{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:22:31Z","timestamp":1777389751388,"version":"3.51.4"},"reference-count":72,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007301","name":"Basic Research Project of Yunnan Province","doi-asserted-by":"publisher","award":["202101AU070161"],"award-info":[{"award-number":["202101AU070161"]}],"id":[{"id":"10.13039\/501100007301","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007301","name":"Basic Research Project of Yunnan Province","doi-asserted-by":"publisher","award":["XDA26050301-01"],"award-info":[{"award-number":["XDA26050301-01"]}],"id":[{"id":"10.13039\/501100007301","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Strategic Priority Research Program of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["202101AU070161"],"award-info":[{"award-number":["202101AU070161"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Strategic Priority Research Program of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDA26050301-01"],"award-info":[{"award-number":["XDA26050301-01"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regional scale identification. In this paper, we proposed a classification method combines multi-source phenology characteristics and random forest algorithm. On the basis of optimization of input samples and features, phenological spectrum, brightness, greenness, wetness, fractional vegetation cover, topography and other features of rubber were extracted. Five classification schemes were constructed for comparison, and the one with the highest classification accuracy was used to identify the spatial pattern of rubber plantations in 2014, 2016, 2018 and 2020 in Xishuangbanna. The results show that: (1) the identification results are in consistent with field survey and rubber plantations area generally shows a first increasing and then decreasing trend; (2) the Overall Accuracy (OA) and Kappa coefficient of the proposed method are 90.0% and 0.86, respectively, with a Producer\u2019s Accuracy (PA) and User\u2019s Accuracy (UA) of 95.2% and 88.8%, respectively; (3) cross-validation was employed to analyze the accuracy evaluation indexes of the identification results: both PA and UA of the rubber plantations stay stable over 85%, with the minimum fluctuation and best stability of UA value. The OA value and Kappa coefficient were stable in the range of 0.88\u20130.90 and 0.84\u20130.86, respectively. The method proposed provides reliable results on spatial distribution of rubber, and is potentially transferable to other mountainous areas as a robust approach for rapid monitoring of rubber plantations.<\/jats:p>","DOI":"10.3390\/rs15051228","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T04:32:33Z","timestamp":1677126753000},"page":"1228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5866-6321","authenticated-orcid":false,"given":"Guokun","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Key Laboratory of Plateau Remote Sensing, Yunnan Provincial Department of Education, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingke","family":"Wen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geomatics (NCG), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxin","family":"Feng","sequence":"additional","affiliation":[{"name":"Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","first-page":"174","article-title":"Remote sensing image extraction for rubber forest distribution in the border regions of China, Laos and Myanmar based on Google Earth Engine platform","volume":"36","author":"Li","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1016\/j.foreco.2007.12.038","article-title":"Carbon stock in rubber tree plantations in Western Ghana and Mato Grosso (Brazil)","volume":"255","author":"Wauters","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.apgeog.2011.06.018","article-title":"Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data","volume":"32","author":"Li","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.gloenvcha.2015.06.002","article-title":"Current trends of rubber plantation expansion may threaten biodiversity and livelihoods","volume":"34","author":"Ahrends","year":"2015","journal-title":"Glob. Environ. Chang. Hum. Policy Dimens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1111\/j.1755-263X.2008.00011.x","article-title":"Is oil palm agriculture really destroying tropical biodiversity?","volume":"1","author":"Koh","year":"2008","journal-title":"Conserv. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1126\/science.1173833","article-title":"The rubber juggernaut","volume":"324","author":"Ziegler","year":"2009","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s10531-006-9052-7","article-title":"Demand for rubber is causing the loss of high diversity rain forest in SW China","volume":"16","author":"Li","year":"2007","journal-title":"Biodivers. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"573","DOI":"10.4236\/ijg.2011.24060","article-title":"Rubber tree distribution mapping in Northeast Thailand","volume":"2","author":"Li","year":"2011","journal-title":"Int. J. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/03066150.2012.750605","article-title":"Expansion of rubber (Hevea brasiliensis) in Mainland Southeast Asia: What are the prospects for smallholders?","volume":"40","author":"Fox","year":"2013","journal-title":"J. Peasant Stud."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.ecolind.2012.08.023","article-title":"Landscape transformation through the use of ecological and socioeconomic indicators in Xishuangbanna, Southwest China, Mekong Region","volume":"36","author":"Xu","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1007\/s10113-019-01509-4","article-title":"After the rubber boom: Good news and bad news for biodiversity in Xishuangbanna, Yunnan, China","volume":"19","author":"Zhang","year":"2019","journal-title":"Reg. Environ. Chang."},{"key":"ref_12","first-page":"1","article-title":"Forest vegetation of Xishuangbanna, south China","volume":"8","author":"Zhu","year":"2006","journal-title":"For. Stud. China"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1915183","DOI":"10.1080\/20964129.2021.1915183","article-title":"Impact of land use and land cover changes on carbon storage in rubber dominated tropical Xishuangbanna, South West China","volume":"7","author":"Sarathchandra","year":"2021","journal-title":"Ecosyst. Health Sustain."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.5751\/ES-01413-100207","article-title":"Integrating sacred knowledge for conservation: Cultures and landscapes in southwest China","volume":"10","author":"Xu","year":"2005","journal-title":"Ecol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1002\/ldr.974","article-title":"Agrobiodiversity loss and livelihood vulnerability as a consequence of converting from subsistence farming systems to commercial plantation-dominated systems in Xishuangbanna, Yunnan, China: A household level analysis","volume":"21","author":"Fu","year":"2010","journal-title":"Land Degrad. Dev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2015.04.008","article-title":"Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery","volume":"105","author":"Qin","year":"2015","journal-title":"ISPRS\u2014J. Photogramm. Remote Sens."},{"key":"ref_17","unstructured":"Zhao, Y.S. (2013). Principle and Method of Remote Sensing Application Analysis, Science Press."},{"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":"1048","DOI":"10.3390\/rs70101048","article-title":"Mapping deciduous rubber plantation areas and stand ages with PALSAR and Landsat images","volume":"7","author":"Kou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5181","DOI":"10.5194\/bg-11-5181-2014","article-title":"Land surface phenological response to decadal climate variability across Australia using satellite remote sensing","volume":"11","author":"Broich","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ren, S.L., Yi, S.H., Peichl, M., and Wang, X.Y. (2017). Diverse responses of vegetation phenology to climate change in different grasslands in Inner Mongolia during 2000\u20132016. Remote Sens., 10.","DOI":"10.3390\/rs10010017"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/2150704X.2014.996678","article-title":"Mapping rubber tree plantations using a Landsat-based phenological algorithm in Xishuangbanna, southwest China","volume":"6","author":"Li","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1080\/01431160119220","article-title":"Mapping land use\/cover distribution on a mountainous tropical island using remote sensing and GIS","volume":"22","author":"Baban","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, F., Li, Z.F., Zhang, S.Y., Huang, N.T., Quan, Z.Y., Zhang, W.M., Liu, X.J., Jiang, X.S., Pan, J.J., and Prishchepov, A.V. (2020). Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China. Remote Sens., 12.","DOI":"10.3390\/rs12122065"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s11442-013-1060-4","article-title":"Rubber plantation and its relationship with topographical factors in the border region of China, Laos and Myanmar","volume":"23","author":"Liu","year":"2013","journal-title":"J. Geogr. Sci."},{"key":"ref_26","first-page":"1769","article-title":"Rubber plantations in Xishuangbanna: Remote sensing identification and digital mapping","volume":"34","author":"Liu","year":"2012","journal-title":"Resour. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jenvman.2011.10.011","article-title":"Rubber and pulp plantations represent a double threat to Hainan\u2019s natural tropical forests","volume":"96","author":"Zhai","year":"2012","journal-title":"J. Environ. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2012.07.004","article-title":"Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery","volume":"74","author":"Dong","year":"2012","journal-title":"ISPRS\u2014J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhai, D.L., Dong, J.W., Cadisch, G., Wang, M.C., Kou, W.L., Xu, J.C., Xiao, X.M., and Abbas, S. (2017). Comparison of pixel-and object-based approaches in phenology-based rubber plantation mapping in fragmented landscapes. Remote Sens., 10.","DOI":"10.3390\/rs10010044"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1080\/10106049.2019.1687592","article-title":"Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation\u2013moisture indices and meteorological data","volume":"36","author":"Xiao","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_31","first-page":"1181","article-title":"A primary study on rubber acreage estimation from MODIS-based information in Hainan","volume":"31","author":"Chen","year":"2010","journal-title":"Chin. J. Trop. Crops"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2012.08.022","article-title":"A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA","volume":"127","author":"Dong","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.3390\/rs5062795","article-title":"Mapping rubber plantations and natural forests in Xishuangbanna (Southwest China) using multi-spectral phenological metrics from MODIS time series","volume":"5","author":"Senf","year":"2013","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6041","DOI":"10.3390\/rs70506041","article-title":"Phenology-based vegetation index differencing for mapping of rubber plantations using Landsat OLI data","volume":"7","author":"Fan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","first-page":"117","article-title":"Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images","volume":"50","author":"Chen","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2017.04.003","article-title":"Obtaining rubber plantation age information from very dense Landsat TM & ETM+ time series data and pixel-based image compositing","volume":"196","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"40","article-title":"An updated delineation of stand ages of deciduous rubber plantations during 1987\u20132018 using Landsat-derived bi-temporal thresholds method in an anti-chronological strategy","volume":"76","author":"Xiao","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","first-page":"30","article-title":"Monitoring annual dynamics of mature rubber plantations in Xishuangbanna during 1987\u20132018 using Landsat time series data: A multiple normalization approach","volume":"77","author":"Xiao","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","first-page":"179","article-title":"Tea plantation identification using GF-1 and Sentinel-2 time series data","volume":"37","author":"Bai","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_40","first-page":"937","article-title":"Mapping Paddy Rice in the Hainan Province Using both Google Earth Engine and Remote Sensing Images","volume":"21","author":"Tan","year":"2019","journal-title":"Geo-Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2014.12.011","article-title":"Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A\/1B CCD images and recurrent neural network","volume":"102","author":"Chen","year":"2015","journal-title":"ISPRS\u2014J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/JSTARS.2010.2076398","article-title":"Large-area classification and mapping of forest and land cover in the Brazilian Amazon: A comparative analysis of ALOS\/PALSAR and Landsat data sources","volume":"3","author":"Walker","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2013.11.025","article-title":"Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data","volume":"155","author":"Lucas","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"299","DOI":"10.3390\/rs14020299","article-title":"Benefits of Google Earth Engine in remote sensing","volume":"26","author":"Wang","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_46","first-page":"170","article-title":"Area monitoring by remote sensing and spatiotemporal variation of rubber plantations in Xishuangbanna","volume":"30","author":"Liao","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_47","first-page":"1028","article-title":"Changes of rubber plantation aboveground biomass along elevation gradient in Xishuangbanna","volume":"25","author":"Xin","year":"2006","journal-title":"Chin. J. Ecol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.agrformet.2006.11.009","article-title":"Using stable isotopes to determine sources of fog drip in a tropical seasonal rain forest of Xishuangbanna, SW China","volume":"143","author":"Liu","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.indcrop.2014.08.001","article-title":"Greater diurnal temperature difference, an overlooked but important climatic driver of rubber yield","volume":"62","author":"Yu","year":"2014","journal-title":"Ind. Crop. Prod."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1111\/j.1744-7429.2006.00146.x","article-title":"Tropical forests of xishuangbanna, China","volume":"38","author":"Cao","year":"2006","journal-title":"Biotropica"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s00484-017-1448-4","article-title":"Responses of rubber leaf phenology to climatic variations in Southwest China","volume":"63","author":"Zhai","year":"2019","journal-title":"Int. J. Biometeorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.landusepol.2013.12.013","article-title":"Can carbon-trading schemes help to protect China\u2019s most diverse forest ecosystems? A case study from Xishuangbanna, Yunnan","volume":"38","author":"Yi","year":"2014","journal-title":"Land Use Pol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1111\/j.1744-7429.2006.00147.x","article-title":"Geological History, Flora, and Vegetation of Xishuangbanna, Southern Yunnan, China","volume":"38","author":"Zhu","year":"2006","journal-title":"Biotropica"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y.Q., 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_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":"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_57","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2021.06.005","article-title":"Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel","volume":"178","author":"Schulz","year":"2021","journal-title":"ISPRS\u2014J. Photogramm. Remote Sens."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2015.03.018","article-title":"Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS)","volume":"163","author":"Zhou","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_61","first-page":"1","article-title":"Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation","volume":"60","author":"Yan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","first-page":"101980","article-title":"Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification","volume":"85","author":"Macintyre","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","first-page":"41","article-title":"The tasselled cap\u2014A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat","volume":"159","author":"Kauth","year":"1976","journal-title":"LARS Symposia."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lamqadem, A.A., Saber, H., and Pradhan, B. (2018). Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques. Remote Sens., 10.","DOI":"10.3390\/rs10121862"},{"key":"ref_65","first-page":"687","article-title":"Orthogonal transformation of segmented images from the satellite Sentinel-2","volume":"70","author":"Nedkov","year":"2017","journal-title":"C. R. Acad. Bulg. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yuan, Y.X., Wen, Q.K., Zhao, X.L., Liu, S., Zhu, K.P., and Hu, B. (2022). Identifying Grassland Distribution in a Mountainous Region in Southwest China Using Multi-Source Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14061472"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, T., Tang, B.H., Huang, L., and Chen, G.K. (2022). Rice and Greenhouse Identification in Plateau Areas Incorporating Sentinel-1\/2 Optical and Radar Remote Sensing Data from Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14225727"},{"key":"ref_68","first-page":"1325","article-title":"Mapping the Spatial Distribution of Tea Plantations with 10 m Resolution in Fujian Province Using Google Earth Engine","volume":"23","author":"Li","year":"2021","journal-title":"Geo-Inf. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1080\/15481603.2019.1690780","article-title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","volume":"57","author":"Gumma","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"112670","DOI":"10.1016\/j.rse.2021.112670","article-title":"A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","volume":"266","author":"Li","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Gao, S.P., Liu, X.L., Bo, Y.C., Shi, Z.T., and Zhou, H.M. (2019). Rubber identification based on blended high spatio-temporal resolution optical remote sensing data: A case study in Xishuangbanna. Remote Sens., 11.","DOI":"10.3390\/rs11050496"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Yang, J.B., Xu, J.C., and Zhai, D.L. (2021). Integrating phenological and geographical information with artificial intelligence algorithm to map rubber plantations in Xishuangbanna. Remote Sens., 13.","DOI":"10.3390\/rs13142793"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:40:22Z","timestamp":1760121622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":72,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051228"],"URL":"https:\/\/doi.org\/10.3390\/rs15051228","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]}}}