{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:15:00Z","timestamp":1760148900327,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovative Research Team of the Ministry of Education of China","award":["IRT_17R59","ZDZX2018020"],"award-info":[{"award-number":["IRT_17R59","ZDZX2018020"]}]},{"name":"Inner Mongolia Key Project","award":["IRT_17R59","ZDZX2018020"],"award-info":[{"award-number":["IRT_17R59","ZDZX2018020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial\u2013temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g\/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.<\/jats:p>","DOI":"10.3390\/rs15123097","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:01:40Z","timestamp":1686708100000},"page":"3097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years"],"prefix":"10.3390","volume":"15","author":[{"given":"Nitu","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Grassland Resources of the Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, China"}]},{"given":"Guixiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5890-1758","authenticated-orcid":false,"given":"Deji","family":"Wuyun","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Bole","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010011, China"}]},{"given":"Wala","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China"}]},{"given":"Guodong","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Grassland Resources of the Ministry of Education, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010011, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.agee.2018.05.014","article-title":"Grazing induced changes in plant diversity is a critical factor controlling grassland productivity in the Desert Steppe, Northern China","volume":"265","author":"Zhang","year":"2018","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e01991","DOI":"10.1016\/j.gecco.2021.e01991","article-title":"Spatiotemporal variation of net primary productivity and its response to drought in Inner Mongolian desert steppe","volume":"33","author":"Yu","year":"2022","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, J., Han, X.-G., Zhang, W., and Shao, C. (2020). Grassland Ecosystems of China, Springer.","DOI":"10.1007\/978-981-15-3421-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1744-697X.2007.00073.x","article-title":"Grassland degradation in China: Methods of monitoring, management and restoration","volume":"53","author":"Akiyama","year":"2007","journal-title":"Grassl. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105747","DOI":"10.1016\/j.ecolind.2019.105747","article-title":"Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data","volume":"108","author":"Xu","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ma, Y., Zhang, Y., and Shang, J. (2022). Review of Remote Sensing Applications in Grassland Monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14122903"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s11629-020-5986-6","article-title":"A remote sensing monitoring method for alpine grasslands desertification in the eastern Qinghai-Tibetan Plateau","volume":"17","author":"Kuang","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ehlers, D., Wang, C., Coulston, J., Zhang, Y., Pavelsky, T., Frankenberg, E., Woodcock, C., and Song, C. (2022). Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data. Remote Sens., 14.","DOI":"10.3390\/rs14051115"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., and Sun, Y. (2021). Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12.","DOI":"10.3390\/f12020216"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Villacr\u00e9s, J., Fuentes, A., Reszka, P., and Cheein, F.A. (2021). Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of Eucalyptus globulus (Labill.) and Pinus radiata (D. Don.). Plants, 10.","DOI":"10.3390\/plants10040697"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","unstructured":"Gon\u00e7alves, A.C., Sousa, A., and Malico, I. (2021). Forest Biomass, IntechOpen."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","unstructured":"Baret, F., Guyot, G., and Major, D.J. (1989, January 10\u201314). TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Baret","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and limits of vegetation indices for LAI and APAR assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/gfs.12152","article-title":"Are soil-adjusted vegetation indices better than soil-unadjusted vegetation indices for above-ground green biomass estimation in arid and semi-arid grasslands?","volume":"70","author":"Ren","year":"2015","journal-title":"Grass Forage Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8223","DOI":"10.1080\/01431161.2019.1606958","article-title":"Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran?","volume":"40","author":"Baghi","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3254","DOI":"10.1109\/JSTARS.2016.2561618","article-title":"Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data\u2014A Machine Learning Approach","volume":"10","author":"Ali","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"35010","DOI":"10.1117\/1.JRS.10.035010","article-title":"Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery","volume":"10","author":"Wu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/S1872-2032(07)60012-2","article-title":"Remote sensing monitoring upon the grass production in China","volume":"27","author":"Xu","year":"2007","journal-title":"Acta Ecol. Sin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/S0034-4257(01)00278-4","article-title":"Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study","volume":"79","author":"Wylie","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5313","DOI":"10.1080\/01431160802036276","article-title":"MODIS-based remote sensing monitoring of grass production in China","volume":"29","author":"Xu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3835","DOI":"10.1080\/01431161.2018.1553319","article-title":"Modelling above-ground biomass based on vegetation indexes: A modified approach for biomass estimation in semi-arid grasslands","volume":"40","author":"Wang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ecolind.2017.02.039","article-title":"Modelling biomass of mountainous grasslands by including a species composition map","volume":"78","author":"Magiera","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2016.08.014","article-title":"Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China","volume":"186","author":"Liang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1139\/cjb-2016-0009","article-title":"Improving Plant Biomass Estimation in the Field Using Partial Least Squares Regression and Ridge Regression","volume":"94","author":"Ohsowski","year":"2016","journal-title":"Botany"},{"key":"ref_37","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":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zhang, L., Xie, D., Yin, X., Liu, C., and Liu, G. (2015). Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8010010"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","article-title":"Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images","volume":"154","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1071\/RJ14054","article-title":"Climate changes during the past 31 years and their contribution to the changes in the productivity of rangeland vegetation in the Inner Mongolian typical steppe","volume":"36","author":"Wu","year":"2014","journal-title":"Rangel. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.actao.2013.12.006","article-title":"Dynamic of grassland vegetation degradation and its quantitative assessment in the northwest China","volume":"55","author":"Zhou","year":"2014","journal-title":"Acta Oecologica"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ecolind.2017.08.019","article-title":"Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010","volume":"83","author":"Zhou","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.jaridenv.2016.09.004","article-title":"Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013","volume":"135","author":"Yang","year":"2016","journal-title":"J. Arid Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.ecolind.2018.01.018","article-title":"The dynamics of sand-stabilization services in Inner Mongolia, China from 1981 to 2010 and its relationship with climate change and human activities","volume":"88","author":"Li","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1007\/s00254-006-0369-z","article-title":"The temporal change of driving factors during the course of land desertification in arid region of North China: The case of Minqin County","volume":"51","author":"Ma","year":"2007","journal-title":"Environ. Geol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e01884","DOI":"10.1016\/j.gecco.2021.e01884","article-title":"Quantitative analysis of relative impacts of climate change and human activities on Xilingol grassland in recent 40 years","volume":"32","author":"Wu","year":"2021","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"134304","DOI":"10.1016\/j.scitotenv.2019.134304","article-title":"Impact of human activities and climate change on the grassland dynamics under different regime policies in the Mongolian Plateau","volume":"698","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.scitotenv.2019.06.503","article-title":"Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013","volume":"690","author":"Liu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.jaridenv.2009.09.030","article-title":"Multi-scale quantitative assessment of the relative roles of climate change and human activities in desertification\u2014A case study of the Ordos Plateau, China","volume":"74","author":"Xu","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_50","first-page":"102475","article-title":"Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method","volume":"103","author":"Wu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13717-020-00226-9","article-title":"The spatiotemporal changes of marshland and the driving forces in the Sanjiang Plain, Northeast China from 1980 to 2016","volume":"9","author":"Li","year":"2020","journal-title":"Ecol. Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e01237","DOI":"10.1016\/j.gecco.2020.e01237","article-title":"Response of plant traits of Stipa breviflora to grazing intensity and fluctuation in annual precipitation in a desert steppe, northern China","volume":"24","author":"Ye","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_53","unstructured":"Vermote, E., and Wolfe, R. (2023, March 19). MOD09GA MODIS\/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V006, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/api\/v1\/productPage\/product=MOD09GA."},{"key":"ref_54","unstructured":"Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D.B., Oimoen, M.J., Zhang, Z., Danielson, J.J., Krieger, T., Curtis, B., and Haase, J. (2011). ASTER Global Digital Elevation Model Version 2\u2014Summary of Validation Results, NASA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2007.04.015","article-title":"Development of a global evapotranspiration algorithm based on MODIS and global meteorology data","volume":"111","author":"Mu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_58","unstructured":"(2022, June 20). OpenLandMap. Available online: https:\/\/opengeohub.org\/about-openlandmap."},{"key":"ref_59","unstructured":"Hengl, T. (2022, June 20). OpenLandMap: Using Machine Learning for Global Good. Available online: https:\/\/opengeohub.org\/article\/openlandmap-using-machine-learning-global-good."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Gaughan, A.E., Stevens, F.R., Linard, C., Jia, P., and Tatem, A.J. (2013). High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0055882"},{"key":"ref_61","unstructured":"WorldPop (2022, May 22). What Is WorldPop?. Available online: https:\/\/www.worldpop.org\/."},{"key":"ref_62","unstructured":"Aiken, L.S., West, S.G., and Pitts, S.C. (2012). Handbook of Psychology, Wiley."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lovric, M. (2011). International Encyclopedia of Statistical Science, Springer.","DOI":"10.1007\/978-3-642-04898-2"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_67","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","first-page":"126","article-title":"A Non-Parametric Approach to the Change-Point Problem","volume":"28","author":"Pettitt","year":"1979","journal-title":"J. R. Stat. Society. Ser. C (Appl. Stat.)"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1080\/01621459.1995.10476499","article-title":"The Theil-Sen Estimator With Doubly Censored Data and Applications to Astronomy","volume":"90","author":"Akritas","year":"1995","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"7796","DOI":"10.1080\/01431161.2013.823000","article-title":"Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia\u2019s grassland between 2001 and 2011","volume":"34","author":"Gao","year":"2013","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:20Z","timestamp":1760126060000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123097"],"URL":"https:\/\/doi.org\/10.3390\/rs15123097","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,6,13]]}}}