{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:47:07Z","timestamp":1769633227444,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"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"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["42171394"],"award-info":[{"award-number":["42171394"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019B020214002"],"award-info":[{"award-number":["2019B020214002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171394"],"award-info":[{"award-number":["42171394"]}],"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>Grain protein content (GPC) is an important indicator of nutritional quality of rice. In this study, nitrogen fertilization experiments were conducted to monitor GPC for high-quality Indica rice varieties Meixiangzhan 2 (V1) and Wufengyou 615 (V2) in 2019 and 2020. Three types of parameters, including photosynthetic sensitive vegetation indices (VIs), canopy leaf area index (LAI), and crop plant nitrogen accumulation (PNA), obtained from UAV hyperspectral images were used to estimate rice GPC. Two-dimensional and three-dimensional GPC indices were constructed by combining any two of the three types of parameters and all three, respectively, based on the Euclidean distance method. The R2 and RMSE of the two-dimensional GPC index model for variety V1 at the tillering stage were 0.81 and 0.40% for modeling and 0.95 and 0.38% for validation, and 0.91 and 0.27% for modeling and 0.83 and 0.36% for validation for variety V2. The three-dimensional GPC index model for variety V1 had R2 and RMSE of 0.86 and 0.34% for modeling and 0.78 and 0.45% for validation, and 0.97 and 0.17% for modeling and 0.96 and 0.17% for validation for variety V2 at the panicle initiation stage. At the heading stage, the R2 and RMSE of the three-dimensional model for variety V1 were 0.92 and 0.26% for modeling and 0.91 and 0.37% for validation, and 0.96 and 0.20% for modeling and 0.99 and 0.15% for validation for variety V2. These results demonstrate that the GPC monitoring models incorporating multiple crop growth parameters based on Euclidean distance can improve GPC estimation accuracy and have the potential for field-scale GPC monitoring.<\/jats:p>","DOI":"10.3390\/rs14163989","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T03:15:27Z","timestamp":1660706127000},"page":"3989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Remote Sensing Monitoring of Rice Grain Protein Content Based on a Multidimensional Euclidean Distance Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"},{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"}]},{"given":"Xia","family":"Jing","sequence":"additional","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9898-628X","authenticated-orcid":false,"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"USDA-Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"}]},{"given":"Jiaojiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"}]},{"given":"Shikang","family":"Ming","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1080\/15226510701374831","article-title":"Yield physiology of rice","volume":"30","author":"Fageria","year":"2007","journal-title":"J. Plant Nutr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/S1537-5110(03)00097-7","article-title":"Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize","volume":"85","author":"Diker","year":"2003","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.fcr.2004.04.004","article-title":"Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR)","volume":"90","author":"Wang","year":"2004","journal-title":"Field Crops Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rsci.2018.10.003","article-title":"Factors affecting sensory quality of cooked japonica rice","volume":"25","author":"Yanjie","year":"2018","journal-title":"Rice Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1111\/jipb.13176","article-title":"Rice seed storage proteins: Biosynthetic pathways and the effects of environmental factors","volume":"63","author":"He","year":"2021","journal-title":"J. Integr. Plant Biol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1080\/10408398.2015.1084992","article-title":"Progress and challenges in improving the nutritional quality of rice (Oryza sativa L.)","volume":"57","author":"Birla","year":"2017","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_7","first-page":"1722","article-title":"Sensitive bands selection and nitrogen content monitoring of rice based on Gaussian regression analysis","volume":"41","author":"Wang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_8","first-page":"89","article-title":"Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat","volume":"12","author":"Yao","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","first-page":"317","article-title":"Investigating error sources in remote sensing of protein content of brown rice towards operational applications on a regional scale","volume":"81","author":"Sakaiya","year":"2012","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_10","first-page":"451","article-title":"Estimating rice grain protein contents with SPOT\/HRV data acquired at maturing stage","volume":"23","author":"Asaka","year":"2003","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_11","first-page":"317","article-title":"NDSI map and IPLS using hyperspectral data for assessment of plant and ecosystem variables","volume":"28","author":"Inoue","year":"2008","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_12","first-page":"358","article-title":"Study for estimation of rice grain protein contents using hyperspectral data","volume":"49","author":"Suhama","year":"2010","journal-title":"J. Jpn. Soc. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"514","DOI":"10.17221\/526\/2012-PSE","article-title":"Prediction of crude protein content in rice grain with canopy spectral reflectance","volume":"58","author":"Zhang","year":"2012","journal-title":"Plant Soil Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.foodchem.2015.05.038","article-title":"Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran","volume":"191","author":"Bagchi","year":"2016","journal-title":"Food Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1080\/00387010.2013.779283","article-title":"Detection of crude protein, crude starch, and amylose for rice by hyperspectral reflectance","volume":"47","author":"Liu","year":"2014","journal-title":"Spectrosc. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110609","DOI":"10.1016\/j.measurement.2021.110609","article-title":"Applications of new technologies for monitoring and predicting grains quality stored: Sensors, internet of things, and artificial intelligence","volume":"188","author":"Lutz","year":"2022","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1007\/s11119-010-9179-0","article-title":"Integrating remote sensing and GIS for prediction of rice protein contents","volume":"12","author":"Ryu","year":"2011","journal-title":"Precis. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"616689","DOI":"10.3389\/fpls.2021.616689","article-title":"Applications of UAS in crop biomass monitoring: A review","volume":"12","author":"Wang","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"281","DOI":"10.14358\/PERS.81.4.281","article-title":"Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs)","volume":"81","author":"Pajares","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhu, X., Guo, R., Liu, T., and Xu, K. (2021). Crop yield prediction based on agrometeorological indexes and remote sensing data. Remote Sens., 13.","DOI":"10.3390\/rs13102016"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Huang, Y., Lai, J., Li, J., Zhang, J., Yang, B., Li, B., Li, S., and Zhu, K. (2019). Improving field-scale wheat LAI retrieval based on UAV remote-sensing observations and optimized VI-LUTs. Remote Sens., 11.","DOI":"10.3390\/rs11202456"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Song, X., Yang, G., Xu, X., Zhang, D., Yang, C., and Feng, H. (2022). Winter wheat nitrogen estimation based on ground-level and UAV-mounted sensors. Sensors, 22.","DOI":"10.3390\/s22020549"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106399","DOI":"10.1016\/j.compag.2021.106399","article-title":"Linear mixed model analysis of NDVI-based canopy coverage, extracted from sequential UAV multispectral imagery of an open field tomato irrigation experiment","volume":"189","author":"Bedoya","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112967","DOI":"10.1016\/j.rse.2022.112967","article-title":"Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data","volume":"273","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12284-018-0198-1","article-title":"Deciphering the environmental impacts on rice quality for different rice cultivated areas","volume":"11","author":"Li","year":"2018","journal-title":"Rice"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s13258-021-01121-z","article-title":"Dissecting the meteorological and genetic factors affecting rice grain quality in Northeast China","volume":"43","author":"Chen","year":"2021","journal-title":"Genes Genom."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Manakos, I., and Braun, M. (2014). Beyond NDVI: Extraction of biophysical variables from remote sensing imagery. Land Use and Land Cover Mapping in Europe: Practices & Trends, Springer.","DOI":"10.1007\/978-94-007-7969-3"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/S0031-9422(97)80003-9","article-title":"Chlorophyll metabolism: From outer space down to the molecular level","volume":"46","year":"1997","journal-title":"Phytochemistry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2010.08.023","article-title":"The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis","volume":"115","author":"Garbulsky","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"100598","article-title":"Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data","volume":"23","author":"Song","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0034-4257(95)00195-6","article-title":"Retrieving leaf area index of boreal conifer forests using Landsat TM images","volume":"55","author":"Chen","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0168-1923(01)00233-7","article-title":"Response of tree phenology to climate change across Europe","volume":"108","author":"Chmielewski","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"110902","DOI":"10.1016\/j.scienta.2022.110902","article-title":"Phenological growth stages of \u2018Barcelona\u2019 hazelnut (Corylus avellana L.) described using an extended BBCH scale","volume":"296","author":"Paradinas","year":"2022","journal-title":"Sci. Hortic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.foreco.2015.12.005","article-title":"Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Picea abies Karst","volume":"364","author":"Lukac","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_36","first-page":"34","article-title":"A possibility of an index of NDVI and SPAD to estimate protein contents of rice","volume":"50","author":"Asanuma","year":"2011","journal-title":"J. Jpn. Soc. Photogramm. Remote Sens."},{"key":"ref_37","first-page":"169","article-title":"Effects of crop residue cover resulting from tillage practices on LAI estimation of wheat canopies using remote sensing","volume":"14","author":"Zhao","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2013.11.018","article-title":"Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis","volume":"143","author":"Cheng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8","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_41","first-page":"221","article-title":"Semi-empirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.081","article-title":"Euclidean distance estimation in incomplete datasets","volume":"248","author":"Mesquita","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.05.026","article-title":"Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)","volume":"197","author":"Amani","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1016\/j.rse.2007.12.006","article-title":"Distance to second cluster as a measure of classification confidence","volume":"112","author":"Mitchell","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.biosystemseng.2017.05.007","article-title":"Comparison of different uni- and multi-variate techniques for monitoring leaf water status as an indicator of water-deficit stress in wheat through spectroscopy","volume":"160","author":"Das","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.3390\/rs70505329","article-title":"Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in Gifu Prefecture, Central Japan","volume":"7","author":"Tanaka","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"23","DOI":"10.54386\/jam.v17i1.971","article-title":"Determination of optimal narrow bands for vegetation indices to discriminate nitrogen status in wheat crop","volume":"17","author":"Lunagaria","year":"2015","journal-title":"J. Agrometeorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1016\/S2095-3119(12)60457-2","article-title":"Common spectral bands and optimum vegetation indices for monitoring leaf nitrogen accumulation in rice and wheat","volume":"11","author":"Wang","year":"2012","journal-title":"J. Integr. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.2134\/agronj14.0084","article-title":"Comparison of different hyperspectral vegetation indices for estimating canopy leaf nitrogen accumulation in rice","volume":"106","author":"Chu","year":"2014","journal-title":"Agron. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.13031\/2013.23940","article-title":"Effect of N availability on vegetative index of cotton canopy: A spatial regression approach","volume":"50","author":"Bajwa","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"205","DOI":"10.2134\/agronj2007.0018","article-title":"A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy","volume":"100","author":"Lee","year":"2008","journal-title":"Agron. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1080\/0143116031000066288","article-title":"Leaf and spike reflectance spectra of rice with contrasting nitrogen supplemental levels","volume":"24","author":"Zhou","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2134\/agronj2004.1350","article-title":"Monitoring leaf nitrogen status in rice with canopy spectral reflectance","volume":"96","author":"Xue","year":"2004","journal-title":"Agron. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"117983","DOI":"10.1016\/j.saa.2019.117983","article-title":"Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice","volume":"229","author":"Das","year":"2020","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"108543","DOI":"10.1016\/j.fcr.2022.108543","article-title":"An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times","volume":"283","author":"Wang","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100059","DOI":"10.1016\/j.focha.2022.100059","article-title":"Variety difference in physico-chemical, cooking, textural, pasting and phytochemical properties of pigmented rice","volume":"1","author":"Devi","year":"2022","journal-title":"Food Chem. Adv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.envsoft.2007.10.003","article-title":"A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index","volume":"23","author":"Duchemin","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"426","DOI":"10.2134\/agronj2008.0139s","article-title":"AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles","volume":"101","author":"Steduto","year":"2009","journal-title":"Agron. J."},{"key":"ref_60","first-page":"431","article-title":"Integrated method for rice cultivation monitoring using Sentinel-2 data and leaf area index","volume":"24","author":"Ali","year":"2021","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106081","DOI":"10.1016\/j.agwat.2020.106081","article-title":"Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches","volume":"233","author":"Calera","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1080\/01431161.2016.1253899","article-title":"Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data","volume":"38","author":"Liu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-021-00750-5","article-title":"Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods","volume":"17","author":"Zhang","year":"2021","journal-title":"Plant Methods"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3989\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:10:41Z","timestamp":1760141441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,16]]},"references-count":63,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163989"],"URL":"https:\/\/doi.org\/10.3390\/rs14163989","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,16]]}}}