{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:48:22Z","timestamp":1775486902254,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Second Tibetan Plateau Scientific Expedition and Research","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research","award":["32071550"],"award-info":[{"award-number":["32071550"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research","award":["31770480"],"award-info":[{"award-number":["31770480"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research","award":["BP0719040"],"award-info":[{"award-number":["BP0719040"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32071550"],"award-info":[{"award-number":["32071550"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31770480"],"award-info":[{"award-number":["31770480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BP0719040"],"award-info":[{"award-number":["BP0719040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the \u2018111\u2019 Programme","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"the \u2018111\u2019 Programme","award":["32071550"],"award-info":[{"award-number":["32071550"]}]},{"name":"the \u2018111\u2019 Programme","award":["31770480"],"award-info":[{"award-number":["31770480"]}]},{"name":"the \u2018111\u2019 Programme","award":["BP0719040"],"award-info":[{"award-number":["BP0719040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005\u20132015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R2vad = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)\/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R2indep = 0.50, RMSEindep = 1332.59 kg DW\/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models\u2019 predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW\/ha, indicating that the results were reliably accurate.<\/jats:p>","DOI":"10.3390\/rs14163843","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"3843","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?"],"prefix":"10.3390","volume":"14","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongzhu","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huzi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Laboratory for the Cognitive Control, Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN 47401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiangang","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiping","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-1653","authenticated-orcid":false,"given":"Ning","family":"Chai","sequence":"additional","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlong","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qisheng","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8588-8026","authenticated-orcid":false,"given":"Mengjing","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6389-8792","authenticated-orcid":false,"given":"Chenli","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Qinghai Provincial Natural Resources Survey and Monitoring, Xining 810000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjie","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of High Water Utilization on Dryland of Gansu Province, Institute of Dryland Farming, Gansu Academy of Agricultural Sciences, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Huang","sequence":"additional","affiliation":[{"name":"Animal Husbandry, Pasture and Green Agriculture Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5893-0604","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"},{"name":"NAU-MSU Asia Hub, Nanjing Agricultural University, Nanjing 210095, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101073","DOI":"10.1016\/j.ecoser.2020.101073","article-title":"Emergy-based ecosystem services valuation and classification management applied to China\u2019s grasslands","volume":"42","author":"Yang","year":"2020","journal-title":"Ecosyst. Serv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.agee.2015.10.009","article-title":"The effect of harvest, mulching and low-dose fertilization of liquid digestate on above ground biomass yield and diversity of lower mountain semi-natural grasslands","volume":"216","author":"Hensgen","year":"2016","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107215","DOI":"10.1016\/j.ecolind.2020.107215","article-title":"Remote sensing inversion of grassland aboveground bio-mass based on high accuracy surface modeling","volume":"121","author":"Zhou","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Z.B., Ma, Y.K., Zhang, Y.N., and Shang, J.L. (2022). Review of Remote Sensing Applications in Grassland Monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14122903"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","article-title":"The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields","volume":"199","author":"Guan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"404","DOI":"10.3390\/rs8050404","article-title":"Using NDVI and EVI to Map Spatiotemporal Variation in the Biomass and Quality of Forage for Migratory Elk in the Greater Yellowstone Ecosystem","volume":"8","author":"Erica","year":"2016","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00048-2","article-title":"A generalized soil-adjusted vegetation index","volume":"82","author":"Gilabert","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.rse.2018.02.068","article-title":"Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for above-ground living biomass estimation in arid grasslands","volume":"209","author":"Ren","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.rse.2009.01.006","article-title":"Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors","volume":"113","author":"Guerschman","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2015.04.021","article-title":"A scalable satellite-based crop yield mapper","volume":"164","author":"Lobell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"e01231","article-title":"Responses of palatable plants to climate and grazing in semi-arid grasslands of Mongolia","volume":"24","author":"Nakano","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143238","DOI":"10.1016\/j.scitotenv.2020.143238","article-title":"Climate regulates the functional traits-aboveground biomass relationships at a community-level in forests: A global meta-analysis","volume":"761","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2013A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"Isprs J. Photo-Grammetry Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, R., Zhao, Y.T., and Lin, H.L. (2021). Spatio-Temporal Variation Characteristics of Aboveground Biomass in the Headwater of the Yellow River Based on Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13173404"},{"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":"108081","DOI":"10.1016\/j.ecolind.2021.108081","article-title":"The use of machine learning methods to estimate above-ground biomass of grasslands: A review","volume":"130","author":"Morais","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1073\/pnas.1118438109","article-title":"Timing of climate variability and grassland productivity","volume":"109","author":"Craine","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4182","DOI":"10.1038\/s41598-017-04038-4","article-title":"Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI","volume":"7","author":"Liu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11442-014-1089-z","article-title":"Livestock-carrying capacity and overgrazing status of alpine grass-land in the Three-River Headwaters region, China","volume":"24","author":"Zhang","year":"2014","journal-title":"Geogr. Sci."},{"key":"ref_23","unstructured":"Hutchinson, M.F. (2004). ANUSPLIN Version 4. 3 User Guide, The Australia National University, Center for Re-source and Environment Studies. Available online: http:\/\/cres.anu.edu.au\/outputs\/anusplin.php."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.atmosenv.2013.04.002","article-title":"Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis","volume":"74","author":"Chen","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_25","first-page":"94","article-title":"New ridge parameters for ridge regression","volume":"15","author":"Dorugade","year":"2014","journal-title":"J. Assoc. Arab. Univ. Basic Appl. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jempfin.2019.08.007","article-title":"Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?","volume":"54","author":"Zhang","year":"2019","journal-title":"J. Empir. Financ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"338823","DOI":"10.1016\/j.aca.2021.338823","article-title":"A novel robust PLS regression method inspired from boosting prin-ciples: RoBoost-PLSR","volume":"1179","author":"Metz","year":"2021","journal-title":"Anal. Chim. Acta"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6940","DOI":"10.1038\/s41598-017-07197-6","article-title":"Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm","volume":"7","author":"Wang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.saa.2019.03.110","article-title":"Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques","volume":"218","author":"Li","year":"2019","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105600","DOI":"10.1016\/j.cmpb.2020.105600","article-title":"Optical health analysis of visual comfort for bright screen display based on back propagation neural network","volume":"196","author":"Wang","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.atmosres.2019.06.005","article-title":"Spatial-temporal analysis of precipitation variability in Qinghai Province, China","volume":"228","author":"Yang","year":"2019","journal-title":"Atmos. Res."},{"key":"ref_33","first-page":"281","article-title":"Spatiotemporal variability of permafrost degradation on the Qinghai-Tibet Plateau","volume":"3","author":"Jin","year":"2011","journal-title":"Sci. Cold Arid. Reg."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1080\/2150704X.2016.1219458","article-title":"Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China","volume":"7","author":"Yuan","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/j.1442-9993.1992.tb00790.x","article-title":"Estimating plant biomass: A review of techniques","volume":"17","author":"Catchpole","year":"1992","journal-title":"Aust. J. Ecol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/s90301768","article-title":"Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling","volume":"9","author":"Wu","year":"2009","journal-title":"Sensors"},{"key":"ref_38","first-page":"3016","article-title":"Variation of Quantitative Characteristics of Alpine Grassland Plant Community along the Altitude Gradient and Its Influencing Factors","volume":"34","author":"Cui","year":"2015","journal-title":"J. Ecol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1071\/SR14207","article-title":"Soil texture controls vegetation biomass and organic carbon storage in arid desert grassland in the middle of Hexi Corridor region in Northwest China","volume":"53","author":"Su","year":"2015","journal-title":"Soil Res."},{"key":"ref_40","first-page":"301","article-title":"The Density of Soil Organic Carbon and the Controlling Factors of Its Transformation in Eastern China","volume":"04","author":"Li","year":"2001","journal-title":"Geogr. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agwat.2020.106201","article-title":"Effect of the differences in spectral response of Mediterranean tree canopies on the estimation of evapotranspiration using vegetation index-based crop coefficients","volume":"238","author":"Carpintero","year":"2020","journal-title":"Agric. Water Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3843\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:09Z","timestamp":1760141169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3843"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163843"],"URL":"https:\/\/doi.org\/10.3390\/rs14163843","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,9]]}}}