{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:36:43Z","timestamp":1778355403337,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020214002"],"award-info":[{"award-number":["2019B020214002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop yields are important for food security and people\u2019s living standards, and it is therefore very important to predict the yield in a timely manner. This study used different vegetation indices and red-edge parameters calculated based on the canopy reflectance obtained from near-surface hyperspectral data and UAV hyperspectral data and used the partial least squares regression (PLSR) and artificial neural network (ANN) methods to estimate the yield of winter wheat at different growth stages. Verification was performed based on these two types of hyperspectral remote sensing data and the yield was estimated using vegetation indices and a combination of vegetation indices and red-edge parameters as the modeling independent variables, respectively, using PLSR and ANN regression, respectively. The results showed that, for the same data source, the optimal vegetation index for estimating the yield was the same in all of the studied growth stages; however, the optimal red-edge parameters were different for different growth stages. Compared with using only the vegetation indices as the modeling factor to estimate yield, the combination of the vegetation indices and red-edge parameters obtained superior estimation results. Additionally, the accuracy of yield estimation was shown to be improved by using the PLSR and ANN methods, with the yield estimation model constructed using the PLSR method having a better prediction effect. Moreover, the yield prediction model obtained using the near-surface hyperspectral sensors had a higher fitting and accuracy than the model obtained using the UAV hyperspectral remote sensing data (the results were based on the specific growth stressors, N and water supply). This study shows that the use of a combination of vegetation indices and red-edge parameters achieved an improved yield estimation compared to the use of vegetation indices alone. In the future, the selection of suitable sensors and methods needs to be considered when constructing models to estimate crop yield.<\/jats:p>","DOI":"10.3390\/rs14174158","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T23:48:58Z","timestamp":1661384938000},"page":"4158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Comparison of Winter Wheat Yield Estimation Based on Near-Surface Hyperspectral and UAV Hyperspectral Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"first","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huilin","family":"Tao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiguang","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhai","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunjiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.fcr.2014.05.001","article-title":"Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images","volume":"164","author":"Wang","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_2","first-page":"125","article-title":"Rice yield forecasting models using satellite imagery in Egypt","volume":"16","author":"Noureldin","year":"2013","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G., and Wang, J. (2016). Estimation of winter wheat biomass and yield by combining the aquacrop model and field hyperspectral data. Remote Sens., 8.","DOI":"10.3390\/rs8120972"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2016.10.009","article-title":"Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring","volume":"187","author":"Campos","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/nature11420","article-title":"Closing yield gaps through nutrient and water management","volume":"490","author":"Mueller","year":"2012","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gennaro, S.F.D., Toscano, P., Gatti, M., Poni, S., Berton, A., and Matese, A. (2022). Spectral comparison of UAV-Based hyper and multispectral cameras for precision viticulture. Remote Sens., 14.","DOI":"10.3390\/rs14030449"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, W., Gao, X., Cheng, Y., Ren, Y., Zhang, Z., Wang, R., Cao, J., and Geng, H. (2022). QTL mapping of leaf area index and chlorophyll content based on UAV remote sensing in wheat. Agriculture, 12.","DOI":"10.3390\/agriculture12050595"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1007\/s11119-022-09884-5","article-title":"An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features","volume":"23","author":"Xu","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of vegetation indices for agricultural crop yield prediction using neural network techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(93)90113-C","article-title":"On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna","volume":"45","author":"Roberto","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.rse.2004.11.012","article-title":"Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data","volume":"94","author":"Formaggio","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1126\/science.1239402","article-title":"Climate change impacts on global food security","volume":"341","author":"Wheeler","year":"2013","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Du, M.M., and Noboru, N. (2016, January 14\u201317). Multi-temporal Monitoring of Wheat Growth through Correlation Analysis of Satellite Images, Unmanned Aerial Vehicle Images with Ground Variable. Proceedings of the 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL, Seattle, WA, USA.","DOI":"10.1016\/j.ifacol.2016.10.002"},{"key":"ref_17","unstructured":"Eisenbeiss, H. (2004, January 18\u201320). A Mini Unmanned Aerial Vehicle (UAV): System Overview and Image Acquisition. Proceedings of the International Workshop on Processing and Visualization Using High-Resolution Imagery, Pitsanulok, Thailand."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"333","DOI":"10.21273\/HORTSCI.43.2.333","article-title":"Remote Sensing of Canopy Cover in Horticultural Crops","volume":"43","author":"Thomas","year":"2008","journal-title":"Hortscience"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.rse.2014.06.006","article-title":"Green area index from an unmanned aerial system over wheat and rapeseed crops","volume":"152","author":"Verger","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.rse.2011.10.016","article-title":"Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters","volume":"117","author":"Mishra","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1016\/j.rse.2009.05.003","article-title":"Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications","volume":"113","author":"Meroni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"117","article-title":"Application of spectral remote sensing for agronomic decisions","volume":"100","author":"Hatfeld","year":"2008","journal-title":"Agron. J."},{"key":"ref_26","first-page":"2585","article-title":"Study on the prediction of cotton yield within field scale with time series hyperspectral imagery","volume":"36","author":"Liu","year":"2016","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_27","first-page":"27","article-title":"Large-area rice yield forecasting using satellite imageries","volume":"12","author":"Wang","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","first-page":"78","article-title":"Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing","volume":"34","author":"Zhu","year":"2018","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.compag.2004.11.014","article-title":"Artificial neural networks to predict corn yield from compact airborne spectrographic imager data","volume":"47","author":"Uno","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","first-page":"1295","article-title":"Development of citrus yield prediction model based on airborne hyperspectral imaging","volume":"30","author":"Ye","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1051\/agro:2004037","article-title":"Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico","volume":"24","author":"Rodriguez","year":"2004","journal-title":"Agronomie"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.eja.2015.08.006","article-title":"Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing","volume":"71","author":"Li","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"The red edge of plant leaf reflectance","volume":"4","author":"Horler","year":"1983","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct georeferencing of ultrahigh-resolution UAV imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE. Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_40","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":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2134\/agronj2000.92183x","article-title":"Spectral vegetation indices as non-destructive tools for determining durum wheat yield","volume":"92","author":"Aparicio","year":"2000","journal-title":"Agron. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"198","DOI":"10.2135\/cropsci1997.0011183X003700010033x","article-title":"Visible and near-infrared reflectance assessment of salinity effects on barley","volume":"37","author":"Penuelas","year":"1997","journal-title":"Crop Sci."},{"key":"ref_44","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_45","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431169308954010","article-title":"The reflectance at the 950\u2013970 nm region as an indicator of plant water status","volume":"14","author":"Filella","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_50","first-page":"194","article-title":"Monitoring nitrogen accumulation in wheat leaf with red edge characteristics parameters","volume":"25","author":"Feng","year":"2009","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","unstructured":"Wold, H. (1966). Estimation of principal components and related models by iterative least squares. Multivariate Analysis, Academic Press."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1002\/col.22393","article-title":"Prediction of the colorimetric parameters and mass loss of heat-treated bamboo: Comparison of multiple linear regression and artificial neural network method","volume":"44","author":"Gurgen","year":"2019","journal-title":"Color Res. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., and Fan, L. (2020). Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors, 20.","DOI":"10.3390\/s20041231"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.3389\/fpls.2017.01733","article-title":"Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hybrid and conventional barley","volume":"8","author":"Kefauver","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13007-018-0338-z","article-title":"Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis","volume":"14","author":"Gong","year":"2018","journal-title":"Plant Methods"},{"key":"ref_57","first-page":"159","article-title":"A radiative transfer model-based method for the estimation of grassland aboveground biomass","volume":"54","author":"Quan","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","first-page":"113","article-title":"Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing","volume":"32","author":"Gao","year":"2016","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Long, H., Yue, J., Li, Z., Yang, G., Yang, X., and Fan, L. (2020). Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors, 20.","DOI":"10.3390\/s20051296"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2013.10.010","article-title":"Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements","volume":"100","author":"Fu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1080\/01431161.2020.1823033","article-title":"Mapping Winter-Wheat Biomass and Grain Yield Based on a Crop Model and UAV Remote Sensing","volume":"42","author":"Yue","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of Winter-Wheat above-Ground Biomass Based on UAV Ultrahigh-Ground-Resolution Image Textures and Vegetation Indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4158\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:38Z","timestamp":1760141678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,24]]},"references-count":62,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174158"],"URL":"https:\/\/doi.org\/10.3390\/rs14174158","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,24]]}}}