{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:53:07Z","timestamp":1783619587040,"version":"3.55.0"},"reference-count":78,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,7]],"date-time":"2022-08-07T00:00:00Z","timestamp":1659830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Fund of China","award":["41401659"],"award-info":[{"award-number":["41401659"]}]},{"name":"National Natural Science Fund of China","award":["2015JY0145"],"award-info":[{"award-number":["2015JY0145"]}]},{"name":"National Natural Science Fund of China","award":["kj-2021-5"],"award-info":[{"award-number":["kj-2021-5"]}]},{"name":"Science and Technology Department of Sichuan Province","award":["41401659"],"award-info":[{"award-number":["41401659"]}]},{"name":"Science and Technology Department of Sichuan Province","award":["2015JY0145"],"award-info":[{"award-number":["2015JY0145"]}]},{"name":"Science and Technology Department of Sichuan Province","award":["kj-2021-5"],"award-info":[{"award-number":["kj-2021-5"]}]},{"name":"Natural Resources Department of Sichuan Province","award":["41401659"],"award-info":[{"award-number":["41401659"]}]},{"name":"Natural Resources Department of Sichuan Province","award":["2015JY0145"],"award-info":[{"award-number":["2015JY0145"]}]},{"name":"Natural Resources Department of Sichuan Province","award":["kj-2021-5"],"award-info":[{"award-number":["kj-2021-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input\u2013output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas.<\/jats:p>","DOI":"10.3390\/rs14153806","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries"],"prefix":"10.3390","volume":"14","author":[{"given":"Shan","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaiyong","family":"Shao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xian","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqiang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"You","sequence":"additional","affiliation":[{"name":"Lizhou Branch of Guangyuan Natural Resources Bureau, Guangyuan 628017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Management Science, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8183-0297","authenticated-orcid":false,"given":"Jiaguo","family":"Qi","sequence":"additional","affiliation":[{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111873","DOI":"10.1016\/j.rse.2020.111873","article-title":"Monitoring cropland abandonment with Landsat time series","volume":"246","author":"Yin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Min, R., Yang, H., Mo, X., Qi, Y., Xu, D., and Deng, X. (2022). Does Institutional Social Insurance Cause the Abandonment of Cultivated Land? Evidence from Rural China. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19031117"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0169-2046(03)00112-9","article-title":"The role of land abandonment in landscape dynamics in the SPA \u2018Encinares del r\u00edo Alberche y Cofio, Central Spain 1984\u20131999","volume":"66","author":"Perry","year":"2004","journal-title":"Landsc. Urban Plan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.gloenvcha.2013.07.004","article-title":"Food production and climate protection\u2014What abandoned lands can do to preserve natural forests","volume":"23","author":"Knoke","year":"2013","journal-title":"Glob. Environ. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.catena.2015.06.002","article-title":"Large-scale carbon sequestration in post-agrogenic ecosystems in Russia and Kazakhstan","volume":"133","author":"Kurganova","year":"2015","journal-title":"Catena"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.jaridenv.2008.06.006","article-title":"Development of spatial heterogeneity in vegetation and soil properties after land abandonment in a semi-arid ecosystem","volume":"72","author":"Lesschen","year":"2008","journal-title":"J. Arid Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.biocon.2015.07.041","article-title":"Post-Soviet land-use change effects on large mammals\u2019 habitat in European Russia","volume":"191","author":"Sieber","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, S., Shao, H., Xian, W., Zhang, S., Zhong, J., and Qi, J. (2021). Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13193956"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","article-title":"Mapping abandoned agriculture with multi-temporal MODIS satellite data","volume":"124","author":"Alcantara","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"035035","DOI":"10.1088\/1748-9326\/8\/3\/035035","article-title":"Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data","volume":"8","author":"Alcantara","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"142651","DOI":"10.1016\/j.scitotenv.2020.142651","article-title":"Mapping abandoned farmland in China using time series MODIS NDVI","volume":"755","author":"Zhu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"024021","DOI":"10.1088\/1748-9326\/7\/2\/024021","article-title":"Effects of institutional changes on land use: Agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe","volume":"7","author":"Prishchepov","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2018.02.050","article-title":"Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series","volume":"210","author":"Yin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.scitotenv.2018.06.326","article-title":"Spatial variation in determinants of agricultural land abandonment in Europe","volume":"644","author":"Levers","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.isprsjprs.2020.05.021","article-title":"Detecting abandoned farmland using harmonic analysis and machine learning","volume":"166","author":"Yoon","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_19","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_20","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2021). ESA WorldCover 10 m 2020 v100. Zenodo."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/s41586-021-03957-7","article-title":"A global inventory of photovoltaic solar energy generating units","volume":"598","author":"Kruitwagen","year":"2021","journal-title":"Nature"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1080\/15481603.2022.2026638","article-title":"Mapping fallow fields using Sentinel-1 and Sentinel-2 archives over farming-pastoral ecotone of Northern China with Google Earth Engine","volume":"59","author":"Wuyun","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/LGRS.2008.919685","article-title":"Unmixing-Based Landsat TM and MERIS FR Data Fusion","volume":"5","author":"Clevers","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2013.03.021","article-title":"Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method","volume":"135","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/JSTARS.2018.2797894","article-title":"Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2015.11.016","article-title":"A flexible spatiotemporal method for fusing satellite images with different resolutions","volume":"172","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111718","DOI":"10.1016\/j.rse.2020.111718","article-title":"Spatially and temporally complete Landsat reflectance time series modelling: The fill-and-fit approach","volume":"241","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2012.04.019","article-title":"A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images","volume":"124","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.isprsjprs.2021.08.015","article-title":"A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky\u2013Golay filter","volume":"180","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112130","DOI":"10.1016\/j.rse.2020.112130","article-title":"Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction","volume":"252","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.06.014","article-title":"A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine","volume":"155","author":"Kong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vollrath, A., Mullissa, A., and Reiche, J. (2020). Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine. Remote Sensing, 12.","DOI":"10.3390\/rs12111867"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5969","DOI":"10.5194\/essd-13-5969-2021","article-title":"NESEA-Rice10: High-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019","volume":"21","author":"Han","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Verhelst, K., Gou, Y., Herold, M., and Reiche, J. (2021). Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1. Forests, 12.","DOI":"10.3390\/f12101374"},{"key":"ref_41","first-page":"044504","article-title":"Combing Sentinel-1 and Sentinel-2 image time series for invasive Spartina alterniflora mapping on Google Earth Engine: A case study in Zhangjiang Estuary","volume":"14","author":"Dong","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining earliest identifiable timing of crops using all available Sentinel 1\/2 imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery","volume":"13","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"30 m annual land cover and its dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_45","unstructured":"Pointereau, P. (2008). Analysis of Farmland Abandonment and the Extent and Location of Agricultural Areas That Are Actually Abandoned or Are in Risk to be Abandoned, EUR-OP."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1007\/s10021-012-9558-7","article-title":"Rewilding Abandoned Landscapes in Europe","volume":"15","author":"Navarro","year":"2012","journal-title":"Ecosystems"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dasari, K., Anjaneyulu, L., Jayasri, P., and Prasad, A. (2015, January 18\u201320). Importance of Speckle Filtering in Image Classification of SAR Data. Proceedings of the 2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE), Bhubaneswar, India.","DOI":"10.1109\/ICMOCE.2015.7489764"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening Gamma: Radiometric Terrain Correction for SAR Imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.08.037","article-title":"Multi-model radiometric slope correction of SAR images of complex terrain using a two-stage semi-empirical approach","volume":"156","author":"Hoekman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.06.018","article-title":"An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine","volume":"178","author":"Ni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","first-page":"102351","article-title":"Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1\/2 images","volume":"101","author":"He","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_55","first-page":"102376","article-title":"Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine","volume":"102","author":"Pan","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"112364","DOI":"10.1016\/j.rse.2021.112364","article-title":"Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0","volume":"258","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-021-00827-9","article-title":"The 10-m crop type maps in Northeast China during 2017\u20132019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., and Saah, D. (2019). Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification. Remote Sens., 11.","DOI":"10.3390\/rs11070831"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., and Schaepman, M.E. (2017). Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_61","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":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","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_63","first-page":"102405","article-title":"Vegetable mapping using fuzzy classification of Dynamic Time Warping distances from time series of Sentinel-1A images","volume":"102","author":"Moola","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","first-page":"102720","article-title":"Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data","volume":"108","author":"Wang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., and Gong, P. (2021). Progress and Trends in the Application of Google Earth and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13183778"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1038\/s41597-020-00646-4","article-title":"Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive","volume":"7","author":"Wang","year":"2020","journal-title":"Sci. Data"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y. (2021). Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7\/8 Images, and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132510"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sazib, N., Mladenova, I., and Bolten, J. (2018). Leveraging the google earth engine for drought assessment using global soil moisture data. Remote Sens., 10.","DOI":"10.3390\/rs10081265"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huang, H., Li, Z., Hackman, K.O., Liu, C., Andriamiarisoa, R.L., Ny Aina Nomenjanahary Raherivelo, T., Li, Y., and Gong, P. (2020). Automatic high-resolution land cover production in Madagascar using sentinel-2 time series, tile-based image classification and google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12213663"},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_74","unstructured":"Powers, D.M. (arXiv, 2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation, arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/S0034-4257(02)00196-7","article-title":"Spectral discrimination of vegetation types in a coastal wetland","volume":"85","author":"Schmidt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/TBDATA.2019.2940237","article-title":"Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery","volume":"6","author":"Shelestov","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Zhang, B., and Liu, M. (2018). Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11010053"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Markert, A.M., Mayer, T., Nauman, C., Haag, A., Poortinga, A., Bhandari, B., Thwal, N.S., Kunlamai, T., and Chishtie, F. (2020). Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12152469"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3806\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:27Z","timestamp":1760141127000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,7]]},"references-count":78,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153806"],"URL":"https:\/\/doi.org\/10.3390\/rs14153806","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,7]]}}}