{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:16:10Z","timestamp":1781136970795,"version":"3.54.1"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["32160369"],"award-info":[{"award-number":["32160369"]}]},{"name":"the National Natural Science Foundation of China","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"the National Natural Science Foundation of China","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"the National Natural Science Foundation of China","award":["32060320"],"award-info":[{"award-number":["32060320"]}]},{"name":"the National Natural Science Foundation of China","award":["202202AD080010"],"award-info":[{"award-number":["202202AD080010"]}]},{"name":"the National Natural Science Foundation of China","award":["202101AT070039"],"award-info":[{"award-number":["202101AT070039"]}]},{"name":"the National Natural Science Foundation of China","award":["202101BD070001-066"],"award-info":[{"award-number":["202101BD070001-066"]}]},{"name":"the National Natural Science Foundation of China","award":["YNWR-QNBJ-2020047"],"award-info":[{"award-number":["YNWR-QNBJ-2020047"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["32160369"],"award-info":[{"award-number":["32160369"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["32060320"],"award-info":[{"award-number":["32060320"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["202202AD080010"],"award-info":[{"award-number":["202202AD080010"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["202101AT070039"],"award-info":[{"award-number":["202101AT070039"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["202101BD070001-066"],"award-info":[{"award-number":["202101BD070001-066"]}]},{"name":"Key Development and Promotion Project of Yunnan Province","award":["YNWR-QNBJ-2020047"],"award-info":[{"award-number":["YNWR-QNBJ-2020047"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["32160369"],"award-info":[{"award-number":["32160369"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["32060320"],"award-info":[{"award-number":["32060320"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["202202AD080010"],"award-info":[{"award-number":["202202AD080010"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["202101AT070039"],"award-info":[{"award-number":["202101AT070039"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["202101BD070001-066"],"award-info":[{"award-number":["202101BD070001-066"]}]},{"name":"Research Foundation for Basic Research of Yunnan Province","award":["YNWR-QNBJ-2020047"],"award-info":[{"award-number":["YNWR-QNBJ-2020047"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["32160369"],"award-info":[{"award-number":["32160369"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["32060320"],"award-info":[{"award-number":["32060320"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["202202AD080010"],"award-info":[{"award-number":["202202AD080010"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["202101AT070039"],"award-info":[{"award-number":["202101AT070039"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["202101BD070001-066"],"award-info":[{"award-number":["202101BD070001-066"]}]},{"name":"Joint Special Project for Agriculture of Yunnan Province, China","award":["YNWR-QNBJ-2020047"],"award-info":[{"award-number":["YNWR-QNBJ-2020047"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["32160369"],"award-info":[{"award-number":["32160369"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["31860182"],"award-info":[{"award-number":["31860182"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["41961053"],"award-info":[{"award-number":["41961053"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["32060320"],"award-info":[{"award-number":["32060320"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["202202AD080010"],"award-info":[{"award-number":["202202AD080010"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["202101AT070039"],"award-info":[{"award-number":["202101AT070039"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["202101BD070001-066"],"award-info":[{"award-number":["202101BD070001-066"]}]},{"name":"\u201cTen Thousand Talents Program\u201d Special Project for Young Top-notch Talents of Yunnan Province","award":["YNWR-QNBJ-2020047"],"award-info":[{"award-number":["YNWR-QNBJ-2020047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to evaluate the potential of fusing freely available multi-modal data for accurately mapping tree species. Sentinel-2, Sentinel-1, and various environmental datasets over a large mountainous forest in Southwest China were obtained and analyzed using Google Earth Engine (GEE). Seven data cases considering the individual or joint performance of different features, and four additional cases considering a novel clustering-based feature selection method, were analyzed. All 11 cases were assessed using three machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting tree (XGBoost). The best performance, with an overall accuracy of 77.98%, was attained from the case with all features and the random forest classifier. Sentinel-2 data alone exhibited similar performance as environmental data in terms of overall accuracy. Similar species, such as oak and birch, cannot be spectrally discriminated based on Sentinel-2-based features alone. The addition of SAR features improved discrimination, especially when distinguishing between some coniferous and deciduous species, but also decreased accuracy for oak. The analysis based on different data cases and feature importance rankings indicated that environmental features are important. The random forest outperformed other models, and a better prediction was achieved for planted tree species compared to that for the natural forest. These results suggest that accurately mapping tree species over large mountainous areas is feasible with freely accessible multi-modal data, especially when considering environmental factors.<\/jats:p>","DOI":"10.3390\/rs15040979","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T05:51:06Z","timestamp":1676008266000},"page":"979","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Synergism of Multi-Modal Data for Mapping Tree Species Distribution\u2014A Case Study from a Mountainous Forest in Southwest China"],"prefix":"10.3390","volume":"15","author":[{"given":"Pengfei","family":"Zheng","sequence":"first","affiliation":[{"name":"Faculty of Forestry, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panfei","family":"Fang","sequence":"additional","affiliation":[{"name":"Faculty of Forestry, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2962-1508","authenticated-orcid":false,"given":"Leiguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650024, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-6690","authenticated-orcid":false,"given":"Guanglong","family":"Ou","sequence":"additional","affiliation":[{"name":"Faculty of Forestry, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-4931","authenticated-orcid":false,"given":"Weiheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650024, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Dai","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinling","family":"Dai","sequence":"additional","affiliation":[{"name":"Art and Design College, Southwest Forestry University, Kunming 650024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1111\/cobi.13408","article-title":"Need for a global map of forest naturalness for a sustainable future","volume":"34","author":"Chiarucci","year":"2020","journal-title":"Conserv. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1146\/annurev-ecolsys-121415-032359","article-title":"Forests, climate, and public policy: A 500-year interdisciplinary odyssey","volume":"47","author":"Bonan","year":"2016","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Graves, S., Asner, G., Martin, R., Anderson, C., Colgan, M., Kalantari, L., and Bohlman, S. (2016). Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data. Remote Sens., 8.","DOI":"10.3390\/rs8020161"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/10106049.2014.885589","article-title":"Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland","volume":"30","author":"Adelabu","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree species classification with random forest using very high spatial resolution 8-band worldview-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9812624","DOI":"10.34133\/2021\/9812624","article-title":"Mapping tree species using advanced remote sensing technologies: A state-of-the-art review and perspective","volume":"2021","author":"Pu","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ho\u015bci\u0142o, A., and Lewandowska, A. (2019). Mapping forest type and tree species on a regional scale using multi-temporal sentinel-2 data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_11","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 2017-2019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112320","DOI":"10.1016\/j.rse.2021.112320","article-title":"Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series","volume":"256","author":"Sun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1080\/10106049.2020.1768593","article-title":"A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: A machine learning approach","volume":"37","author":"Htitiou","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112743","DOI":"10.1016\/j.rse.2021.112743","article-title":"Mapping temperate forest tree species using dense Sentinel-2 time series","volume":"267","author":"Hemmerling","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"202","article-title":"Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data","volume":"57","author":"Vuolo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M., and Small, D. (2017). Using multitemporal sentinel-1 C-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern Switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tomppo, E., Antropov, O., and Praks, J. (2019). Boreal forest snow damage mapping using multi-temporal sentinel-1 data. Remote Sens., 11.","DOI":"10.3390\/rs11040384"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nasirzadehdizaji, R., Balik Sanli, F., Abdikan, S., Cakir, Z., Sekertekin, A., and Ustuner, M. (2019). Sensitivity analysis of multi-temporal sentinel-1 SAR parameters to crop height and canopy coverage. Appl. Sci., 9.","DOI":"10.3390\/app9040655"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, Q., Wang, L., Huang, J., Lu, L., Li, Y., Du, Y., and Ling, F. (2022). Mapping plant diversity based on combined SENTINEL-1\/2 Data\u2014Opportunities for subtropical mountainous forests. Remote Sens., 14.","DOI":"10.3390\/rs14030492"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lechner, M., Dost\u00e1lov\u00e1, A., Hollaus, M., Atzberger, C., and Immitzer, M. (2022). Combination of sentinel-1 and sentinel-2 data for tree species classification in a central European biosphere reserve. Remote Sens., 14.","DOI":"10.3390\/rs14112687"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1080\/01431161.2018.1479788","article-title":"Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification","volume":"39","author":"Wagner","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chiang, S.-H., and Valdez, M. (2019). Tree species classification by integrating satellite imagery and topographic variables using maximum entropy method in a Mongolian forest. Forests, 10.","DOI":"10.3390\/f10110961"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11258-014-0316-0","article-title":"The relationship between climatic conditions and generative reproduction of a lowland population of Pulsatilla vernalis: The last breath of a relict plant or a fluctuating cycle of regeneration?","volume":"215","author":"Grzyl","year":"2014","journal-title":"Plant Ecol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/j.1461-0248.2007.01150.x","article-title":"Prediction of plant species distributions across six millennia","volume":"11","author":"Pearman","year":"2008","journal-title":"Ecol. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1017\/S026646741400025X","article-title":"Rainfall and temperature affect tree species distribution in Ghana","volume":"30","author":"Amissah","year":"2014","journal-title":"J. Trop. Ecol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1111\/geb.12426","article-title":"Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics","volume":"25","author":"Deblauwe","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1111\/j.1365-2435.2007.01374.x","article-title":"The role of desiccation tolerance in determining tree species distributions along the Malay\u2013Thai Peninsula","volume":"22","author":"Baltzer","year":"2008","journal-title":"Funct. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113103","DOI":"10.1016\/j.rse.2022.113103","article-title":"Improving the characterization of global aquatic land cover types using multi-source earth observation data","volume":"278","author":"Xu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"102861","article-title":"Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods","volume":"111","author":"Zhou","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ins.2010.10.016","article-title":"Fuzzy clustering algorithms for unsupervised change detection in remote sensing images","volume":"181","author":"Ghosh","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.patrec.2013.10.017","article-title":"Pattern classification and clustering: A review of partially supervised learning approaches","volume":"37","author":"Schwenker","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lim, J., Kim, K.-M., and Jin, R. (2019). Tree species classification using Hyperion and sentinel-2 data with machine learning in South Korea and China. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030150"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7589","DOI":"10.1109\/JSTARS.2021.3098817","article-title":"Exploitation of time series sentinel-2 data and different machine learning algorithms for detailed tree species classification","volume":"14","author":"Xi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Hua, Z. (2013). The floras of southern and tropical southeastern Yunnan have been shaped by divergent geological histories. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0064213"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of phenological research using sentinel-2 data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_43","first-page":"36","article-title":"A proposal of the Temporal Window Operation (TWO) method to remove high-frequency noises in AVHRR NDVI time series data","volume":"38","author":"Park","year":"1999","journal-title":"J. Jpn. Soc. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MSP.2011.941097","article-title":"What is a Savitzky-Golay filter? [lecture notes]","volume":"28","author":"Schafer","year":"2011","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2021.03.004","article-title":"Mapping crop types in complex farming areas using SAR imagery with dynamic time warping","volume":"175","author":"Gella","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106753","DOI":"10.1016\/j.compag.2022.106753","article-title":"A Machine Learning approach to reconstruct cloudy affected vegetation indices imagery via data fusion from Sentinel-1 and Landsat 8","volume":"194","author":"Dias","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sarzynski, T., Giam, X., Carrasco, L., and Lee, J.S.H. (2020). Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12071220"},{"key":"ref_48","unstructured":"Wan, Z., Hook, S., and Hulley, G. (2015). NASA LP DAAC."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.5194\/essd-11-1583-2019","article-title":"SM2RAIN\u2013ASCAT (2007\u20132018): Global daily satellite rainfall data from ASCAT soil moisture observations","volume":"11","author":"Brocca","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"112103","DOI":"10.1016\/j.rse.2020.112103","article-title":"Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians","volume":"251","author":"Grabska","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.mcm.2011.10.045","article-title":"Object-oriented feature selection of high spatial resolution images using an improved Relief algorithm","volume":"58","author":"Jia","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, M., Yang, F., Yang, C., Yang, P., Gao, Y., Shang, Y., and Peng, D. (2022). Forest height mapping using feature selection and machine learning by integrating multi-source satellite data in Baoding City, North China. Remote Sens., 14.","DOI":"10.3390\/rs14184434"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3136625","article-title":"Feature selection","volume":"50","author":"Li","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.strusafe.2003.05.002","article-title":"An examination of methods for approximating implicit limit state functions from the viewpoint of statistical learning theory","volume":"26","author":"Hurtado","year":"2004","journal-title":"Struct. Saf."},{"key":"ref_56","first-page":"44","article-title":"Tree species identification using XGBoost based on GF-2 images","volume":"5","author":"Cai","year":"2019","journal-title":"For. Resour. Wanagement"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree species classification with multi-temporal sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Xie, B., Cao, C., Xu, M., Duerler, R.S., Yang, X., Bashir, B., Chen, Y., and Wang, K. (2021). Analysis of regional distribution of tree species using multi-seasonal sentinel-1&2 imagery within google earth engine. Forests, 12.","DOI":"10.21203\/rs.3.rs-245409\/v1"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ma, M., Liu, J., Liu, M., Zeng, J., and Li, Y. (2021). Tree species classification based on sentinel-2 imagery and random forest classifier in the eastern regions of the Qilian mountains. Forests, 12.","DOI":"10.3390\/f12121736"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lim, J., Kim, K.-M., Kim, E.-H., and Jin, R. (2020). Machine learning for tree species classification using sentinel-2 spectral information, crown texture, and environmental variables. Remote Sens., 12.","DOI":"10.3390\/rs12122049"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2003.814132","article-title":"High-resolution measurements of scattering in wheat canopies-implications for crop parameter retrieval","volume":"41","author":"Brown","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wang, M., Li, M., Wang, F., and Ji, X. (2022). Exploring the optimal feature combination of tree species classification by fusing multi-feature and multi-temporal sentinel-2 data in Changbai mountain. Forests, 13.","DOI":"10.3390\/f13071058"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10342-011-0513-5","article-title":"Statistical mapping of tree species over Europe","volume":"131","author":"Brus","year":"2011","journal-title":"Eur. J. For. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1111\/j.1744-7429.2010.00711.x","article-title":"Patterns and determinants of floristic variation across lowland forests of Bolivia","volume":"43","author":"Toledo","year":"2011","journal-title":"Biotropica"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/979\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:30:02Z","timestamp":1760121002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":66,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040979"],"URL":"https:\/\/doi.org\/10.3390\/rs15040979","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,10]]}}}