{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:31:36Z","timestamp":1768267896905,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"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":["31770677"],"award-info":[{"award-number":["31770677"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The aboveground biomass (AGB) of a forest is an important indicator of the forest\u2019s terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R2 values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t\/ha for forestland in 2016.<\/jats:p>","DOI":"10.3390\/rs14215456","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"5456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest"],"prefix":"10.3390","volume":"14","author":[{"given":"Huafang","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Forestry, Southwest Forestry University, Kunming 650224, China"},{"name":"Center for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"},{"name":"Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Qin","sequence":"additional","affiliation":[{"name":"MOA Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"De-Li","family":"Zhai","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-6690","authenticated-orcid":false,"given":"Guanglong","family":"Ou","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1754-8721","authenticated-orcid":false,"given":"Xiong","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"},{"name":"Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaojuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Center for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"},{"name":"Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlong","family":"Fan","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunliang","family":"Zhao","sequence":"additional","affiliation":[{"name":"MOA Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yin, G., Zhang, Y., Sun, Y., Wang, T., Zeng, Z., and Piao, S. (2015). MODIS Based Estimation of Forest Aboveground Biomass in China. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130143"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1890\/1051-0761(2002)012[0891:FCSITN]2.0.CO;2","article-title":"Forest carbon sinks in the Northern Hemisphere","volume":"12","author":"Goodale","year":"2002","journal-title":"Ecol. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1111\/j.1365-2486.2005.00955.x","article-title":"Aboveground forest biomass and the global carbon balance","volume":"11","author":"Houghton","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A Large and Persistent Carbon Sink in the World\u2032s Forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ou, G.L., Lv, Y.Y., Xu, H., and Wang, G.X. (2019). Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images. Remote Sens., 11.","DOI":"10.3390\/rs11232750"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5534","DOI":"10.3390\/rs70505534","article-title":"National Forest Aboveground Biomass Mapping from ICESat\/GLAS Data and MODIS Imagery in China","volume":"7","author":"Chi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","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":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S0034-4257(02)00130-X","article-title":"Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks","volume":"84","author":"Dong","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1080\/01431160310001654978","article-title":"Mapping Mediterranean scrub with satellite imagery: Biomass estimation and spectral behaviour","volume":"25","author":"Calvao","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liang, S., and Yang, L. (2019). A Review of Regional and Global Gridded Forest Biomass Datasets. Remote Sens., 11.","DOI":"10.3390\/rs11232744"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, M., Long, J., and Lin, H. (2021). A Novel Method for Estimating Spatial Distribution of Forest Above-Ground Biomass Based on Multispectral Fusion Data and Ensemble Learning Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13193910"},{"key":"ref_12","first-page":"102307","article-title":"Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping","volume":"98","author":"Cooper","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote Sensing of Above-Ground Biomass. Remote Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1080\/014311600210119","article-title":"Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia","volume":"21","author":"Steininger","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"L10501","DOI":"10.1029\/2004GL019782","article-title":"Forest biomass estimation over regional scales using multisource data","volume":"31","author":"Baccini","year":"2004","journal-title":"Geophys. Res. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160500142145","article-title":"Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1080\/01431160500044705","article-title":"An efficient regression strategy for extracting forest biomass information from satellite sensor data","volume":"26","author":"Rahman","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"073484","DOI":"10.1117\/1.JRS.7.073484","article-title":"Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data","volume":"7","author":"Zhou","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"644","DOI":"10.2134\/agronj2010.0449","article-title":"Determining Biophysical Parameters for Olive Trees Using CASI-Airborne and Quickbird-Satellite Imagery","volume":"103","author":"Gomez","year":"2011","journal-title":"Agron. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs4010135","article-title":"Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)","volume":"4","author":"Gomez","year":"2012","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qiu, P.H., Wang, D.Z., Zou, X.Q., Yang, X., Xie, G.Z., Xu, S.J., and Zhong, Z.Q. (2019). Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data. Forests, 10.","DOI":"10.3390\/f10100871"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s11676-018-0713-7","article-title":"Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data","volume":"30","author":"Zhang","year":"2019","journal-title":"J. For. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1139\/cjfr-2017-0346","article-title":"Integrating forest inventory data and MODIS data to map species-level biomass in Chinese boreal forests","volume":"48","author":"Zhang","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lu, J., Feng, Z., and Zhu, Y. (2019). Estimation of Forest Biomass and Carbon Storage in China Based on Forest Resources Inventory Data. Forests, 10.","DOI":"10.3390\/f10080650"},{"key":"ref_29","unstructured":"Forestry Department of Yunnan Province (2017). Report of Forest Resource Survey in Yunnan Province, Yunnan Science and Technology Press."},{"key":"ref_30","unstructured":"Forestry Department of Yunnan Province (2018). Forest Resources in Yunnan, Yunnan Science and Technology Press."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, F., Niu, S., Tong, X., Zhao, J., Sun, Y., and He, T. (2014). The Impact of Precipitation Regimes on Forest Fires in Yunnan Province, Southwest China. Sci. World J., 326782.","DOI":"10.1155\/2014\/326782"},{"key":"ref_32","unstructured":"Editting Committee of Yunnan Forest (1986). Yunnan Forest, Yunnan Science and Technology Press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10666-005-9019-1","article-title":"Modeling distribution changes of vegetation in China under future climate change","volume":"11","author":"Weng","year":"2006","journal-title":"Env. Model Assess"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1080\/22797254.2021.1917308","article-title":"Evaluating FY3C-VIRR reconstructed land surface temperature in cloudy regions","volume":"54","author":"Yongqian","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_35","unstructured":"Guo, N., Wang, X., Cai, D., and Yang, J. (2007, January 23\u201327). Comparison and evaluation between MODIS vegetation indices in Northwest China. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain."},{"key":"ref_36","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spe."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetatiob index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of Changes in Leaf Water-Content Using near-Infrared and Middle-Infrared Reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.asr.2012.01.014","article-title":"An introduction to China FY3 radio occultation mission and its measurement simulation","volume":"49","author":"Bi","year":"2012","journal-title":"Adv. Sp. Res."},{"key":"ref_48","first-page":"302","article-title":"Assimilation of FY-3A microwave observations and simulation of brightness temperature under cloudy and rainy condition","volume":"30","author":"Dong","year":"2014","journal-title":"J. Trop. Meteorol."},{"key":"ref_49","first-page":"23","article-title":"Experiments of assimilating FY-3A microwave data in forecast of typhoon Morakot","volume":"28","author":"Yang","year":"2012","journal-title":"J. Trop. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1007\/s11434-010-3240-2","article-title":"Analysis for retrieval and validation results of FY-3 Total Ozone Unit (TOU)","volume":"55","author":"Wang","year":"2010","journal-title":"China Sci. Bull."},{"key":"ref_51","first-page":"1084","article-title":"Forest biomass of China: An estimate based on the biomass-volume relationship","volume":"8","author":"Fang","year":"1998","journal-title":"Ecol. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1016\/j.rse.2010.02.022","article-title":"Measuring biomass changes due to woody encroachment and deforestation\/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter","volume":"115","author":"Mitchard","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1016\/j.rse.2011.03.021","article-title":"Forest biomass mapping from lidar and radar synergies","volume":"115","author":"Sun","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"436537","article-title":"Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates","volume":"2012","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_55","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lee, H., Wang, J., and Leblon, B. (2020). Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn. Remote Sens., 12.","DOI":"10.3390\/rs12132071"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Gilani, H., and Ahmad, S.R. (2021). Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery-A Literature Review. Forests, 12.","DOI":"10.3390\/f12070914"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"109246","DOI":"10.1016\/j.ecolind.2022.109246","article-title":"Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis","volume":"142","author":"Li","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Esteban, J., McRoberts, R.E., Fernandez-Landa, A., Luis Tome, J., and Naesset, E. (2019). Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data. Remote Sens., 11.","DOI":"10.3390\/rs11161944"},{"key":"ref_62","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_63","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, F., Wang, W., and Yu, K. (2021). Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices. Remote Sens., 13.","DOI":"10.3390\/rs13122339"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2015.02.026","article-title":"Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data","volume":"163","author":"McRoberts","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhang, F., and Zhou, G. (2019). Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol., 19.","DOI":"10.1186\/s12898-019-0233-0"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"189","DOI":"10.5589\/m07-025","article-title":"Retrieving crop physiological parameters and assessing water deficiency using MODIS data during the winter wheat growing period","volume":"33","author":"Yi","year":"2007","journal-title":"Can. J. Remote Sens."},{"key":"ref_67","first-page":"48","article-title":"Accuracy and Sensitivity of Retrieving Vegetation Leaf Water Content","volume":"31","author":"Chen","year":"2016","journal-title":"Remote Sens. Inf."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"083677","DOI":"10.1117\/1.JRS.8.083677","article-title":"Integration method to estimate above-ground biomass in arid prairie regions using active and passive remote sensing data","volume":"8","author":"Xing","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1002\/2017JG004145","article-title":"Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth","volume":"122","author":"Momen","year":"2017","journal-title":"J. Geophys. Res. Biogeo."},{"key":"ref_70","unstructured":"Salajanu, D., and Jacobs, D.M. (2007, January 7\u201311). Accuracy assessment of biomass and forested area classification from modis, landstat-tm satellite imagery and forest inventory plot data. Proceedings of the ASPRS 2007 Annual Conference, Tampa, FL, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1002\/rse2.203","article-title":"The real potential of current passive satellite data to map aboveground biomass in tropical forests","volume":"7","author":"Jha","year":"2021","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_72","first-page":"160","article-title":"Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area","volume":"14","author":"Tian","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5456\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:06:06Z","timestamp":1760144766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,30]]},"references-count":72,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215456"],"URL":"https:\/\/doi.org\/10.3390\/rs14215456","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,30]]}}}