{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:46:54Z","timestamp":1774615614694,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"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":["42301456"],"award-info":[{"award-number":["42301456"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022NSFSC1040"],"award-info":[{"award-number":["2022NSFSC1040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SKLGP2022Z017"],"award-info":[{"award-number":["SKLGP2022Z017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Sichuan, China","award":["42301456"],"award-info":[{"award-number":["42301456"]}]},{"name":"Natural Science Foundation of Sichuan, China","award":["2022NSFSC1040"],"award-info":[{"award-number":["2022NSFSC1040"]}]},{"name":"Natural Science Foundation of Sichuan, China","award":["SKLGP2022Z017"],"award-info":[{"award-number":["SKLGP2022Z017"]}]},{"name":"the Independent Research Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["42301456"],"award-info":[{"award-number":["42301456"]}]},{"name":"the Independent Research Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["2022NSFSC1040"],"award-info":[{"award-number":["2022NSFSC1040"]}]},{"name":"the Independent Research Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["SKLGP2022Z017"],"award-info":[{"award-number":["SKLGP2022Z017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoig\u00ea Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g\/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g\/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning.<\/jats:p>","DOI":"10.3390\/rs16071117","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T10:03:59Z","timestamp":1711101839000},"page":"1117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoig\u00ea Plateau, China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1758-2989","authenticated-orcid":false,"given":"Zhifei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Li","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Zhengwei","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xueman","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1793-7106","authenticated-orcid":false,"given":"Linlong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Guichuan","family":"Kang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9629-475X","authenticated-orcid":false,"given":"Wenqian","family":"Bai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2923-4221","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yixian","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2016.08.001","article-title":"Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space","volume":"120","author":"Shoko","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108081","DOI":"10.1016\/j.ecolind.2021.108081","article-title":"The use of machine learning methods to estimate aboveground biomass of grasslands: A review","volume":"130","author":"Morais","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ecolind.2014.01.015","article-title":"An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province, China","volume":"40","author":"Fu","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_4","first-page":"33","article-title":"Aboveground biomass production and soil moisture characteristics of different herb communities in the Loess Hilly-gully Region","volume":"12","author":"Dongmei","year":"2014","journal-title":"Sci. Soil Water Conserv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1016\/j.foreco.2008.11.016","article-title":"Aboveground biomass assessment in Colombia: A remote sensing approach","volume":"257","author":"Anaya","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2015.12.002","article-title":"Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data","volume":"173","author":"Su","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124744","DOI":"10.1016\/j.jhydrol.2020.124744","article-title":"Spatiotemporal dynamics of soil moisture in the karst areas of China based on reanalysis and observations data","volume":"585","author":"Deng","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141525","DOI":"10.1016\/j.scitotenv.2020.141525","article-title":"Response of the weathering carbon sink in terrestrial rocks to climate variables and ecological restoration in China","volume":"750","author":"Gong","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shen, G., Yang, X., Jin, Y., Luo, S., Xu, B., and Zhou, Q. (2019). Land use changes in the Zoige Plateau based on the object-oriented method and their effects on landscape patterns. Remote Sens., 12.","DOI":"10.3390\/rs12010014"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jaridenv.2008.09.027","article-title":"Aboveground biomass in Tibetan grasslands","volume":"73","author":"Yang","year":"2009","journal-title":"J. Arid Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yin, G., Li, A., Wu, C., Wang, J., Xie, Q., Zhang, Z., Nan, X., Jin, H., Bian, J., and Lei, G. (2018). Seamless upscaling of the field-measured grassland aboveground biomass based on gaussian process regression and gap-filled landsat 8 OLI reflectance. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070242"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107450","DOI":"10.1016\/j.ecolind.2021.107450","article-title":"A method to avoid spatial overfitting in estimation of grassland above-ground biomass on the Tibetan Plateau","volume":"125","author":"Yu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154226","DOI":"10.1016\/j.scitotenv.2022.154226","article-title":"Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years","volume":"826","author":"Ge","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A review of radar remote sensing for biomass estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/JSTARS.2020.2999348","article-title":"Modeling alpine grassland above ground biomass based on remote sensing data and machine learning algorithm: A case study in east of the Tibetan Plateau, China","volume":"13","author":"Meng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.ecolmodel.2009.04.025","article-title":"A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China","volume":"220","author":"Xie","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1579\/0044-7447-32.8.502","article-title":"Monitoring change in mountainous dry-heath vegetation at a regional ScaleUsing multitemporal landsat TM data","volume":"32","author":"Nordberg","year":"2003","journal-title":"AMBIO J. Hum. Environ."},{"key":"ref_18","first-page":"184","article-title":"Estimating above-ground biomass on mountain meadows and pastures through remote sensing","volume":"38","author":"Barrachina","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.01.009","article-title":"Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels","volume":"160","author":"Chen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"159","article-title":"A radiative transfer model-based method for the estimation of grassland aboveground biomass","volume":"54","author":"Quan","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/jpe\/rtu002","article-title":"The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls","volume":"8","author":"Jiang","year":"2015","journal-title":"J. Plant Ecol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.ecolind.2015.11.005","article-title":"Modeling grassland aboveground biomass using a pure vegetation index","volume":"62","author":"Li","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.soilbio.2012.09.030","article-title":"midDRIFTS-based partial least square regression analysis allows predicting microbial biomass, enzyme activities and 16S rRNA gene abundance in soils of temperate grasslands","volume":"57","author":"Rasche","year":"2013","journal-title":"Soil Biol. Biochem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3204","DOI":"10.1080\/01431161.2018.1541110","article-title":"Mapping pasture biomass in Mongolia using partial least squares, random forest regression and Landsat 8 imagery","volume":"40","author":"Otgonbayar","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114020","DOI":"10.1088\/1748-9326\/ac2e85","article-title":"Estimating the grassland aboveground biomass in the Three-River Headwater Region of China using machine learning and Bayesian model averaging","volume":"16","author":"Zeng","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_26","first-page":"15","article-title":"Remote sensing retrieval of nature grassland biomass in Menyuan County, Qinghai Province experimental area based on Sentinel-2 data","volume":"32","author":"Guo","year":"2023","journal-title":"Acta Prataculturae Sin."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.rse.2017.10.011","article-title":"Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region","volume":"204","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vamvakoulas, C., Alexandris, S., and Argyrokastritis, I. (2020). Dry above ground biomass for a soybean crop using an empirical model in Greece. Energies, 13.","DOI":"10.3390\/en13010201"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.ecolind.2019.02.023","article-title":"Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm","volume":"102","author":"Zeng","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1146850","DOI":"10.3389\/fevo.2023.1146850","article-title":"Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China","volume":"11","author":"Liu","year":"2023","journal-title":"Front. Ecol. Evol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, L., Li, A., Yin, G., Nan, X., and Bian, J. (2019). Retrieval of grassland aboveground biomass through inversion of the PROSAIL model with MODIS imagery. Remote Sens., 11.","DOI":"10.3390\/rs11131597"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2018.09.028","article-title":"Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model","volume":"218","author":"Punalekar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2012.02.011","article-title":"Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model","volume":"121","author":"Si","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"12","article-title":"Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data","volume":"26","author":"Duan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","first-page":"102454","article-title":"Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression","volume":"102","author":"Adeluyi","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pampanoni, V., Laneve, G., and Santilli, G. (2022, January 17\u201322). Evaluating Sentinel-3 Viability for Vegetation Canopy Monitoring and Fuel Moisture Content Estimation. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884150"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.03.011","article-title":"Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response","volume":"148","author":"Casas","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.3390\/rs5073280","article-title":"Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model","volume":"5","author":"Rivera","year":"2013","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106304","DOI":"10.1016\/j.compag.2021.106304","article-title":"Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model","volume":"187","author":"Wan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiao, Q., Sun, Q., Zhang, B., Huang, W., Ye, H., Zhang, Z., Zhang, X., and Qian, B. (2021). A random forest algorithm for retrieving canopy chlorophyll content of wheat and soybean trained with PROSAIL simulations using adjusted average leaf angle. Remote Sens., 14.","DOI":"10.3390\/rs14010098"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/10106049.2020.1756461","article-title":"Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: A comparison of support vector machine and traditional regression models","volume":"37","author":"Deb","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2006.09.031","article-title":"Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer","volume":"107","author":"Durbha","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_45","first-page":"117","article-title":"Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat","volume":"29","author":"Liang","year":"2013","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_46","first-page":"102340","article-title":"Retrieval of betalain contents based on the coupling of radiative transfer model and SVM model","volume":"100","author":"Sawut","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hou, M., Ge, J., Gao, J., Meng, B., Li, Y., Yin, J., Liu, J., Feng, Q., and Liang, T. (2020). Ecological risk assessment and impact factor analysis of alpine wetland ecosystem based on LUCC and boosted regression tree on the Zoige Plateau, China. Remote Sens., 12.","DOI":"10.3390\/rs12030368"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10661-010-1781-0","article-title":"Changes in alpine wetland ecosystems of the Qinghai\u2013Tibetan plateau from 1967 to 2004","volume":"180","author":"Zhang","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s12524-011-0129-8","article-title":"Inversion of PROSAIL model for retrieval of plant biophysical parameters","volume":"40","author":"Tripathi","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/JSTARS.2012.2186118","article-title":"Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach","volume":"5","author":"Darvishzadeh","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","unstructured":"Kuusk, A. (1991). Photon-Vegetation Interactions: Applications in Optical Remote Sensing and Plant Ecology, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.matcom.2009.09.005","article-title":"Monte Carlo algorithms for evaluating Sobol\u2019sensitivity indices","volume":"81","author":"Dimov","year":"2010","journal-title":"Math. Comput. Simul."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2019.01.039","article-title":"Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach","volume":"224","author":"Xu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ecolind.2018.01.012","article-title":"Are remotely sensed traits suitable for ecological analysis? A case study of long-term drought effects on leaf mass per area of wetland vegetation","volume":"88","author":"Feilhauer","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2018.10.008","article-title":"Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST\u2013PROSAIL model","volume":"102","author":"Huang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2017.02.002","article-title":"Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model","volume":"126","author":"Li","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"125033","DOI":"10.1016\/j.jhydrol.2020.125033","article-title":"Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)","volume":"588","author":"Panahi","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gao, H., and Zhang, X. (2023). Combining Radiative Transfer Model and Regression Algorithms for Estimating Aboveground Biomass of Grassland in West Ujimqin, China. Remote Sens., 15.","DOI":"10.3390\/rs15112918"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s11284-007-0423-7","article-title":"Small-scale species richness and its spatial variation in an alpine meadow on the Qinghai-Tibet Plateau","volume":"23","author":"Chen","year":"2008","journal-title":"Ecol. Res."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, A., and Bian, J. (2016). Simulation of the grazing effects on grassland aboveground net primary production using DNDC model combined with time-series remote sensing data\u2014A case study in Zoige Plateau, China. Remote Sens., 8.","DOI":"10.3390\/rs8030168"},{"key":"ref_65","first-page":"41","article-title":"High-resolution leaf area index inversion based on the Kernel Ridge Regression algorithm and PROSAIL model","volume":"31","author":"Guo","year":"2022","journal-title":"Acta Prataculturae Sin."},{"key":"ref_66","first-page":"2415","article-title":"Variations of forage yield and forage-livestock balance in grasslands over the Tibetan Pla-teau, China","volume":"32","author":"Mo","year":"2021","journal-title":"Ying Yong Sheng Tai Xue Bao = J. Appl. Ecol."},{"key":"ref_67","first-page":"1","article-title":"Generating spatiotemporally continuous grassland aboveground biomass on the tibetan plateau through PROSAIL model inversion on google earth engine","volume":"60","author":"Xie","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.rse.2008.11.007","article-title":"The ASTER spectral library version 2.0","volume":"113","author":"Baldridge","year":"2009","journal-title":"Remote Sens. 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