{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:41:08Z","timestamp":1772750468943,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U23A2017"],"award-info":[{"award-number":["U23A2017"]}]},{"name":"National Natural Science Foundation of China","award":["32171648"],"award-info":[{"award-number":["32171648"]}]},{"name":"National Natural Science Foundation of China","award":["2024AFD373"],"award-info":[{"award-number":["2024AFD373"]}]},{"name":"Nature Science Foundation of Hubei Province","award":["U23A2017"],"award-info":[{"award-number":["U23A2017"]}]},{"name":"Nature Science Foundation of Hubei Province","award":["32171648"],"award-info":[{"award-number":["32171648"]}]},{"name":"Nature Science Foundation of Hubei Province","award":["2024AFD373"],"award-info":[{"award-number":["2024AFD373"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The lakes of Jianghan Plain, as an important component of the water bodies in the middle and lower reaches of the Yangtze River plain, have made significant contributions to maintaining the ecological health and promoting the sustainable development of the Jianghan Plain. However, there is a relatively limited understanding regarding the trends of lake area change for different types of lakes and their dominant factors over the past three decades in the Jianghan Plain. Based on the Google Earth Engine (GEE) platform, combined with the water body index method, the changes in area of three different types of lakes (area &gt; 1 km2) in the Jianghan Lake Group from 1990 to 2020 were extracted and analyzed. Additionally, the Partial least squares structural equation model (PLS-SEM) was utilized to analyze the driving factors affecting the changes in water body area of these lakes. The results show that from 1990 to 2020, the area of the lakes of the wet season and level season exhibited a decreasing trend, decreasing by 893.1 km2 and 77.9 km2, respectively. However, the area of dry season lakes increased by 59.27 km2. The areas of all three types of lakes reached their minimum values in 2006. According to the PLS-SEM results, the continuous changes in the lakes\u2019 area are mainly controlled by environmental factors overall. Furthermore, human factors mainly influence the mutation of the lakes\u2019 area. This study achieved precise extraction of water body areas and accurate analysis of driving factors, providing a basis for a comprehensive understanding of the dynamic changes in the lakes of Jianghan Plain, which is beneficial for the rational utilization and protection of water resources.<\/jats:p>","DOI":"10.3390\/rs16111892","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T11:17:52Z","timestamp":1716549472000},"page":"1892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of Lake Area Dynamics and Driving Forces in the Jianghan Plain Based on GEE and SEM for the Period 1990 to 2020"],"prefix":"10.3390","volume":"16","author":[{"given":"Minghui","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6686-4974","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1038\/nclimate3111","article-title":"Earth\u2019s Surface Water Change over the Past 30 Years","volume":"6","author":"Donchyts","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Jiang, W., Ling, Z., Wang, X., Peng, K., and Wang, C. (2021). Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators. Water, 13.","DOI":"10.3390\/w13020138"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128829","DOI":"10.1016\/j.jclepro.2021.128829","article-title":"Additional Surface-Water Deficit to Meet Global Universal Water Accessibility by 2030","volume":"320","author":"Bo","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1672\/1","article-title":"Vegetation Patterns Resulting from Spatial and Temporal Variability in Hydrology, Soils, and Trampling in an Isolated Basin Marsh, New Hampshire, USA","volume":"25","author":"Koning","year":"2005","journal-title":"Wetlands"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.ecolind.2009.05.006","article-title":"Terrestrial Birds as Indicators of Agricultural-Induced Changes and Associated Loss in Conservation Value of Mediterranean Wetlands","volume":"10","author":"Robledano","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-Resolution Mapping of Global Surface Water and Its Long-Term Changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9333","DOI":"10.1007\/s11356-023-31702-2","article-title":"Analysis of Surface Water Area Dynamics and Driving Forces in the Bosten Lake Basin Based on GEE and SEM for the Period 2000 to 2021","volume":"31","author":"Li","year":"2024","journal-title":"Env. Sci. Pollut. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2017.05.039","article-title":"An Approach for Global Monitoring of Surface Water Extent Variations in Reservoirs Using MODIS Data","volume":"202","author":"Khandelwal","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"135","article-title":"Monitoring the Dynamics of Surface Water Fraction from MODIS Time Series in a Mediterranean Environment","volume":"66","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1080\/2150704X.2016.1260178","article-title":"Lake Water Surface Mapping in the Tibetan Plateau Using the MODIS MOD09Q1 Product","volume":"8","author":"Lu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_11","first-page":"284","article-title":"Testing Estimation of Water Surface in Italian Rice District from MODIS Satellite Data","volume":"52","author":"Ranghetti","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2015.11.020","article-title":"Monitoring the River Plume Induced by Heavy Rainfall Events in Large, Shallow, Lake Taihu Using MODIS 250m Imagery","volume":"173","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.02.012","article-title":"Spectral Matching Based on Discrete Particle Swarm Optimization: A New Method for Terrestrial Water Body Extraction Using Multi-Temporal Landsat 8 Images","volume":"209","author":"Jia","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1016\/j.scitotenv.2018.11.390","article-title":"Spatiotemporal Patterns and Effects of Climate and Land Use on Surface Water Extent Dynamics in a Dryland Region with Three Decades of Landsat Satellite Data","volume":"658","author":"Tulbure","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2015.04.009","article-title":"Super-Resolution Mapping of Wetland Inundation from Remote Sensing Imagery Based on Integration of Back-Propagation Neural Network and Genetic Algorithm","volume":"164","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"73","article-title":"A Robust Multi-Band Water Index (MBWI) for Automated Extraction of Surface Water from Landsat 8 OLI Imagery","volume":"68","author":"Wang","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hardy, A., Ettritch, G., Cross, D., Bunting, P., Liywalii, F., Sakala, J., Silumesii, A., Singini, D., Smith, M., and Willis, T. (2019). Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats. Remote Sens., 11.","DOI":"10.3390\/rs11050593"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Schwatke, C., Scherer, D., and Dettmering, D. (2019). Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11091010"},{"key":"ref_19","first-page":"595","article-title":"Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions","volume":"73","author":"Steinhausen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C., Jia, M., Chen, N., and Wang, W. (2018). Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin. Remote Sens., 10.","DOI":"10.3390\/rs10101635"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"110","article-title":"Mapping Cropland Extent of Southeast and Northeast Asia Using Multi-Year Time-Series Landsat 30-m Data Using a Random Forest Classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hird, J., DeLancey, E., McDermid, G., and Kariyeva, J. (2017). Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sens., 9.","DOI":"10.3390\/rs9121315"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"557","DOI":"10.2166\/wcc.2021.282","article-title":"Enhanced Index for Water Body Delineation and Area Calculation Using Google Earth Engine: A Case Study of the Manchar Lake","volume":"13","author":"Ismail","year":"2022","journal-title":"J. Water Clim. Chang."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1073\/pnas.1719275115","article-title":"Divergent Trends of Open-Surface Water Body Area in the Contiguous United States from 1984 to 2016","volume":"115","author":"Zou","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1080\/22797254.2022.2052188","article-title":"Characterizing Surface Water Changes across the Tibetan Plateau Based on Landsat Time Series and LandTrendr Algorithm","volume":"55","author":"Chai","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deng, Y., Jiang, W., Tang, Z., Ling, Z., and Wu, Z. (2019). Long-Term Changes of Open-Surface Water Bodies in the Yangtze River Basin Based on the Google Earth Engine Cloud Platform. Remote Sens., 11.","DOI":"10.3390\/rs11192213"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.034","article-title":"Surface Water Extent Dynamics from Three Decades of Seasonally Continuous Landsat Time Series at Subcontinental Scale in a Semi-Arid Region","volume":"178","author":"Tulbure","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, Y., Ling, F., Liu, Y., and Fang, F. (2017). Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976\u20132015. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6030068"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cavallo, C., Papa, M.N., Gargiulo, M., Palau-Salvador, G., Vezza, P., and Ruello, G. (2021). Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13173525"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 Algorithms in Data Mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Assoc Comp Machinery XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.scitotenv.2019.06.341","article-title":"Continuous Monitoring of Lake Dynamics on the Mongolian Plateau Using All Available Landsat Imagery and Google Earth Engine","volume":"689","author":"Zhou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1207\/S15328007SEM0702_4","article-title":"A New Inferential Test for Path Models Based on Directed Acyclic Graphs","volume":"7","author":"Shipley","year":"2000","journal-title":"Struct. Equ. Model. A Multidiscip. J."},{"key":"ref_44","first-page":"739","article-title":"A Global Goodness-of-Fit Index for PLS Structural Equation Modelling","volume":"1","author":"Tenenhaus","year":"2004","journal-title":"Proc. XLII SIS Sci. Meet."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"118249","DOI":"10.1016\/j.jenvman.2023.118249","article-title":"Determining the Main Contributing Factors to Nutrient Concentration in Rivers in Arid Northwest China Using Partial Least Squares Structural Equation Modeling","volume":"343","author":"Wang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2753\/MTP1069-6679190202","article-title":"PLS-SEM: Indeed a Silver Bullet","volume":"19","author":"Hair","year":"2011","journal-title":"J. Mark. Theory Pract."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"695241","DOI":"10.3389\/fpsyg.2021.695241","article-title":"Applying Social Cognitive Theory in Predicting Physical Activity Among Chinese Adolescents: A Cross-Sectional Study With Multigroup Structural Equation Model","volume":"12","author":"Liu","year":"2022","journal-title":"Front. Psychol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"151310","DOI":"10.1016\/j.scitotenv.2021.151310","article-title":"Spatiotemporal Dynamics of Wetlands and Their Driving Factors Based on PLS-SEM: A Case Study in Wuhan","volume":"806","author":"Wang","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100327","DOI":"10.1016\/j.ijme.2019.100327","article-title":"Entrepreneurship Education Programmes: How Learning, Inspiration and Resources Affect Intentions for New Venture Creation in a Developing Economy","volume":"18","author":"Ahmed","year":"2020","journal-title":"Int. J. Manag. Educ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cheng, C., Zhang, F., Tan, M.L., Kung, H.-T., Shi, J., Zhao, Q., Wang, W., Duan, P., An, C., and Cai, Y. (2022). Characteristics of Dissolved Organic Matter and Its Relationship with Water Quality along the Downstream of the Kaidu River in China. Water, 14.","DOI":"10.3390\/w14213544"},{"key":"ref_51","first-page":"21","article-title":"The Spatial-Temporal Characteristics and Driving Forces Analysis of Water Area Landscape Pattern Changes on the Jianghan Plain","volume":"34","author":"Chang","year":"2023","journal-title":"Adv. Water Sci."},{"key":"ref_52","first-page":"25","article-title":"Evolution and Controlling Factors of Wetlands in Jianghan Plain","volume":"28","author":"Wei","year":"2021","journal-title":"Saf. Environ. Eng."},{"key":"ref_53","first-page":"958","article-title":"Effect of Human Activities and Association Rules Mining on Spatiotemporal Evolution in Jianghan Lake Group","volume":"36","author":"Feng","year":"2018","journal-title":"J. Appl. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113657","DOI":"10.1016\/j.rse.2023.113657","article-title":"Integrating ICESat-2 Altimetry and Machine Learning to Estimate the Seasonal Water Level and Storage Variations of National-Scale Lakes in China","volume":"294","author":"Song","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.1175\/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2","article-title":"Interannual Variability of the Asian Summer Monsoon: Contrasts between the Indian and the Western North Pacific-East Asian Monsoons","volume":"14","author":"Wang","year":"2001","journal-title":"J. Clim."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1029\/2001GL013874","article-title":"A Unified Monsoon Index","volume":"29","author":"Li","year":"2002","journal-title":"Geophys. Res. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1126\/science.269.5224.676","article-title":"Decadal trends in the north-atlantic oscillation\u2014Regional temperatures and precipitation","volume":"269","author":"Hurrell","year":"1995","journal-title":"Science"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1029\/2000GL012745","article-title":"The Atlantic Multidecadal Oscillation and Its Relation to Rainfall and River Flows in the Continental US","volume":"28","author":"Enfield","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41597-024-03223-1","article-title":"A Prolonged Artificial Nighttime-Light Dataset of China (1984\u20132020)","volume":"11","author":"Zhang","year":"2024","journal-title":"Sci. Data"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1038\/s41597-022-01649-z","article-title":"A Comprehensive Data Set of Physical and Human-Dimensional Attributes for China\u2019s Lake Basins","volume":"9","author":"Chen","year":"2022","journal-title":"Sci. Data"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1007\/s00704-020-03333-x","article-title":"Determining the Most Accurate Program for the Mann-Kendall Method in Detecting Climate Mutation","volume":"142","author":"Wang","year":"2020","journal-title":"Theor. Appl. Clim."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1007\/s11769-018-1002-2","article-title":"Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982\u20132015 Time Period","volume":"28","author":"Guo","year":"2018","journal-title":"Chin. Geogr. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1016\/j.rse.2009.04.016","article-title":"Vegetation Dynamics from NDVI Time Series Analysis Using the Wavelet Transform","volume":"113","author":"Gilabert","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.envsoft.2010.07.006","article-title":"Improving Daily Rainfall Estimation from NDVI Using a Wavelet Transform","volume":"26","author":"Quiroz","year":"2011","journal-title":"Environ. Model."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.envsoft.2016.06.007","article-title":"Identifying the Urban-Rural Fringe Using Wavelet Transform and Kernel Density Estimation: A Case Study in Beijing City, China","volume":"83","author":"Peng","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"110481","DOI":"10.1016\/j.ecolind.2023.110481","article-title":"Evolution of Environmental Quality and Its Response to Human Disturbances of the Urban Agglomeration in the Northern Slope of the Tianshan Mountains","volume":"153","author":"Yu","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1023\/B:WARM.0000043140.61082.60","article-title":"The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series","volume":"18","author":"Yue","year":"2004","journal-title":"Water Resour. Manag."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1177\/002224378201900402","article-title":"Recent Developments in Structural Equation Modeling","volume":"19","year":"1982","journal-title":"J. Mark. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical Remotely Sensed Time Series Data for Land Cover Classification: A Review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A Meta-Analysis of Remote Sensing Research on Supervised Pixel-Based Land-Cover Image Classification Processes: General Guidelines for Practitioners and Future Research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ksenak, L., Pukanska, K., Bartos, K., and Blistan, P. (2022). Assessment of the Usability of SAR and Optical Satellite Data for Monitoring Spatio-Temporal Changes in Surface Water: Bodrog River Case Study. Water, 14.","DOI":"10.3390\/w14030299"},{"key":"ref_74","first-page":"132","article-title":"Water Storage Effects of Three Gorges Project on Water Regime of Poyang Lake","volume":"31","author":"Lai","year":"2012","journal-title":"J. Hydroelectr. Eng."},{"key":"ref_75","first-page":"212","article-title":"Analysis on the Characteristics and Driving Factors of Water Area Change in Poyang Lake During Flood Season Under Long Time Series","volume":"28","author":"Tian","year":"2021","journal-title":"Res. Soil. Water Conserv."},{"key":"ref_76","first-page":"177","article-title":"Characteristics and Driving Factors of Water Area Change of Poyang Lake During Dry Season in Recent 40Years","volume":"35","author":"Wu","year":"2021","journal-title":"J. Soil Water Conserv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, M., Wang, L., She, J., Zhu, L., and Li, X. (2021). Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13245125"},{"key":"ref_78","first-page":"1","article-title":"Quantitative Assessment of Water Stage Changes of Poyang Lake in Dry Period and Its Influencing Factors","volume":"39","author":"Wang","year":"2020","journal-title":"J. Hydroelectr. Eng."},{"key":"ref_79","first-page":"25","article-title":"Study on Driving Factors of Temporal and Spatial Evolution of Water Level in Poyang Lake","volume":"39","author":"Guo","year":"2020","journal-title":"J. Hydroelectr. 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