{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:08:16Z","timestamp":1770883696882,"version":"3.50.1"},"reference-count":90,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"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":["51809250"],"award-info":[{"award-number":["51809250"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Provincial Natural Science Foundation for Innovation Groups","award":["2019CFA019"],"award-info":[{"award-number":["2019CFA019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m \u00d7 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m \u00d7 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with \u02132 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity\u2019s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.<\/jats:p>","DOI":"10.3390\/rs14030492","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T22:51:06Z","timestamp":1642719066000},"page":"492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mapping Plant Diversity Based on Combined SENTINEL-1\/2 Data\u2014Opportunities for Subtropical Mountainous Forests"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2292-619X","authenticated-orcid":false,"given":"Qichi","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Lihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Jinliang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"given":"Lijie","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430072, China"}]},{"given":"Yun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-4897","authenticated-orcid":false,"given":"Feng","family":"Ling","sequence":"additional","affiliation":[{"name":"Key Laboratory for Environment and Disaster Monitoring and Evaluation, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ecolind.2015.12.026","article-title":"Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data","volume":"64","author":"Heiskanen","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lausch, A., Erasmi, S., King, D., Magdon, P., and Heurich, M. (2016). Understanding Forest Health with Remote Sensing\u2014Part I\u2014A Review of Spectral Traits, Processes and Remote-Sensing Characteristics. Remote Sens., 8.","DOI":"10.3390\/rs8121029"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2018.05.014","article-title":"Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China","volume":"213","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.ecoser.2014.05.006","article-title":"Linkages between biodiversity attributes and ecosystem services: A systematic review","volume":"9","author":"Harrison","year":"2014","journal-title":"Ecosyst. Serv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ecoinf.2019.04.001","article-title":"Estimating tree species diversity from space in an alpine conifer forest: The Rao\u2019s Q diversity index meets the spectral variation hypothesis","volume":"52","author":"Torresani","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2017.12.014","article-title":"Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of \u03b1-diversity (species richness) in prairie ecosystems","volume":"206","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"43","article-title":"How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring","volume":"10","author":"Vihervaara","year":"2017","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fauvel, M., Lopes, M., Dubo, T., Rivers-Moore, J., Frison, P.-L., Gross, N., and Ouin, A. (2020). Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series. Remote Sens. Environ., 237.","DOI":"10.1016\/j.rse.2019.111536"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2020.10.018","article-title":"From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2","volume":"171","author":"Hamrouni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s13595-014-0446-5","article-title":"Climate change impacts and adaptation in forest management: A review","volume":"72","author":"Keenan","year":"2015","journal-title":"Ann. For. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.rse.2015.12.019","article-title":"Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest","volume":"174","author":"Wallis","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"e01418","article-title":"How evergreen and deciduous trees coexist during secondary forest succession: Insights into forest restoration mechanisms in Chinese subtropical forest","volume":"25","author":"Zhang","year":"2021","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111262","DOI":"10.1016\/j.rse.2019.111262","article-title":"Characterizing global forest canopy cover distribution using spaceborne lidar","volume":"231","author":"Tang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.isprsjprs.2017.10.008","article-title":"Remote sensing of species diversity using Landsat 8 spectral variables","volume":"133","author":"Madonsela","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1007\/s10661-017-6295-6","article-title":"Can tree species diversity be assessed with Landsat data in a temperate forest?","volume":"189","author":"Arekhi","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111626","DOI":"10.1016\/j.rse.2019.111626","article-title":"Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models","volume":"239","author":"Randin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/S0169-5347(01)02283-2","article-title":"Vive la diff\u00e9rence: Plant functional diversity matters to ecosystem processes","volume":"16","author":"Cabido","year":"2001","journal-title":"Trends Ecol. Evol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125318","DOI":"10.1016\/j.jhydrol.2020.125318","article-title":"Soil, biochar, and nitrogen loss to runoff from loess soil amended with biochar under simulated rainfall","volume":"591","author":"Li","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"966","DOI":"10.3732\/ajb.1700061","article-title":"Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales","volume":"104","author":"Gamon","year":"2017","journal-title":"Am. J. Bot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.ecoinf.2010.06.001","article-title":"Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges","volume":"5","author":"Rocchini","year":"2010","journal-title":"Ecol. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2015.09.016","article-title":"Estimating and mapping forest structural diversity using airborne laser scanning data","volume":"170","author":"Mura","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ecoinf.2014.08.006","article-title":"The relationship between the spectral diversity of satellite imagery, habitat heterogeneity, and plant species richness","volume":"24","author":"Warren","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/rse2.9","article-title":"Satellite remote sensing to monitor species diversity: Potential and pitfalls","volume":"2","author":"Rocchini","year":"2015","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.3390\/rs70302692","article-title":"Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile","volume":"7","author":"Ceballos","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1126\/science.1256014","article-title":"Sensing biodiversity","volume":"346","author":"Turner","year":"2014","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/rse2.137","article-title":"UAV-derived estimates of forest structure to inform ponderosa pine forest restoration","volume":"6","author":"Belmonte","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.3390\/rs71013895","article-title":"Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure","volume":"7","author":"Dandois","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1111\/2041-210X.12219","article-title":"Applications of airborne lidar for the assessment of animal species diversity","volume":"5","author":"Simonson","year":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Carrasco, L., Giam, X., Pape\u015f, M., and Sheldon, K. (2019). Metrics of Lidar-Derived 3D Vegetation Structure Reveal Contrasting Effects of Horizontal and Vertical Forest Heterogeneity on Bird Species Richness. Remote Sens., 11.","DOI":"10.3390\/rs11070743"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.3390\/rs4092818","article-title":"Modelling Forest \u03b1-Diversity and Floristic Composition\u2014On the Added Value of LiDAR plus Hyperspectral Remote Sensing","volume":"4","author":"Leutner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.02.018","article-title":"Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine","volume":"175","author":"Adrian","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112505","DOI":"10.1016\/j.rse.2021.112505","article-title":"Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape","volume":"262","author":"Hauser","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111670","DOI":"10.1016\/j.rse.2020.111670","article-title":"Vegetation cover dependence on accumulated antecedent precipitation in Australia: Relationships with photosynthetic and non-photosynthetic vegetation fractions","volume":"240","author":"Guerschman","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.09.019","article-title":"Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China","volume":"218","author":"Ge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2007.03.018","article-title":"Effects of spatial and spectral resolution in estimating ecosystem \u03b1-diversity by satellite imagery","volume":"111","author":"Rocchini","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111218","DOI":"10.1016\/j.rse.2019.111218","article-title":"Remote sensing of terrestrial plant biodiversity","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wyniawskyj, N.S., Napiorkowska, M., Petit, D., Podder, P., and Marti, P. (August, January 28). Forest Monitoring in Guatemala Using Satellite Imagery and Deep Learning. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899782"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shin, P., Sankey, T., Moore, M., and Thode, A. (2018). Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand. Remote Sens., 10.","DOI":"10.3390\/rs10081266"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., and Yang, X. (2017). Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_42","unstructured":"European Space Agency (E.S.A.) (2021, May 15). Copernicus Missions. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-1."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.01.018","article-title":"Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities","volume":"223","author":"Rapinel","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"126","article-title":"Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems","volume":"66","author":"Mura","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Van Passel, J., De Keersmaecker, W., and Somers, B. (2020). Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using Sentinel-2 Satellite Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12081276"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.isprsjprs.2021.08.017","article-title":"Mapping dominant leaf type based on combined Sentinel-1\/-2 data\u2014Challenges for mountainous countries","volume":"180","author":"Waser","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"112456","DOI":"10.1016\/j.rse.2021.112456","article-title":"Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe","volume":"260","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111496","DOI":"10.1016\/j.rse.2019.111496","article-title":"Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets\u2014A case study","volume":"236","author":"Forkuor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z. (2019). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sens., 11.","DOI":"10.3390\/rs11040414"},{"key":"ref_53","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_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"112822","DOI":"10.1016\/j.rse.2021.112822","article-title":"Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning","volume":"269","author":"Zhao","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/nclimate2919","article-title":"Combining satellite data for better tropical forest monitoring","volume":"6","author":"Reiche","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/rse2.139","article-title":"Combining optical and radar satellite image time series to map natural vegetation: Savannas as an example","volume":"6","author":"Lopes","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"107408","DOI":"10.1016\/j.dib.2021.107408","article-title":"Estimating crop parameters using Sentinel-1 and 2 datasets and geospatial field data","volume":"38","author":"Mercier","year":"2021","journal-title":"Data Brief."},{"key":"ref_60","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 J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2020.11.023","article-title":"Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery","volume":"172","author":"Bhattarai","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yang, Q., Zhang, H., Wang, L., Ling, F., Wang, Z., Li, T., and Huang, J. (2021). Topography and soil content contribute to plant community composition and structure in subtropical evergreen-deciduous broadleaved mixed forests. Plant Divers.","DOI":"10.1016\/j.pld.2021.03.003"},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1111\/geb.12161","article-title":"Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation","volume":"23","author":"Pinaud","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_66","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_67","first-page":"464","article-title":"Spatial scale analysis of the species diversity and distribution of rare and endangered plants in northwest Hubei, China","volume":"37","author":"Yang","year":"2019","journal-title":"Plant Sci. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4261","DOI":"10.1016\/j.rse.2008.07.007","article-title":"Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China","volume":"112","author":"Zeng","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.3390\/rs70302668","article-title":"A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VEN\u03bcS and Sentinel-2 Images","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/36.964973","article-title":"Filtering of Multichannel SAR Images","volume":"39","author":"Quegan","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","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":"Matton","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-based modeling of gross primary production in an evergreen needleleaf forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2012.01.019","article-title":"Analysis of sapling density regeneration in Yellowstone National Park with hyperspectral remote sensing data","volume":"121","author":"Potter","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2016.06.016","article-title":"Spectral considerations for modeling yield of canola","volume":"184","author":"Sulik","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.rse.2012.02.013","article-title":"Relationship between floristic similarity and vegetated land surface phenology: Implications for the synoptic monitoring of species diversity at broad geographic regions","volume":"121","author":"Tuanmu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_78","unstructured":"J\u00f8rgensen, S.E., and Fath, B.D. (2008). Simpson Index. Encyclopedia of Ecology, Academic Press."},{"key":"ref_79","unstructured":"J\u00f8rgensen, S.E., and Fath, B.D. (2008). Shannon\u2013Wiener Index. Encyclopedia of Ecology, Academic Press."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.conbuildmat.2018.09.017","article-title":"Use of random forests regression for predicting IRI of asphalt pavements","volume":"189","author":"Gong","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.ecolind.2009.07.012","article-title":"Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?","volume":"10","author":"Oldeland","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1080\/17445647.2017.1372316","article-title":"Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia","volume":"13","author":"Clerici","year":"2017","journal-title":"J. Maps"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/JSTARS.2015.2503773","article-title":"Observations of Cutting Practices in Agricultural Grasslands Using Polarimetric SAR","volume":"9","author":"Voormansik","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2017.04.016","article-title":"Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species","volume":"129","author":"Shoko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Maltby, E., and Barker, T. (2009). The Wetlands Handbook, Wiley-Blackwell.","DOI":"10.1002\/9781444315813"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2018.10.037","article-title":"Detecting prairie biodiversity with airborne remote sensing","volume":"221","author":"Gholizadeh","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1080\/13658816.2017.1346255","article-title":"Estimating the prediction performance of spatial models via spatial k-fold cross validation","volume":"31","author":"Pohjankukka","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_88","first-page":"218","article-title":"Assessing floristic composition with multispectral sensors\u2014A comparison based on monotemporal and multiseasonal field spectra","volume":"21","author":"Feilhauer","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Lopes, M., Fauvel, M., Ouin, A., and Girard, S. (2017). Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation. Remote Sens., 9.","DOI":"10.3390\/rs9100993"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/rse2.173","article-title":"Do acoustically detectable species reflect overall diversity? A case study from Australia\u2019s arid zone","volume":"6","author":"Smith","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:59Z","timestamp":1760133899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":90,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030492"],"URL":"https:\/\/doi.org\/10.3390\/rs14030492","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,20]]}}}