{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:33:40Z","timestamp":1760402020777,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T00:00:00Z","timestamp":1578096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0504500"],"award-info":[{"award-number":["2018YFB0504500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801268"],"award-info":[{"award-number":["41801268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2018CFB272"],"award-info":[{"award-number":["2018CFB272"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["CUG170662"],"award-info":[{"award-number":["CUG170662"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spectra of reflectance (Sr) and fluorescence (Sf) are significant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced fluorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs*   \u03a6    + GSIs*   \u03a6   , where    \u03a6    is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in different ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the    \u03a6    value in NCIH-F formula. Simultaneously, the photochemical reflectance index (PRI) formed with 531 and 570 nm was modified based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the artificial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an efficient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.<\/jats:p>","DOI":"10.3390\/rs12010185","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T03:48:48Z","timestamp":1578282528000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice"],"prefix":"10.3390","volume":"12","author":[{"given":"Lin","family":"Du","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), 388, Lumo Road, Wuhan 430074, China"},{"name":"Artificial Intelligence School, Wuchang University of Technology, 16, Jiangxia Avenue, Wuhan 430223, China"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), 388, Lumo Road, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-7983","authenticated-orcid":false,"given":"Bowen","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129, Luoyu Road, Wuhan 430079, China"}]},{"given":"Jia","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), 388, Lumo Road, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6361-3005","authenticated-orcid":false,"given":"Biwu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129, Luoyu Road, Wuhan 430079, China"}]},{"given":"Shuo","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129, Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129, Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1256-2097","authenticated-orcid":false,"given":"Shalei","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, 30, Xiaohongshan West, Wuhan 430071, China"}]},{"given":"Wei","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129, Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, 129, Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,4]]},"reference":[{"key":"ref_1","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2007JG000676","article-title":"Nitrogen controls plant canopy light-use efficiency in temperate and boreal ecosystems","volume":"113","author":"Kergoat","year":"2008","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1111\/gcb.13981","article-title":"Increasing canopy photosynthesis in rice can be achieved without a large increase in water use-A model based on free-air CO2 enrichment","volume":"24","author":"Ikawa","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.rse.2017.05.019","article-title":"Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism","volume":"196","author":"Dechant","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s13593-012-0111-z","article-title":"Precision nitrogen management of wheat. A review","volume":"33","author":"Diacono","year":"2012","journal-title":"Agron. Sustain. Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00096-0","article-title":"Precision agriculture\u2014A worldwide overview","volume":"36","author":"Zhang","year":"2002","journal-title":"Comput. Electr. Agric."},{"key":"ref_7","first-page":"47","article-title":"Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels","volume":"25","author":"Schlemmer","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","first-page":"69","article-title":"Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index","volume":"67","author":"Delegido","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C., Nie, S., Xi, X., Luo, S., and Sun, X. (2016). Estimating the biomass of maize with hyperspectral and LiDAR data. Remote Sens., 9.","DOI":"10.3390\/rs9010011"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6770","DOI":"10.1021\/es070144e","article-title":"Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network","volume":"41","author":"Yi","year":"2007","journal-title":"Environ. Sci. Tech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"E185","DOI":"10.1073\/pnas.1210196109","article-title":"Hyperspectral remote sensing of foliar nitrogen content","volume":"110","author":"Knyazikhin","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.eja.2013.09.006","article-title":"Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression","volume":"52","author":"Li","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/S0034-4257(01)00226-7","article-title":"Nitrogen influence on fresh-leaf NIR spectra","volume":"78","author":"Johnson","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/S0034-4257(01)00182-1","article-title":"Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies","volume":"76","author":"Curran","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11119-011-9243-4","article-title":"Active crop sensor to detect variability of nitrogen supply and biomass on sugarcane fields","volume":"13","author":"Portz","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1080\/01431160110075622","article-title":"Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia","volume":"23","author":"Gong","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11119-010-9165-6","article-title":"Eevaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages","volume":"11","author":"Li","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_19","first-page":"512","article-title":"Research of new vegetation index for estimating crop canopy biomass","volume":"2","author":"Pengfei","year":"2010","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11104-013-1937-0","article-title":"Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice","volume":"376","author":"Tian","year":"2014","journal-title":"Plant Soil"},{"key":"ref_21","first-page":"136","article-title":"Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR","volume":"44","author":"Du","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2016.06.014","article-title":"Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence","volume":"232","author":"Liu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1111\/j.1365-2486.2007.01352.x","article-title":"Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?","volume":"13","author":"Grace","year":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"210","DOI":"10.17221\/73\/2014-PSE","article-title":"Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency","volume":"60","author":"Slamka","year":"2014","journal-title":"Plant Soil Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"150","DOI":"10.5589\/m07-022","article-title":"A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn","volume":"33","author":"Apostol","year":"2007","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","first-page":"7","article-title":"Chlorophyll fluorescence characteristics throughout spring triticale development stages as affected by fertilization","volume":"62","author":"Feiziene","year":"2012","journal-title":"Acta Agric. Scand. Sect. B-Soil Plant Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/0034-4257(94)90122-8","article-title":"Remote sensing vegetation status by laser-induced fluorescence","volume":"47","author":"Dahn","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0034-4257(94)90126-0","article-title":"Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in rice plants","volume":"47","author":"Subhash","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_29","first-page":"988","article-title":"Effect of nitrogen application rate on flag leaf chlorophyll fluorescence characteristics and yield in wheat under integration of water and fertilizer","volume":"38","author":"Sun","year":"2018","journal-title":"J. Triticeae Crop."},{"key":"ref_30","first-page":"108","article-title":"Detection of pepper leaves nitrogen contents in greenhouse based on chlorophyll fluorescence image","volume":"43","author":"Yang","year":"2017","journal-title":"J. Hunan Agric. Univ. (Nat. Sci.)"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"182","DOI":"10.17221\/7\/2015-PSE","article-title":"Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content","volume":"61","author":"Yang","year":"2015","journal-title":"Plant Soil Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jasper, J., Reusch, S., and Link, A. (2009, January 6\u20138). Active sensing of the N status of wheat using optimized wavelength combination: Impact of seed rate, variety and growth stage, in Precision Agriculture\u201909: Papers. Proceedings of the the 7th European Conference on Precision Agriculture, Wageningen, The Netherlands.","DOI":"10.3920\/9789086866649_003"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1016\/j.rse.2010.04.006","article-title":"New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat","volume":"114","author":"Chen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1364\/AO.23.000134","article-title":"Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation","volume":"23","author":"Chappelle","year":"1984","journal-title":"Appl. Opt."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/S0034-4257(00)00148-6","article-title":"Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation","volume":"74","author":"Miller","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/S0034-4257(00)00149-8","article-title":"Chlorophyll fluorescence effects on vegetation apparent reflectance: II. Laboratory and airborne canopy-level measurements with hyperspectral data","volume":"74","author":"Miller","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Du, S.S.L., Jian, Y., Jia, S., and Wei, G. (2016). Using different regression methods to estimate leaf nitrogen content in rice by fusing hyperspectral LiDAR data and laser-induced chlorophyll fluorescence data. Remote Sens., 8.","DOI":"10.3390\/rs8060526"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19354","DOI":"10.1364\/OE.24.019354","article-title":"Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra","volume":"24","author":"Jian","year":"2016","journal-title":"Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6389","DOI":"10.1364\/AO.40.006389","article-title":"Noninvasive measurement of fluorophore concentration in turbid media with a simple fluorescence\/reflectance ratio technique","volume":"40","author":"Weersink","year":"2001","journal-title":"Appl. Opt."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6539","DOI":"10.1364\/OE.25.006539","article-title":"Potential of spectral ratio indices derived from hyperspectral LiDAR and laser-induced chlorophyll fluorescence spectra on estimating rice leaf nitrogen contents","volume":"25","author":"Du","year":"2017","journal-title":"Opt. Express"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/LGRS.2005.844658","article-title":"Band selection based on feature weighting for classification of hyperspectral data","volume":"2","author":"Huang","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1080\/01431160110107743","article-title":"The role of feature selection in artificial neural network applications","volume":"23","author":"Kavzoglu","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/36.934069","article-title":"A new search algorithm for feature selection in hyperspectral remote sensing images","volume":"39","author":"Serpico","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.cageo.2012.03.008","article-title":"Sensitivity analysis for volcanic source modeling quality assessment and model selection","volume":"44","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1109\/TGRS.2011.2109390","article-title":"Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","unstructured":"Stocker, T., Qin, D., Plattner, G., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, B., and Midgley, B. (2013). IPCC, 2013: Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"29413","DOI":"10.1364\/OE.20.029413","article-title":"Reflectances from a supercontinuum laser-based instrument: Hyperspectral, polarimetric and angular measurements","volume":"20","author":"Ceolato","year":"2012","journal-title":"Opt. Express"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.agrformet.2014.08.018","article-title":"Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR","volume":"198","author":"Nevalainen","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"56932","DOI":"10.1039\/C5RA08166A","article-title":"Vegetation identification based on characteristics of fluorescence spectral spatial distribution","volume":"5","author":"Jian","year":"2015","journal-title":"RSC Adv."},{"key":"ref_50","first-page":"383","article-title":"A century of Kjeldahl\u2019s nitrogen determination","volume":"26","author":"Wutzke","year":"1984","journal-title":"Z. Fur Med. Lab."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2018.10.018","article-title":"Effect of environmental conditions on sun-induced fluorescence in a mixed forest and a cropland","volume":"219","author":"Damm","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"Yegnanarayana, B. (2009). Artificial Neural Networks, PHI Learning Pvt. Ltd."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"S29","DOI":"10.1080\/15476510.1988.10401466","article-title":"The role of chlorophyll fluorescence in the detection of stress conditions in plants","volume":"19","author":"Lichtenthaler","year":"1988","journal-title":"Crit. Rev. Anal. Chem."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.07.007","article-title":"Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves","volume":"168","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"Zarco-Tejada, P.J., Miller, J.R., Pedr\u00f3s, R., Verhoef, W., and Berger, M. (2004, January 17\u201319). FluorMODgui: A graphic user interface for the spectral simulation of leaf and canopy fluorescence effects. Proceedings of the 2nd International Workshop on Remote Sensing of Vegetation Fluorescence, Montreal, QC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.5194\/bg-6-3109-2009","article-title":"An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance","volume":"6","author":"Verhoef","year":"2009","journal-title":"Biogeosciences"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.rse.2018.02.029","article-title":"Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance","volume":"209","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.rse.2016.09.017","article-title":"Fluspect-B: A model for leaf fluorescence, reflectance and transmittance spectra","volume":"186","author":"Vilfan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2018.05.013","article-title":"Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion","volume":"215","author":"Celesti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.04.012","article-title":"Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics","volume":"211","author":"Vilfan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1016\/j.scitotenv.2018.03.378","article-title":"Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK","volume":"634","author":"Muhammed","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 modis land-surface temperature\/emissivity.product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, M.M., Guojin, H., Zhaoming, Z., Guizhou, W., Zhengjia, Z., Xiaojie, C., Zhijie, W., and Xiuguo, L. (2017). Comparison of spatial interpolation and regression analysis models for an estimation of monthly near surface air temperature in China. Remote Sens., 9.","DOI":"10.3390\/rs9121278"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhengjia, Z., Chao, W., Hong, Z., Yixian, T., and Xiuguo, L. (2018). Analysis of permafrost region coherence variation in the Qinghai\u2013Tibet Plateau with a high-resolution TerraSAR-X image. Remote Sens., 10.","DOI":"10.3390\/rs10020298"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.agrformet.2012.04.006","article-title":"The variability of soil thermal and hydrological dynamics with vegetation cover in a permafrost region","volume":"162","author":"Genxu","year":"2012","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:18:52Z","timestamp":1760361532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,4]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010185"],"URL":"https:\/\/doi.org\/10.3390\/rs12010185","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,1,4]]}}}