reflection of the impact of climate change and human activities on regional water and intra-annual variation of surface water in the Hetao Plain is significant, but only the interannual resources. Precipitation had statistically significant positive effects on the water body area and water variation of surface water is considered in this study, and the intra-annual variation of surface water body number, and it had statistically significant negative effects with temperature and irrigation. is not described. It is of great significance to understand the law of continuous surface water change Thein Hetao arid and Plain semi-arid is an important areas (taking commodity the Hetao grain Plain base as in an China, example) while for agriculture the study is of the global pillar change, industry of thewhich region’s is worthy economy. of attention The dynamic in the future. change of surface water has an important impact on agricultural development. It is essential to understand the area, number, and influencing factors of surface water

Water 2020, 12, 3010 18 of 21 changes to formulate reasonable plans in the fields of water resources management, agricultural irrigation, and ecological protection to improve the sustainable use of water resources and achieve economic and social sustainability.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/12/11/3010/s1, Figure S1: The slope value in the Hetao Plain. Figure S2: The time series of the permanent water body area and annual cumulative precipitation in the Hetao Plain from 1989 to 2019. Figure S3: The trend of sown area in the Hetao Plain from 1989 to 2018. Figure S4: The trend of population and GDP in the Hetao Plain from 1989 to 2018. Figure S5: Changes in the land-use area in the Hetao Plain from 1990 to 2015. Figure S6: The ratio of irrigated area to the sown area in the Hetao Plain from 1989 to 2018. Table S1: The trend and correlation in the permanent water body area and precipitation in the Hetao Plain. Author Contributions: This research was carried out under the cooperation of all authors. H.X. provided the writing ideas for the research, R.W. completed data collection, analysis and wrote the paper, and H.X., Y.Q., W.N., L.P., R.L., X.Z., X.B. and P.F. all contributed to the discussion and revision of the paper. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Open Fund of CMA Henan Key Laboratory of Agrometeorological Support and Applied Technique (AMF201809), and the Major project· of Collaborative Innovation Center on Yellow River Civilization of Henan Province (2020M19). We are grateful to all contractors, image providers. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Liu, J.; Yang, H.; Gosling, S.N.; Kummu, M.; Florke, M.; Pfister, S.; Hanasaki, N.; Wada, Y.; Zhang, X.; Zheng, C.; et al. Water scarcity assessments in the past, present and future. Earth Future 2017, 5, 545–559. [CrossRef][PubMed] 2. Aherne, J.; Larssen, T.; Cosby, B.J.; Dillon, P.J. Climate variability and forecasting surface water recovery from acidification: Modelling drought-induced sulphate release from wetlands. Sci. Total Environ. 2006, 365, 186–199. [CrossRef] 3. Ferguson, I.M.; Maxwell, R.M. Human impacts on terrestrial hydrology: Climate change versus pumping and irrigation. Environ. Res. Lett. 2012, 7, 0044022. [CrossRef] 4. Mueller, N.; Lewis, A.; Roberts, D.; Ring, S.; Melrose, R.; Sixsmith, J.; Lymburner, L.; McIntyre, A.; Tan, P.; Curnow, S.; et al. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens. Environ. 2016, 174, 341–352. [CrossRef] 5. Tulbure, M.G.; Broich, M. Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS J. Photogramm. Remote Sens. 2013, 79, 44–52. [CrossRef] 6. Yin, Y.Y.; Tang, Q.H.; Liu, X.C.; Zhang, X.J. Water scarcity under various socio-economic pathways and its potential effects on food production in the Yellow River basin. Hydrol. Earth Syst. Sci. 2017, 21, 791–804. [CrossRef] 7. Song, C.; Huang, B.; Ke, L. Modeling and analysis of lake water storage changes on the Tibetan Plateau using multi-mission satellite data. Remote Sens. Environ. 2013, 135, 25–35. [CrossRef] 8. Hall, J.W.; Grey, D.; Garrick, D.; Fung, F.; Brown, C.; Dadson, S.J.; Sadoff, C.W. Coping with the curse of freshwater variability. Science 2014, 346, 429–430. [CrossRef] 9. Tulbure, M.G.; Broich, M.; Stehman, S.V.; Kommareddy, A. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sens. Environ. 2016, 178, 142–157. [CrossRef] 10. Zurqani, H.A.; Post, C.J.; Mikhailova, E.A.; Schlautman, M.A.; Sharp, J.L. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 2018, 69, 175–185. [CrossRef] 11. Holben, B.N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 2007, 7, 1417–1434. [CrossRef] 12. Li, S.; Sun, D.; Goldberg, M.; Stefanidis, A. Derivation of 30-m-resolution water maps from TERRA/MODIS and SRTM. Remote Sens. Environ. 2013, 134, 417–430. [CrossRef] 13. Feng, L.; Hou, X.; Zheng, Y. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations. Remote Sens. Environ. 2019, 221, 675–686. [CrossRef] Water 2020, 12, 3010 19 of 21

14. Chen, L.; Michishita, R.; Xu, B. Abrupt spatiotemporal land and water changes and their potential drivers in Poyang Lake, 2000–2012. ISPRS J. Photogramm. Remote Sens. 2014, 98, 85–93. [CrossRef] 15. Chen, B.; Chen, L.; Huang, B.; Michishita, R.; Xu, B. Dynamic monitoring of the Poyang Lake wetland by integrating Landsat and MODIS observations. ISPRS J. Photogramm. Remote Sens. 2018, 139, 75–87. [CrossRef] 16. Harvey, K.R.; Hill, G.J.E. Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: A comparison of aerial photography, Landsat TM and SPOT satellite imagery. Int. J. Remote Sens. 2001, 22, 2911–2925. [CrossRef] 17. Li, S.; Sun, D.; Goldberg, M.D.; Sjoberg, B.; Santek, D.; Hoffman, J.P.; DeWeese, M.; Restrepo, P.; Lindsey, S.; Holloway, E. Automatic near real-time flood detection using Suomi-NPP/VIIRS data. Remote Sens. Environ. 2018, 204, 672–689. [CrossRef] 18. Liao, A.; Chen, L.; Chen, J.; He, C.; Cao, X.; Chen, J.; Peng, S.; Sun, F.; Gong, P. High-resolution remote sensing mapping of global land water. Sci. China Earth Sci. 2014, 57, 2305–2316. [CrossRef] 19. Feng, M.; Sexton, J.O.; Channan, S.; Townshend, J.R. A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic–spectral classification algorithm. Int. J. Digit. Earth. 2015, 9, 113–133. [CrossRef] 20. Carroll, M.; Wooten, M.; DiMiceli, C.; Sohlberg, R.; Kelly, M. Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991–2011. Remote Sens. 2016, 8, 622. [CrossRef] 21. Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [CrossRef] 22. Sheng, Y.; Song, C.; Wang, J.; Lyons, E.A.; Knox, B.R.; Cox, J.S.; Gao, F. Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery. Remote Sens. Environ. 2016, 185, 129–141. [CrossRef] 23. Zou, Z.; Dong, J.; Menarguez, M.A.; Xiao, X.; Qin, Y.; Doughty, R.B.; Hooker, K.V.; David Hambright, K. Continued decrease of open surface water body area in Oklahoma during 1984–2015. Sci. Total. Environ. 2017, 595, 451–460. [CrossRef] 24. Wang, X.; Xiao, X.; Zou, Z.; Hou, L.; Qin, Y.; Dong, J.; Doughty, R.B.; Chen, B.; Zhang, X.; Chen, Y.; et al. Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 2020, 163, 312–326. [CrossRef][PubMed] 25. Wang, X.; Xiao, X.; Zou, Z.; Chen, B.; Ma, J.; Dong, J.; Doughty, R.B.; Zhong, Q.; Qin, Y.; Dai, S.; et al. Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine. Remote Sens. Environ. 2020, 238, 110987. [CrossRef] 26. Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10 m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sens. 2016, 8, 354. [CrossRef] 27. Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens. 2017, 9, 596. [CrossRef] 28. Hansen, M.C.; Egorov, A.; Potapov, P.V.; Stehman, S.V.; Tyukavina, A.; Turubanova, S.A.; Roy, D.P.; Goetz, S.J.; Loveland, T.R.; Ju, J.; et al. Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sens. Environ. 2014, 140, 466–484. [CrossRef] 29. Yamazaki, D.; Trigg, M.A.; Ikeshima, D. Development of a global ~90m water body map using multi-temporal Landsat images. Remote Sens. Environ. 2015, 171, 337–351. [CrossRef] 30. Hou, X.; Feng, L.; Duan, H.; Chen, X.; Sun, D.; Shi, K. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sens. Environ. 2017, 190, 107–121. [CrossRef] 31. Feng, L.; Hu, C.; Chen, X.; Li, R. Satellite observations make it possible to estimate Poyang Lake’s water budget. Environ Res. Lett. 2011, 6, 044023. [CrossRef] 32. Homer, C.; Dewitz, J.; Yang, L.; Jin, S.; Danielson, P.; Xian, G.; Coulston, J.; Herold, N.; Wickham, J.; Megown, K. Completion of the 2011 National Land Cover Database for the Conterminous United States—Representing a Decade of Land Cover Change Information. Photogramm. Eng. Remote Sens. 2015, 81, 345–354. 33. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [CrossRef] Water 2020, 12, 3010 20 of 21

34. Dong, J.; Xiao, X.; Menarguez, M.A.; Zhang, G.; Qin, Y.; Thau, D.; Biradar, C.; Moore, B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 2016, 185, 142–154. [CrossRef] 35. Shelestov, A.; Lavreniuk, M.; Kussul, N.; Novikov, A.; Skakun, S. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Front. Earth Sci. 2017, 5, 17. [CrossRef] 36. Xiong, J.; Thenkabail, P.S.; Gumma, M.K.; Teluguntla, P.; Poehnelt, J.; Congalton, R.G.; Yadav, K.; Thau, D. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 2017, 126, 225–244. [CrossRef] 37. Zhou, Y.; Dong, J. Review on monitoring open surface water body using remote sensing. J. Geogr. Inf. Sci. 2019, 21, 1768–1778. 38. Feng, Q.; Liu, J.; Gong, J. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water 2015, 7, 1437–1455. [CrossRef] 39. Ko, B.; Kim, H.H.; Nam, J.Y. Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers. Sensors 2015, 15, 13763–13777. [CrossRef] 40. Acharya, T.D.; Lee, D.H.; Yang, I.T.; Lee, J.K. Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree. Sensors 2016, 16, 1075. [CrossRef] 41. Hinton, G.E.; Salakhutdinov, R.R. Reducing the Dimensionality of Data with Neural Networks. Science 2006, 313, 504–507. [CrossRef][PubMed] 42. Li, W.; Du, Z.; Ling, F.; Zhou, D.; Wang, H.; Gui, Y.; Sun, B.; Zhang, X. A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI. Remote Sens. 2013, 5, 5530–5549. [CrossRef] 43. Huang, C.; Chen, Y.; Zhang, S.; Wu, J. Detecting, Extracting, and Monitoring Surface Water from Space Using Optical Sensors: A Review. Rev. Geophys. 2018, 56, 333–360. [CrossRef] 44. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 2007, 17, 1425–1432. [CrossRef] 45. Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 2014, 140, 23–35. [CrossRef] 46. Fisher, A.; Flood, N.; Danaher, T. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sens. Environ. 2016, 175, 167–182. [CrossRef] 47. Chen, B.; Xiao, X.; Li, X.; Pan, L.; Doughty, R.; Ma, J.; Dong, J.; Qin, Y.; Zhao, B.; Wu, Z.; et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens. 2017, 131, 104–120. [CrossRef] 48. Mohammadi, A.; Costelloe, J.F.; Ryu, D. Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone flood Plains. Remote Sens. Environ. 2017, 190, 70–82. [CrossRef] 49. Wang, Y.; Ma, J.; Xiao, X.; Wang, X.; Dai, S.; Zhao, B. Long-Term Dynamic of Poyang Lake Surface Water: A Mapping Work Based on the Google Earth Engine Cloud Platform. Remote Sens. 2019, 11, 313. [CrossRef] 50. Liu, H.; Yin, Y.; Piao, S.; Zhao, F.; Engels, M.; Ciais, P. Disappearing lakes in semiarid Northern China: Drivers and environmental impact. Environ Sci. Technol. 2013, 47, 12107–12114. [CrossRef] 51. Jin, S.; Yang, L.; Danielson, P.; Homer, C.; Fry, J.; Xian, G. A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sens. Environ. 2013, 132, 159–175. [CrossRef] 52. Chen, F.; Zhang, M.; Tian, B.; Li, Z. Extraction of Glacial Lake Outlines in Tibet Plateau Using Landsat 8 Imagery and Google Earth Engine. IEEE J. Sel. Top Appl. Earth Obs. Remote Sens. 2017, 10, 4002–4009. [CrossRef] 53. Nyland, E.K.; Gunn, G.E.; Shiklomanov, N.I.; Engstrom, N.R.; Streletskiy, D.A. Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine. Remote Sens. 2018, 10, 1226. [CrossRef] 54. Zou, Z.; Xiao, X.; Dong, J.; Qin, Y.; Doughty, R.B.; Menarguez, M.A.; Zhang, G.; Wang, J. Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016. Proc. Natl. Acad. Sci. USA 2018, 115, 3810–3815. [CrossRef][PubMed] Water 2020, 12, 3010 21 of 21

55. Xia, H.; Zhao, J.; Qin, Y.; Yang, J.; Cui, Y.; Song, H.; Ma, L.; Jin, N.; Meng, Q. Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sens. 2019, 11, 1824. [CrossRef] 56. Wang, X.; Xiao, X.; Zou, Z.; Dong, J.; Qin, Y.; Doughty, R.B.; Menarguez, M.A.; Chen, B.; Wang, J.; Ye, H.; et al. Gainers and losers of surface and terrestrial water resources in China during 1989–2016. Nat. Commun. 2020, 11, 3471. [CrossRef][PubMed] 57. Dwyer, J.L.; Roy, D.P.; Sauer, B.; Jenkerson, C.B.; Zhang, H.K.; Lymburner, L. Analysis ready data: Enabling analysis of the Landsat archive. Remote Sens. 2018, 10, 1363. 58. Wulder, M.A.; White, J.C.; Loveland, T.R.; Woodcock, C.E.; Belward, A.S.; Cohen, W.B.; Fosnight, E.A.; Shaw, J.; Masek, J.G.; Roy, D.P. The global Landsat archive: Status, consolidation, and direction. Remote Sens. Environ. 2016, 185, 271–283. [CrossRef] 59. Zhou, Y.; Dong, J.; Xiao, X.; Liu, R.; Zou, Z.; Zhao, G.; Ge, Q. Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine. Sci. Total Environ. 2019, 689, 366–380. [CrossRef] 60. Foga, S.; Scaramuzza, P.L.; Guo, S.; Zhu, Z.; Dilley, R.D.; Beckmann, T.; Schmidt, G.L.; Dwyer, J.L.; Joseph Hughes, M.; Laue, B. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 2017, 194, 379–390. [CrossRef] 61. Rodell, M.; Houser, P.R.; Jambor, U.E.A.; Gottschalck, J.; Mitchell, K.; Meng, C.J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M.; et al. The Global Land Data Assimilation System. Bam. Meteorol. Soc. 2004, 85, 381–394. [CrossRef] 62. Jun, C.; Ban, Y.; Li, S. Open access to Earth land-cover map. Nature 2014, 514, 434. [CrossRef] 63. Tao, S.; Fang, J.; Zhao, X.; Zhao, S.; Shen, H.; Hu, H.; Tang, Z.; Wang, Z.; Guo, Q. Rapid loss of lakes on the Mongolian Plateau. Proc. Indian Natl. Sci. 2015, 112, 2281–2286. [CrossRef][PubMed] 64. Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [CrossRef] 65. Lei, J.I.; Zhang, L.I.; Wylie, B. Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogramm. Eng. Remote Sens. 2009, 75, 1307–1317. 66. Verpoorter, C.; Kutser, T.; Tranvik, L. Automated mapping of water bodies using Landsat multispectral data. Limnol. Oceanogr. Meth. 2012, 10, 1037–1050. [CrossRef] 67. Santoro, M.; Wegmueller, U.; Lamarche, C.; Bontemps, S.; Defoumy, P.; Arino, O. Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale. Remote Sens. Environ. 2015, 171, 185–201. [CrossRef] 68. Su, K.; Wei, D.Z.; Lin, W.X. Evaluation of ecosystem services value and its implications for policy making in China—A case study of Fujian province. Ecol. Indic. 2020, 108, 105752. [CrossRef] 69. Yigzaw, W.; Hossain, F. Water sustainability of large cities in the United States from the perspectives of population increase, anthropogenic activities, and climate change. Earth Future 2016, 4, 603–617. [CrossRef] 70. Xia, H.; Qin, Y.; Feng, G.; Meng, Q.; Cui, Y.; Song, H.; Ouyang, Y.; Liu, G. Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China. Forests 2019, 10, 1007. [CrossRef] 71. Xia, H.; Zhao, W.; Li, A.; Bian, J.; Zhang, Z. Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China. Remote Sens. 2017, 9, 31. [CrossRef] 72. Zhai, K.; Wu, X.; Qin, Y.; Du, P. Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo. Spat. Inf. Sci. 2015, 18, 32–42. [CrossRef] 73. Zhou, Y.; Dong, J.; Xiao, X.; Xiao, T.; Yang, Z.; Zhao, G.; Zou, Z.; Qin, Y. Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water 2017, 9, 256. [CrossRef]

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).