{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:48:32Z","timestamp":1772902112525,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T00:00:00Z","timestamp":1565654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471283"],"award-info":[{"award-number":["41471283"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human\u2212dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal\u2212spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious\u2212relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal\u2212spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is \u22121.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious\u2212relevant land disturbances with high frequency Landsat archives in an efficient way.<\/jats:p>","DOI":"10.3390\/rs11161891","type":"journal-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T04:31:21Z","timestamp":1565670681000},"page":"1891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Scheme for the Long-Term Monitoring of Impervious\u2212Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6352-2963","authenticated-orcid":false,"given":"Hanzeyu","family":"Xu","sequence":"first","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yuchun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4662-1622","authenticated-orcid":false,"given":"Chong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China"},{"name":"Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), Jiangxi Normal University, Nanchang 330022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6762-2475","authenticated-orcid":false,"given":"Xiao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, Texas A&amp;M University, College Station, TX 77843-3147, USA"}]},{"given":"Hong","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1126\/science.1153012","article-title":"The Urban Transformation of the Developing World","volume":"319","author":"Montgomery","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1016\/j.rse.2010.03.003","article-title":"Mapping global urban areas using MODIS 500-m data: New methods and datasets based on \u2018urban ecoregions\u2019","volume":"114","author":"Schneider","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16083","DOI":"10.1073\/pnas.1211658109","article-title":"Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools","volume":"109","author":"Seto","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"034002","DOI":"10.1088\/1748-9326\/10\/3\/034002","article-title":"A new urban landscape in East\u2013Southeast Asia, 2000\u20132010","volume":"10","author":"Schneider","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2018.10.015","article-title":"A global record of annual urban dynamics (1992\u20132013) from nighttime lights","volume":"219","author":"Zhou","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.scib.2019.04.024","article-title":"40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/01944369608975688","article-title":"Impervious Surface Coverage: The Emergence of a Key Environmental Indicator","volume":"62","author":"Arnold","year":"1996","journal-title":"J. Am. Plan. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2019.04.025","article-title":"An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks","volume":"229","author":"Liu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Deng, C., and Zhu, Z. (2018). Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.01.003","article-title":"Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery","volume":"113","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1109\/TGRS.2011.2128874","article-title":"Modeling Urban Heat Islands and Their Relationship with Impervious Surface and Vegetation Abundance by Using ASTER Images","volume":"49","author":"Weng","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0921-8181(00)00021-7","article-title":"The impact of land use\u2014Land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective","volume":"25","author":"Carlson","year":"2000","journal-title":"Glob. Planet. Chang."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2016.12.011","article-title":"Quantifying annual changes in built-up area in complex urban-rural landscapes from analyses of PALSAR and Landsat images","volume":"124","author":"Qin","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2019.04.020","article-title":"Understanding an urbanizing planet: Strategic directions for remote sensing","volume":"228","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.scitotenv.2019.02.178","article-title":"Characterizing the spatial pattern of annual urban growth by using time series Landsat imagery","volume":"666","author":"Fu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"024008","DOI":"10.1088\/1748-9326\/9\/2\/024008","article-title":"Expansion and growth in Chinese cities, 1978\u20132010","volume":"9","author":"Schneider","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the free and open Landsat data policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2015.06.007","article-title":"A 30-year (1984\u20132013) record of annual urban dynamics of Beijing City derived from Landsat data","volume":"166","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8271","DOI":"10.1080\/01431161.2018.1483088","article-title":"Long-term monitoring of citrus orchard dynamics using time-series Landsat data: A case study in southern China","volume":"39","author":"Xu","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1109\/TGRS.2013.2272545","article-title":"On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data","volume":"52","author":"Brooks","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A.H., Cohen, W.B., Qiu, S., and Zhou, C. (2019). Continuous monitoring of land disturbance based on Landsat time series. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2019.03.009"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hao, B., Ma, M., Li, S., Li, Q., Hao, D., Huang, J., Ge, Z., Yang, H., and Han, X. (2019). Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine. Sensors, 19.","DOI":"10.3390\/s19092118"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cohen, B.W., Healey, P.S., Yang, Z., Stehman, V.S., Brewer, K.C., Brooks, B.E., Gorelick, N., Huang, C., Hughes, J.M., and Kennedy, E.R. (2017). How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?. Forests, 8.","DOI":"10.3390\/f8040098"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2011.09.024","article-title":"Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan","volume":"122","author":"Kennedy","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Nanjing Statistical Bureau (2018). Nanjing Statistical Yearbook."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2013.07.008","article-title":"Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier","volume":"139","author":"Grinand","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5851","DOI":"10.1080\/01431161.2013.798055","article-title":"Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach","volume":"34","author":"Yu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1007\/s11430-014-4919-z","article-title":"A multi-resolution global land cover dataset through multisource data aggregation","volume":"57","author":"Yu","year":"2014","journal-title":"Sci. China Earth Sci."},{"key":"ref_49","unstructured":"Gong, P., Wang, J., Ji, L., and Yu, L. (2019, July 02). FROM-GLC 2015 v0.1. Available online: http:\/\/data.ess.tsinghua.edu.cn\/."},{"key":"ref_50","unstructured":"Pesaresi, M., Ehrlich, D., Florczyk, A., Freire, S., Julea, A., Kemper, T., Soille, P., and Syrris, V. (2015). GHS Built-Up Grid, Derived from Landsat, Multitemporal (1975, 1990, 2000, 2014), European Commission, Joint Research Centre (JRC)."},{"key":"ref_51","unstructured":"National Bureau of Statistics (2017). China City Statistical Yearbook."},{"key":"ref_52","unstructured":"Jiangsu Statistical Bureau (2018). Jiangsu Statistical Yearbook."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","first-page":"13","article-title":"An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS\/Proba) imagery","volume":"18","author":"Chan","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2017.08.036","article-title":"An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_63","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio, RMRS-GTR-164-CD. LA 1-51."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.rse.2007.01.016","article-title":"Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study","volume":"109","author":"Gitelson","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/00031305.1998.10480559","article-title":"Violin Plots: A Box Plot-Density Trace Synergism","volume":"52","author":"Hintze","year":"1998","journal-title":"Am. Stat."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Dannenberg, P.M., Hakkenberg, R.C., and Song, C. (2016). Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm. Remote Sens., 8.","DOI":"10.3390\/rs8080691"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2017.11.015","article-title":"A LandTrendr multispectral ensemble for forest disturbance detection","volume":"205","author":"Cohen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0034-4257(01)00249-8","article-title":"Effects of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus radiometric and geometric calibrations and corrections on landscape characterization","volume":"78","author":"Vogelmann","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.5589\/m08-020","article-title":"Evaluation of Landsat-7 SLC-off image products for forest change detection","volume":"34","author":"Wulder","year":"2008","journal-title":"Can. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:40Z","timestamp":1760188240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,13]]},"references-count":70,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161891"],"URL":"https:\/\/doi.org\/10.3390\/rs11161891","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,13]]}}}