{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T12:56:26Z","timestamp":1782824186417,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No. 32071902"],"award-info":[{"award-number":["No. 32071902"]}]},{"name":"National Natural Science Foundation of China","award":["No. BE2020319"],"award-info":[{"award-number":["No. BE2020319"]}]},{"name":"National Natural Science Foundation of China","award":["No. yzuxk202007"],"award-info":[{"award-number":["No. yzuxk202007"]}]},{"name":"National Natural Science Foundation of China","award":["No. yzuxk202008"],"award-info":[{"award-number":["No. yzuxk202008"]}]},{"name":"the Key Research Program of Jiangsu Province, China","award":["No. 32071902"],"award-info":[{"award-number":["No. 32071902"]}]},{"name":"the Key Research Program of Jiangsu Province, China","award":["No. BE2020319"],"award-info":[{"award-number":["No. BE2020319"]}]},{"name":"the Key Research Program of Jiangsu Province, China","award":["No. yzuxk202007"],"award-info":[{"award-number":["No. yzuxk202007"]}]},{"name":"the Key Research Program of Jiangsu Province, China","award":["No. yzuxk202008"],"award-info":[{"award-number":["No. yzuxk202008"]}]},{"name":"the Yangzhou University Interdisciplinary Research Foundation for Crop Science Discipline of Targeted Support","award":["No. 32071902"],"award-info":[{"award-number":["No. 32071902"]}]},{"name":"the Yangzhou University Interdisciplinary Research Foundation for Crop Science Discipline of Targeted Support","award":["No. BE2020319"],"award-info":[{"award-number":["No. BE2020319"]}]},{"name":"the Yangzhou University Interdisciplinary Research Foundation for Crop Science Discipline of Targeted Support","award":["No. yzuxk202007"],"award-info":[{"award-number":["No. yzuxk202007"]}]},{"name":"the Yangzhou University Interdisciplinary Research Foundation for Crop Science Discipline of Targeted Support","award":["No. yzuxk202008"],"award-info":[{"award-number":["No. yzuxk202008"]}]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["No. 32071902"],"award-info":[{"award-number":["No. 32071902"]}]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["No. BE2020319"],"award-info":[{"award-number":["No. BE2020319"]}]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["No. yzuxk202007"],"award-info":[{"award-number":["No. yzuxk202007"]}]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["No. yzuxk202008"],"award-info":[{"award-number":["No. yzuxk202008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1\/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R2 of 0.878 and RMSE of 0.773 for the validation set 1, and R2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.<\/jats:p>","DOI":"10.3390\/rs14122777","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression Algorithm"],"prefix":"10.3390","volume":"14","author":[{"given":"Shu","family":"Ji","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Gu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8354-7014","authenticated-orcid":false,"given":"Xiaobo","family":"Xi","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Hong","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyang","family":"Huo","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changwei","family":"Tan","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops\/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China\/Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs\/Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology\/Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, K., Gong, Y., Fang, S., Duan, B., Yuan, N., Peng, Y., Wu, X., and Zhu, R. (2021). Combining spectral and texture features of UAV images for the remote estimation of rice LAI throughout the entire growing season. Remote Sens., 13.","DOI":"10.3390\/rs13153001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-UI-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2006.09.037","article-title":"LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages","volume":"112","author":"Yao","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhou, G., Song, X., and He, Q. (2022). Dynamic characteristics of canopy and vegetation water content during an entire maize growing season in relation to spectral-based indices. Remote Sens., 14.","DOI":"10.3390\/rs14030584"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Olson, M.B., Crawford, M.M., and Vyn, T.J. (2022). Hyperspectral indices for predicting nitrogen use efficiency in maize hybrids. Remote Sens., 14.","DOI":"10.3390\/rs14071721"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Prasad, N., Semwal, M., and Kalra, A. (2022). Hyperspectral vegetation indices offer insights for determining economically optimal time of harvest in Mentha arvensis. Ind. Crops Prod., 180.","DOI":"10.1016\/j.indcrop.2022.114753"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"891","DOI":"10.13031\/aea.12903","article-title":"Classification of sugar beets based on hyperspectral and extreme learning machine methods","volume":"34","author":"Yang","year":"2018","journal-title":"Appl. Eng. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"El-Hendawy, S., Al-Suhaibani, N., Mubushar, M., Tahir, M.U., Marey, S., Refay, Y., and Tola, E. (2022). Combining hyperspectral reflectance and multivariate regression models to estimate plant biomass of advanced spring wheat lines in diverse phenological stages under salinity conditions. Appl. Sci., 12.","DOI":"10.3390\/app12041983"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, J., Zhang, J., Fan, Y., Cheng, Y., Wang, B., Wu, X., Tan, X., Tan, T., and Li, S. (2021). Predicting grain yield and protein content using canopy reflectance in maize grown under different water and nitrogen levels. Field Crops Res., 260.","DOI":"10.1016\/j.fcr.2020.107988"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gu, X., Wang, L., Song, X., and Xu, X. (2016, January 26\u201329). Estimating Leaf Nitrogen Accumulation in Maize Based on Canopy Hyperspectrum Data. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, Edinburgh, UK.","DOI":"10.1117\/12.2241152"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, J., Ni, J., Wang, D., Deng, L., Li, J., and Han, Z. (2020). Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging. Spectrochim. Acta A, 234.","DOI":"10.1016\/j.saa.2020.118269"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.proeng.2017.01.202","article-title":"Wheat hardness prediction research based on NIR hyperspectral analysis combined with ant colony optimization algorithm","volume":"174","author":"Zhang","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_13","first-page":"2556","article-title":"Chlorophyll content estimation of northeast japonica rice based on improved feature band selection and hybrid integrated modeling","volume":"41","author":"Liu","year":"2021","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J., Sun, L., Feng, G., Bai, H., Yang, J., Gai, Z., Zhao, Z., and Zhang, G. (2022). Intelligent detection of hard seeds of snap bean based on hyperspectral imaging. Spectrochim. Acta A, 275.","DOI":"10.1016\/j.saa.2022.121169"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhou, Q., Shang, J., Liu, C., Zhuang, T., Ding, J., Xian, Y., Zhao, L., Wang, W., and Zhou, G. (2021). UAV- and machine learning-based retrieval of wheat SPAD values at the overwintering stage for variety screening. Remote Sens., 13.","DOI":"10.3390\/rs13245166"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, T., Gao, M., Cao, C., You, J., Zhang, X., and Shen, L. (2022). Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data. Comput. Electron. Agric., 193.","DOI":"10.1016\/j.compag.2022.106728"},{"key":"ref_17","first-page":"75","article-title":"Visible and near-infrared spectroscopy with multi-parameters optimization of Savitzky-Golay smoothing applied to rapid analysis of soil cr content of pearl river delta","volume":"9","author":"Shi","year":"2021","journal-title":"J. Geogr. Environ. Protect."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, S., Hu, T., Luo, L., He, Q., Zhang, S., Li, M., Cui, X., and Li, H. (2020). Rapid estimation of leaf nitrogen content in apple-trees based on canopy hyperspectral reflectance using multivariate methods. Infrared Phys. Technol., 111.","DOI":"10.1016\/j.infrared.2020.103542"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, J., Yang, W., Zhang, M., Feng, M., Xiao, L., and Ding, G. (2021). Estimation of water content in corn leaves using hyperspectral data based on fractional order Savitzky-Golay derivation coupled with wavelength selection. Comput. Electron. Agric., 182.","DOI":"10.1016\/j.compag.2021.105989"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhang, Q., Yi, X., Ma, L., Zhang, L., Huang, C., Zhang, Z., and Lv, X. (2022). Estimation of cotton leaf area index (LAI) based on spectral transformation and vegetation index. Remote Sens., 14.","DOI":"10.3390\/rs14010136"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, Z.-H., Wang, L.-Y., Yang, Z.-Q., Zhang, Y.-Y., Li, X., Song, L., He, L., Duan, J.-Z., and Feng, W. (2022). Hyperspectral monitoring of powdery mildew diease severity in wheat based on machine learning. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.828454"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, S., Zhou, K., Ding, R., Cheng, Y., and Jiang, G. (2022). Estimation of soil copper content based on fractional-order derivative spectroscopy and spectral characteristic band selection. Spectrochim. Acta A, 275.","DOI":"10.1016\/j.saa.2022.121190"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Saldanha","year":"2001","journal-title":"Chemometr. Intell. Lab."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1112\/plms\/s1-20.1.297","article-title":"On the generalised equations of elasticity, and their application to the wave theory of light","volume":"s1-20","author":"Pearson","year":"1888","journal-title":"Lond. Math. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","article-title":"New vegetation index and its application in estimating leaf area index of rice","volume":"14","author":"Wang","year":"2007","journal-title":"Rice Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.2134\/agronj2011.0202","article-title":"Estimating rice grain yield potential using normalized difference vegetation index","volume":"103","author":"Harrell","year":"2011","journal-title":"Agron. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3663","DOI":"10.1080\/014311699211264","article-title":"Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation","volume":"20","author":"Adams","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0034-4257(02)00011-1","article-title":"Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data","volume":"81","author":"Serrano","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11648\/j.sjams.20210906.12","article-title":"Effect of multicollinearity on variable selection in multiple regression","volume":"9","author":"Etaga","year":"2021","journal-title":"Sci. J. Appl. Math. Stat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11629-021-6988-8","article-title":"Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: Using logistic regression","volume":"19","author":"Abeysiriwardana","year":"2022","journal-title":"J. Mt. Sci-Engl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/09720502.2010.10700699","article-title":"Collinearity diagnostics of binary logistic regression model","volume":"13","author":"Midi","year":"2010","journal-title":"J. Interdiscip. Math."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ivanda, A., \u0160eri\u0107, L., Bugari\u0107, M., and Braovi\u0107, M. (2021). Mapping chlorophyll-a concentrations in the Ka\u0161tela Bay and Bra\u010d Channel using ridge regression and Sentinel-2 satellite images. Electronics, 10.","DOI":"10.3390\/electronics10233004"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hssaini, L., Razouk, R., and Bouslihim, Y. (2022). Rapid prediction of fig phenolic acids and flavonoids using mid-infrared spectroscopy combined with partial least square regression. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.782159"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s12665-022-10307-x","article-title":"Prediction of soil heavy metal concentrations in copper tailings area using hyperspectral reflectance","volume":"81","author":"Yang","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Schmitz, P.K., and Kandel, H.J. (2021). Using canopy measurements to predict soybean seed yield. Remote Sens., 13.","DOI":"10.3390\/rs13163260"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3850","DOI":"10.1080\/01431161.2021.1883201","article-title":"Exploring the potential of canopy reflectance spectra for estimating organic carbon content of aboveground vegetation in coastal wetlands","volume":"42","author":"Cheng","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sapes, G., Lapadat, C., Schweiger, A.K., Juzwik, J., Montgomery, R., Gholizadeh, H., Townsend, P.A., Gamon, J.A., and Cavender-Bares, J. (2022). Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination. Remote Sens. Environ., 273.","DOI":"10.1016\/j.rse.2022.112961"},{"key":"ref_43","first-page":"284","article-title":"Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance","volume":"8","author":"Panigrahi","year":"2021","journal-title":"Inf. Process. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1186\/s13007-019-0507-8","article-title":"Remote estimation of rice LAI based on Fourier spectrum texture from UAV image","volume":"15","author":"Duan","year":"2019","journal-title":"Plant Methods"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shi, Y., Gao, Y., Wang, Y., Luo, D., Chen, S., Ding, Z., and Fan, K. (2022). Using unmanned aerial vehicle-based multispectral image data to monitor the growth of intercropping crops in tea plantation. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.820585"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., Ma, C., Ding, F., Li, Y., Chen, W., Li, J., and Xiao, Z. (2021). Hyperspectral estimation of winter wheat leaf area index based on continuous wavelet transform and fractional order differentiation. Sensors, 21.","DOI":"10.3390\/s21248497"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xing, N., Huang, W., Dong, Y., Ye, H., Pignatti, S., Laneve, G., and Casa, R. (2020). Estimation of winter wheat leaf area index at different growth stages using optimized red-edge hyperspectral vegetation indices. IOP Conf. Ser. Earth Environ. Sci., 509.","DOI":"10.1088\/1755-1315\/509\/1\/012027"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, Z., Jia, K., Xiao, C., Wei, D., Zhao, X., Lan, J., Wei, X., Yao, Y., Wang, B., and Sun, Y. (2020). Leaf area index estimation algorithm for GF-5 hyperspectral data based on different feature selection and machine learning methods. Remote Sens., 12.","DOI":"10.3390\/rs12132110"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhang, G., Hao, H., Wang, Y., Jiang, Y., Shi, J., Yu, J., Cui, X., Li, J., Zhou, S., and Yu, B. (2021). Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy. Spectrochim. Acta A, 263.","DOI":"10.1016\/j.saa.2021.120187"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, T., Xu, T., Yu, F., Yuan, Q., Guo, Z., and Xu, B. (2021). A method combining ELM and PLSR (ELM-P) for estimating chlorophyll content in rice with feature bands extracted by an improved ant colony optimization algorithm. Comput. Electron. Agr., 186.","DOI":"10.1016\/j.compag.2021.106177"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1007\/s11119-021-09804-z","article-title":"Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling","volume":"22","author":"Guo","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xie, S., Ding, F., Chen, S., Wang, X., Li, Y., and Ma, K. (2022). Prediction of soil organic matter content based on characteristic band selection method. Spectrochim. Acta A, 273.","DOI":"10.1016\/j.saa.2022.120949"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"881","DOI":"10.3103\/S1068373921120104","article-title":"Estimation of winter wheat yield using the principal component analysis based on the integration of satellite and ground information","volume":"46","author":"Kleshchenko","year":"2021","journal-title":"Russ. Meteorol. Hydrol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Peron-Danaher, R., Russell, B., Cotrozzi, L., Mohammadi, M., and Couture, J.J. (2021). Incorporating multi-scale, spectrally detected nitrogen concentrations into assessing nitrogen use efficiency for winter wheat breeding populations. Remote Sens., 13.","DOI":"10.3390\/rs13193991"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A.M., Sharma, E., Jui, S.J., Deo, R.C., Nguyen-Huy, T., and Ali, M. (2022). Kernel ridge regression hybrid method for wheat yield prediction with satellite-derived predictors. Remote Sens., 14.","DOI":"10.3390\/rs14051136"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2777\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:08Z","timestamp":1760138828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122777"],"URL":"https:\/\/doi.org\/10.3390\/rs14122777","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}