Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models

ISPRS Journal of Photogrammetry and Remote Sensing - Tập 150 - Trang 185-196 - 2019
X.Q. Xu1, J.S. Lu1, N. Zhang1, T.C. Yang1, J.Y. He1, X. Yao1, T. Cheng1, Y. Zhu1, W.X. Cao1, Y.C. Tian1
1National Engineering and Technology Center for Information Agriculture, Key Laboratory of Crop System Analysis and Decision Making, Minstry of Agriculture and Rural Affairs, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, PR China

Tài liệu tham khảo

Allen, 1969, Interaction of isotropic light with a compact plant leaf, J. Opt. Soc. Am., 59, 1376, 10.1364/JOSA.59.001376 Ali, 2016, Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest, Int. J. Appl. Earth Obs., 45, 66, 10.1016/j.jag.2015.11.004 Annandale, 2004, Two-dimensional solar radiation interception model for hedgerow fruit trees, Agr. Forest Meteorol., 121, 207, 10.1016/j.agrformet.2003.08.004 Atzberger, 2004, Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models, Remote Sens. Environ., 93, 53, 10.1016/j.rse.2004.06.016 Atzberger, 2010, Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat, Comput. Electron. Agr., 73, 165, 10.1016/j.compag.2010.05.006 Barry, 2009, Estimation of chlorophyll content in eucalyptus globulus foliage with the leaf reflectance model PROSPECT, Agr. Forest Meteorol., 149, 1209, 10.1016/j.agrformet.2009.01.005 Chen, 1992, Defining leaf area index for non-flat leaves, Plant Cell Environ., 15, 421, 10.1111/j.1365-3040.1992.tb00992.x Cheuk, 1997, Structured arc reversal and simulation of dynamic probabilistic networks Chitsazan, 2015, Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging, J. Hydrol., 528, 52, 10.1016/j.jhydrol.2015.06.007 Colomina, 2014, Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm., 92, 79, 10.1016/j.isprsjprs.2014.02.013 Combal, 2003, Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem, Remote Sens. Environ., 84, 1, 10.1016/S0034-4257(02)00035-4 Croft, 2014, The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures, Ecol. Complex., 17, 119, 10.1016/j.ecocom.2013.11.005 Darvishzadeh, 2011, Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models, ISPRS J. Photogramm., 66, 894, 10.1016/j.isprsjprs.2011.09.013 Darvishzadeh, 2008, Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland, Remote Sens. Environ., 112, 2592, 10.1016/j.rse.2007.12.003 Datt, 1999, A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves, J. Plant Physiol., 154, 30, 10.1016/S0176-1617(99)80314-9 Delegido, 2013, A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems, Eur. J. Agron., 46, 42, 10.1016/j.eja.2012.12.001 Drury, 2017, A survey of the applications of bayesian networks in agriculture, Eng. Appl. Artif. Intel., 65, 29, 10.1016/j.engappai.2017.07.003 Duan, 2014, Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data, Int. J. Appl. Earth Obs., 26, 12, 10.1016/j.jag.2013.05.007 Gitelson, 2005, Remote estimation of canopy chlorophyll content in crops, Geophys. Res. Lett., 32, 93, 10.1029/2005GL022688 Hatfield, 2008, Application of spectral remote rensing for agronomic decisions, Agron. J., 100, 117, 10.2134/agronj2006.0370c Houborg, 2009, Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale, Remote Sens. Environ., 113, 259, 10.1016/j.rse.2008.09.014 Jacquemoud, 1990, PROSPECT: A model of leaf optical properties spectra, Remote Sens. Environ., 34, 75, 10.1016/0034-4257(90)90100-Z Jacquemoud, 2009, PROSPECT+SAIL models: A review of use for vegetation characterization, Remote Sens. Environ., 113, S56, 10.1016/j.rse.2008.01.026 Jay, 2017, Estimating leaf chlorophyll content in sugar beet canopies using millimeter to centimeter-scale reflectance imagery, Remote Sens. Environ., 198, 173, 10.1016/j.rse.2017.06.008 Jay, 2017, Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping, Field Crop. Res., 210, 33, 10.1016/j.fcr.2017.05.005 Jeffreys, 1946, An invariant form for the prior probability in estimation problems, Proc. Royal Soc. London, 186, 453, 10.1098/rspa.1946.0056 Jin, 2018, A review of data assimilation of remote sensing and crop models, Eur. J. Agron., 92, 141, 10.1016/j.eja.2017.11.002 Jin, 2017, Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery, Remote Sens. Environ., 198, 105, 10.1016/j.rse.2017.06.007 Ju, 2010, Estimating leaf chlorophyll content using red edge parameters, Pedosphere, 20, 633, 10.1016/S1002-0160(10)60053-7 Jurdao, 2013, Regional estimation of woodland moisture content by inverting radiative transfer models, Remote Sens. Environ., 132, 59, 10.1016/j.rse.2013.01.004 Kalacska, 2005, Estimating leaf area index from satellite imagery using bayesian networks, IEEE T. Geosci. Remote, 43, 1866, 10.1109/TGRS.2005.848412 Koetz, 2005, Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics, Remote Sens. Environ., 95, 115, 10.1016/j.rse.2004.11.017 Kuusk, 1991, 139 Laurent, 2013, A Bayesian object-based approach for estimating vegetation biophysical and biochemical variables from APEX at-sensor radiance, Remote Sens. Environ., 139, 6, 10.1016/j.rse.2013.07.032 Li, 2016, Urban land use extraction from very high resolution remote sensing imagery using a Bayesian network, ISPRS J. Photogramm., 122, 192, 10.1016/j.isprsjprs.2016.10.007 Liang, 2015, Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method, Remote Sens. Environ., 165, 123, 10.1016/j.rse.2015.04.032 Lichtenthaler, 1983, Determinations of total carotenoids and chlorophyll a and b of leaf extracts in different solvents, Biochem. Soc. Trans., 603, 591, 10.1042/bst0110591 Merzlyak, 1997, Remote estimation of chlorophyll content in higher plant leaves, Int. J. Remote Sens., 18, 2691, 10.1080/014311697217558 Murphy, K., 1998. A brief introduction to graphical models and Bayesian networks. [https://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html]. Oliveira, 2019, Generating a hyperspectral digital surface model using a hyperspectral 2D frame camera, ISPRS J. Photogramm., 147, 345, 10.1016/j.isprsjprs.2018.11.025 Pearl, 1988 Qu, 2008, A Bayesian network algorithm for retrieving the characterization of land surface vegetation, Remote Sens. Environ., 112, 613, 10.1016/j.rse.2007.03.031 Quan, 2015, A bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval, IEEE T. Geosci. Remote, 53, 6507, 10.1109/TGRS.2015.2442999 Richter, 2009, Experimental assessment of the sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize, Can. J. Remote Sens., 35, 230, 10.5589/m09-010 Shachter, 1990, Evidence absorption and propagation through evidence reversals, Machine Intelligence Pattern Recognit., 10, 173, 10.1016/B978-0-444-88738-2.50021-X Sims, 2002, Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sens. Environ., 81, 337, 10.1016/S0034-4257(02)00010-X Suits, 1972, The calculation of the directional reflectance of a vegetative canopy, Remote Sens. Environ., 2, 117, 10.1016/0034-4257(71)90085-X Sun, 2018, Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval, ISPRS J. Photogramm., 135, 78, 10.1016/j.isprsjprs.2017.11.010 Tari, 1996, A Bayesian Network for predicting yield response of winter wheat to fungicide programmes, Comput. Electron. Agr., 15, 111, 10.1016/0168-1699(96)00011-7 Thompson, 2017, Airborne mapping of benthic reflectance spectra with bayesian linear mixtures, Remote Sens. Environ., 200, 18, 10.1016/j.rse.2017.07.030 Varvia, 2018, Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data, J. Quant Spectrosc. Radiat. Transf., 208, 19, 10.1016/j.jqsrt.2018.01.008 Verhoef, 1984, Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model, Remote Sens. Environ., 16, 125, 10.1016/0034-4257(84)90057-9 Verrelst, 2015, Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison, ISPRS J. Photogramm., 108, 260, 10.1016/j.isprsjprs.2015.04.013 Vohland, 2010, Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL, Int. J. Appl. Earth Obs., 12, 71, 10.1016/j.jag.2009.10.005 Wang, 2014, Sensitivity analysis of vegetation parameters based on PROSAIL model, Remote Sens. Technol. Appl., 2, 219 Weiss, 2000, Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data, Agronomie, 20, 3, 10.1051/agro:2000105 Yang, 2012, Classification of 10 m-resolution SPOT data using a combined Bayesian Network Classifier-shape adaptive neighborhood method, ISPRS J. Photogramm., 72, 36, 10.1016/j.isprsjprs.2012.05.011 Yao, 2008, LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages, Remote Sens. Environ., 112, 94, 10.1016/j.rse.2006.09.037 Zhang, 2012, Estimating leaf area index from MODIS and surface meteorological data using a dynamic bayesian network, Remote Sens. Environ., 127, 30, 10.1016/j.rse.2012.08.015 Zhao, 2018, Development and testing of an ear-leaf model for rice canopy reflectance, J. Appl. Remote Sens., 12, 1 Zhou, 2014, Development of a novel bidirectional canopy reflectance model for row-planted rice and wheat, Remote Sens., 6, 7632, 10.3390/rs6087632 Zhou, 2017, Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery, ISPRS J. Photogramm., 130, 246, 10.1016/j.isprsjprs.2017.05.003