{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T20:44:35Z","timestamp":1774471475306,"version":"3.50.1"},"reference-count":107,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T00:00:00Z","timestamp":1639094400000},"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":["31770679"],"award-info":[{"award-number":["31770679"]}],"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>Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.<\/jats:p>","DOI":"10.3390\/rs13245030","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:29:33Z","timestamp":1639358973000},"page":"5030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm"],"prefix":"10.3390","volume":"13","author":[{"given":"Chunhua","family":"Qian","sequence":"first","affiliation":[{"name":"School of Forestry, Nanjing Forestry University, Nanjing 210037, China"},{"name":"School of Smart Agricultural, Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, China"}]},{"given":"Hequn","family":"Qiang","sequence":"additional","affiliation":[{"name":"School of Smart Agricultural, Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, China"},{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215301, China"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Smart Agricultural, Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, China"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1126\/science.1058629","article-title":"Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998","volume":"292","author":"Fang","year":"2001","journal-title":"Science"},{"key":"ref_2","first-page":"1311","article-title":"Research progress of Forest Aboveground Biomass Estimation by remote sensing","volume":"31","author":"Tang","year":"2012","journal-title":"Chin. J. Ecol."},{"key":"ref_3","first-page":"144","article-title":"Review on estimation methods of Forest Aboveground Biomass","volume":"33","author":"Zhang","year":"2011","journal-title":"J. Beijing For. Univ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1126\/science.256.5053.70","article-title":"Biomass and Carbon Budget of European Forests, 1971 to 1990","volume":"256","author":"Kauppi","year":"1992","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/S0269-7491(01)00212-3","article-title":"Measuring carbon in forests: Current status and future challenges","volume":"116","author":"Brown","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_6","first-page":"631","article-title":"Research progress of forest biomass estimation by remote sensing technology","volume":"37","author":"Li","year":"2012","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_7","first-page":"80","article-title":"Mitigation of Carbon Emissions to the Atmosphere by Forest Management","volume":"75","author":"Brown","year":"1996","journal-title":"Commonw. For. Rev."},{"key":"ref_8","first-page":"1","article-title":"Temporal and spatial dynamics of vegetation carbon storage in forest ecosystem in China","volume":"26","author":"Xu","year":"2007","journal-title":"Prog. Geogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"967","DOI":"10.14358\/PERS.71.8.967","article-title":"Satellite Estimation of Aboveground Biomass and Impacts of Forest Stand Structure","volume":"71","author":"Lu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"984","DOI":"10.3390\/f4040984","article-title":"Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest","volume":"4","author":"He","year":"2013","journal-title":"Forests"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/JSTARS.2012.2211863","article-title":"Stem volume and above-ground biomass estimation of individual pine trees from LiDAR Data: Contribution of full-waveform signals","volume":"6","author":"Allouis","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","first-page":"1","article-title":"Sustainable forest management strategy in China under the background of low carbon economy","volume":"25","author":"Zhao","year":"2012","journal-title":"World For. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3371","DOI":"10.1109\/TGRS.2012.2219872","article-title":"Forest biomass estimation using texture measurements of high-resolution dual-polarization C-Band SAR Data J","volume":"51","author":"Sarker","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, C., Li, M., and Liu, Z. (2019). Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms. Forests, 10.","DOI":"10.3390\/f10121073"},{"key":"ref_15","first-page":"935","article-title":"Importance of Biomass in the Global Carbon Cycle","volume":"114","author":"Houghton","year":"2009","journal-title":"J. Geophys. Res. Bio Geosci."},{"key":"ref_16","first-page":"62","article-title":"Review on remote sensing retrieval methods of Forest Aboveground Biomass","volume":"19","author":"Liu","year":"2015","journal-title":"J. Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/JSTARS.2016.2597762","article-title":"Consequences of Landsat Image Strata Classification Errors on Bias and Variance of Inventory Estimates: A Forest Inventory Case Study","volume":"10","author":"Crosby","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","first-page":"33","article-title":"Study on forest biomass estimation model of Nanbei mountain in Xining City Based on landsat-8 image","volume":"31","author":"Yang","year":"2016","journal-title":"J. Northwest For. Univ."},{"key":"ref_19","first-page":"226","article-title":"Optimal extraction of characteristic variables and forest biomass inversion based on Landsat 8 OLI","volume":"30","author":"Xu","year":"2015","journal-title":"Remote. Sens. Technol. Appl."},{"key":"ref_20","first-page":"1","article-title":"Biomass estimation and reconstruction of Pinus Takayama in Shangri La based on Landsat TM. For","volume":"41","author":"Lu","year":"2016","journal-title":"Inventory Plan."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Y., and Li, M. (2019). Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China. Forests, 10.","DOI":"10.3390\/f10020104"},{"key":"ref_22","first-page":"84","article-title":"Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods","volume":"8","author":"Joshi","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ou, G., Li, C., Lv, Y., Wei, A., Xiong, H., Xu, H., and Wang, G. (2019). Improving aboveground biomass estimation of pinus densata forests in yunnan using landsat 8 imagery by incorporating age dummy variable and method comparison. Remote Sens., 11.","DOI":"10.3390\/rs11070738"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11252-016-0585-6","article-title":"Modeling above-ground carbon storage: A remote sensing approach to derive individual tree species information in urban settings","volume":"20","author":"Tigges","year":"2016","journal-title":"Urban Ecosyst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2014.12.019","article-title":"Decrease of L-band SAR backscatter with biomass of dense forests","volume":"159","author":"Mermoz","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.asr.2017.04.018","article-title":"Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest","volume":"60","author":"Kumar","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/TGRS.2020.3018638","article-title":"Model-Based Estimation of Forest Canopy Height and Biomass in the Canadian Boreal Forest Using Radar, LiDAR, and Optical Remote Sensing","volume":"59","author":"Benson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Feng, Z., Lu, J., and Liu, J. (2020). Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data. Forests, 11.","DOI":"10.3390\/f11020163"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z. (2019). Optimal combination of predictors and algorithms for forest above-ground biomass mapping from sentinel and SRTM data. Remote Sens., 11.","DOI":"10.3390\/rs11040414"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Laurin, G.V., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., and Papale, D. (2016). Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates. Remote Sens., 9.","DOI":"10.3390\/rs9010018"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Zhang, B., Wang, Z., and Xi, Y. (2018). Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests, 9.","DOI":"10.3390\/f9100582"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2018.04.056","article-title":"Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar","volume":"213","author":"Minh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"387","article-title":"Estimation of vegetation biomass in mangrove wetland by radar remote sensing","volume":"10","author":"Li","year":"2006","journal-title":"J. Remote. Sens."},{"key":"ref_36","first-page":"13","article-title":"Estimation of forest biomass based on Remote Sensing Information","volume":"31","author":"Guo","year":"2003","journal-title":"J. Northeast. For. Univ."},{"key":"ref_37","first-page":"818","article-title":"Establishment of remote sensing estimation model of biomass on alpine and subalpine coniferous forest land in Northwest Yunnan and determination of light saturation point based on Landsat 8 OLI","volume":"43","author":"Wu","year":"2021","journal-title":"J. Yunnan Univ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1139\/cjfr-2019-0216","article-title":"Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable","volume":"50","author":"Li","year":"2020","journal-title":"Can. J. For. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1080\/10798587.2017.1296660","article-title":"Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm","volume":"24","author":"Wang","year":"2018","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ecoinf.2018.12.010","article-title":"Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam","volume":"50","author":"Dang","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_43","first-page":"1","article-title":"Research on Hyperspectral Remote Sensing Extraction of Red Tide Based on depth confidence network (DBN)","volume":"38","author":"Jiang","year":"2019","journal-title":"Ocean Technol."},{"key":"ref_44","first-page":"67","article-title":"Advances in Evaluation of Sustainable Development Capability in Karst Region","volume":"40","author":"Liu","year":"2012","journal-title":"Guizhou Agric. Sci."},{"key":"ref_45","first-page":"256","article-title":"Depth determination method of DBN network","volume":"30","author":"Pan","year":"2015","journal-title":"Control Decis."},{"key":"ref_46","first-page":"227","article-title":"Vegetation degradation and attribution in Guizhou Province Based on MODIS NDVI","volume":"38","author":"Ma","year":"2019","journal-title":"Carsologica Sin."},{"key":"ref_47","first-page":"44","article-title":"The Evaluation Studies Progress and Prospects of Sustainable Development in Rocky Deserti-fication Reegion","volume":"1","author":"Xiong","year":"2012","journal-title":"Ecol. Econ."},{"key":"ref_48","unstructured":"Ren, J., Meng, D., and Chen, H. (2019, January 13\u201315). Analysis of Temporal and Spatial Evolution of Rocky Desertification Sensitivity in Tongren, Guizhou Province. Proceedings of the IOP Conference Series: Materials Science and Engineering, Chengdu, China."},{"key":"ref_49","unstructured":"Xiong, K.N. (2002). A Typical Study on Karst Rocky Desertification Based on Remote Sensing and GIS: A Case Study of Guizhou Province, Geological Publishing House."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/ldr.1102","article-title":"Assessing spatial-temporal evolution processes of karst rocky desertification land: Indications for restoration strategies","volume":"24","author":"Bai","year":"2013","journal-title":"Land Degrad. Dev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jclepro.2019.117888","article-title":"Spatiotemporal variations in cropland abandonment in the Guizhou\u2013Guangxi karst mountain area, China","volume":"238","author":"Han","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.18306\/dlkxjz.2019.11.011","article-title":"Temporal and spatial evolution of rocky desertification and influencing factors of human activities in Guanling County, Guizhou Province from 2010 to 2015","volume":"38","author":"Yao","year":"2019","journal-title":"Prog. Geogr."},{"key":"ref_53","first-page":"126","article-title":"Spatial pattern and composition structure of forest in Guizhou","volume":"1","author":"Yao","year":"2003","journal-title":"Acta Geogr. Sin."},{"key":"ref_54","first-page":"725","article-title":"Dynamic changes of forest resources in Guizhou Province","volume":"6","author":"Zhang","year":"2003","journal-title":"Geogr. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1002\/ldr.3731","article-title":"Factors influencing the evolution of human-driven rocky desertification in karst areas","volume":"32","author":"Zhang","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_56","first-page":"497","article-title":"Biomass and net production of forest vegetation in China","volume":"5","author":"Fang","year":"1996","journal-title":"Acta Ecol. Sin."},{"key":"ref_57","first-page":"44","article-title":"Comparison on Estimation of Wood Biomass Using Forest Inventory Data","volume":"48","author":"Li","year":"2012","journal-title":"Sci. Silvae Sin."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"15501477211039137","DOI":"10.1177\/15501477211039137","article-title":"Long-term changes of forest biomass and its driving factors in karst area, Guizhou, China","volume":"17","author":"Qian","year":"2021","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_59","unstructured":"(2021, May 15). Earth Engine Code Editor. Available online: https:\/\/code.earthengine.google.com\/."},{"key":"ref_60","unstructured":"(2021, May 15). Google Earth Engine. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/modis."},{"key":"ref_61","first-page":"39","article-title":"Tropical forest cover density mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_63","unstructured":"Zhou, Z.H. (2016). Machine Learning, Tsinghua University Press."},{"key":"ref_64","unstructured":"Du, P.J. (2019). Remote Sensing Multi Classifier Ensemble Method and Its Application, Science Press."},{"key":"ref_65","first-page":"443","article-title":"Daily Sediment Yield Modeling with Artificial Neural Network Using 10-fold Cross Validation Method: A Small Agricultural Watershed, Kapgari, India","volume":"4","author":"Singh","year":"2011","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_67","first-page":"2596","article-title":"Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks","volume":"35","author":"Cui","year":"2015","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.3390\/rs3071427","article-title":"Evaluating the remote sensing and inventory-based estimation of biomass in the western carpathians","volume":"3","author":"Magdalena","year":"2011","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"124828","DOI":"10.1016\/j.jhydrol.2020.124828","article-title":"Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach","volume":"585","author":"Wang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"114664","DOI":"10.1016\/j.applthermaleng.2019.114664","article-title":"Anomaly detection of gas turbines based on normal pattern extraction","volume":"166","author":"Bai","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_71","unstructured":"Duan, L.F., Pan, J.X., and Guo, Z.L. (2019). Nondestructive monitoring of multi variety rice biomass based on deep belief network. J. Agric. Mach., 136\u2013143."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Dong, L., Du, H., Han, N., Li, X., Zhu, D., Mao, F., Zhang, M., Zheng, J., Liu, H., and Huang, Z. (2020). Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2. Remote Sens., 12.","DOI":"10.3390\/rs12060958"},{"key":"ref_73","unstructured":"Gao, Y.K. (2018). Estimation of Forest Aboveground Biomass in Typical Subtropical Areas Based on Machine Learning and Multi-Source Data. [Master\u2019s Thesis, Zhejiang Agriculture and Forestry University]."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.rse.2015.07.005","article-title":"Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR","volume":"168","author":"Santoro","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_75","first-page":"76","article-title":"Comprehensive evaluation of light and temperature utilization ability of summer maize varieties based on Principal Component Cluster stepwise regression analysis","volume":"350","author":"Wang","year":"2020","journal-title":"Shandong Agric. Sci."},{"key":"ref_76","first-page":"3200","article-title":"Study on quality grade evaluation of Andrographis paniculata based on principal component clustering and PLS regression analysis","volume":"50","author":"Cui","year":"2019","journal-title":"Chin. Tradit. Herb. Drugs"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/36.298018","article-title":"An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS","volume":"32","author":"Huete","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11119-008-9075-z","article-title":"A broad-band leaf chlorophyll vegetation index at the canopy scale","volume":"9","author":"Vincini","year":"2008","journal-title":"Precis. Agric."},{"key":"ref_79","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_80","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_81","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote. Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote. Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"968","DOI":"10.2134\/agronj2005.0200","article-title":"Aerial Color Infrared Photography for Determining Early In-Season Nitrogen Requirements in Corn","volume":"98","author":"Sripada","year":"2006","journal-title":"Agron. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.2134\/agronj2004.0314","article-title":"Aerial Color Infrared Photography for Determining Late-Season Nitrogen Requirements in Corn","volume":"97","author":"Sripada","year":"2005","journal-title":"Agron. J."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote. Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0034-4257(01)00342-X","article-title":"Airborne multi-spectral data for quantifying leaf area index, nitrogen concentration and photosynthetic efficiency in agriculture","volume":"81","author":"Boegh","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_89","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_90","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_91","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":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Towers, P.C., Strever, A., and Poblete-Echeverr\u00eda, C. (2019). Comparison of vegetation indices for leaf area index estimation in vertical shoot positioned vine canopies with and without grenbiule hail-protection netting. Remote Sens., 11.","DOI":"10.3390\/rs11091073"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_95","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_96","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/S0034-4257(03)00094-4","article-title":"Retrieval of leaf area index in different vegetation types using high resolution satellite data","volume":"86","author":"Colombo","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling ap-proaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_99","first-page":"405","article-title":"Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in Oregon forests","volume":"56","author":"Duane","year":"2010","journal-title":"For. Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2005.05.009","article-title":"Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection","volume":"97","author":"Healey","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_101","first-page":"103","article-title":"A visible band index for remote sensing leaf chlorophyll content at the canopy scale","volume":"21","author":"Hunt","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.2134\/agronj2010.0395","article-title":"Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index","volume":"103","author":"Hunt","year":"2011","journal-title":"Agron. J."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1080\/01431160110107806","article-title":"Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction","volume":"23","author":"Gitelson","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1080\/01431160210121764","article-title":"Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas","volume":"23","author":"Price","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(01)00296-6","article-title":"A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery","volume":"80","author":"Rogan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/S0034-4257(02)00042-1","article-title":"Impacts of Vegetation Dynamics on the Identification of Land-cover Change in a Biologically Complex Community in North Carolina, USA","volume":"82","author":"Luneetta","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_107","first-page":"3053","article-title":"Transformed difference vegetation index (TDVI) for vegetation cover mapping","volume":"5","author":"Bannari","year":"2003","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5030\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:45:15Z","timestamp":1760168715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,10]]},"references-count":107,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245030"],"URL":"https:\/\/doi.org\/10.3390\/rs13245030","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,10]]}}}