Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer
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M. Schaepman | Z. Malenovský | L. Homolová | R. Zurita-Milla | P. Lukeš | V. Kaplan | J. Hanus | J. Gastellu-Etchegorry | J. Hanuš
[1] John R. Miller,et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .
[2] Mathias Disney,et al. Monte Carlo ray tracing in optical canopy reflectance modelling , 2000 .
[3] Andrew K. Skidmore,et al. Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor , 2011 .
[4] John R. Miller,et al. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..
[5] K. Soudani,et al. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .
[6] Tomáš Polák,et al. Does the azimuth orientation of Norway spruce (Picea abies/L./Karst.) branches within sunlit crown part influence the heterogeneity of biochemical, structural and spectral characteristics of needles? , 2007 .
[7] T. Painter,et al. Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .
[8] P. Curran. Remote sensing of foliar chemistry , 1989 .
[9] G. Heinson,et al. Electrical evidence of continental accretion: Steeply‐dipping crustal‐scale conductivity contrast , 2006 .
[10] M. Schaepman,et al. Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval , 2010 .
[11] Moon S. Kim,et al. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .
[12] R. Myneni,et al. A Three-Dimensional Radiative Transfer Method for Optical Remote Sensing of Vegetated Land Surfaces , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.
[13] M. Schaepman,et al. Applicability of the PROSPECT model for Norway spruce needles , 2006 .
[14] Roberta E. Martin,et al. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .
[15] Jean-Luc Widlowski,et al. The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models , 2008 .
[16] R. Clark,et al. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .
[17] F. Baret,et al. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .
[18] E. Milton,et al. The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .
[19] A. Wellburn. The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution* , 1994 .
[20] Zuzana Lhotáková,et al. Measurement methods and variability assessment of the Norway spruce total leaf area: implications for remote sensing , 2013, Trees.
[21] V. Demarez,et al. Modeling radiative transfer in heterogeneous 3-D vegetation canopies , 1996 .
[22] Jadunandan Dash,et al. The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation , 2010 .
[23] Nadine Gobron,et al. Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption , 2006 .
[24] Luis Alonso,et al. Gridding Artifacts on Medium-Resolution Satellite Image Time Series: MERIS Case Study , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[25] S. Ustin,et al. Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .
[26] Shunlin Liang,et al. Earth system science related imaging spectroscopy — an assessment , 2009 .
[27] J. Clevers. Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .
[28] C. Willmott. ON THE VALIDATION OF MODELS , 1981 .
[29] Philip Lewis,et al. 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains , 2006 .
[30] R. Clark,et al. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data , 2003 .
[31] Michael E. Schaepman,et al. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .
[32] Giuseppina Rea,et al. Technological applications of chlorophyll a fluorescence for the assessment of environmental pollutants , 2011, Analytical and bioanalytical chemistry.
[33] D. Roberts,et al. Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data , 2011 .
[34] H. Scheer,et al. A Red-Shifted Chlorophyll , 2010, Science.
[35] R. Clark,et al. Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .
[36] Pauline Stenberg,et al. Simulations of the effects of shoot structure and orientation on vertical gradients in intercepted light by conifer canopies. , 1996, Tree physiology.
[37] M. Marek,et al. Test of Accuracy of LAI Estimation by LAI-2000 under Artificially Changed Leaf to Wood Area Proportions , 2000, Biologia Plantarum.
[38] A. Wellburn,et al. The spectral determination of chlorophyll a and chlorophyll b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. , 1994 .
[39] S. Ustin,et al. Mapping nonnative plants using hyperspectral imagery , 2003 .
[40] Jing M. Chen,et al. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery , 2008 .
[41] R. Richter,et al. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .
[42] Clement Atzberger,et al. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.
[43] D. Normile,et al. Round and round: a guide to the carbon cycle. , 2009, Science.
[44] J. Dungan,et al. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .
[45] C. Jordan. Derivation of leaf-area index from quality of light on the forest floor , 1969 .
[46] F. Baret,et al. PROSPECT: A model of leaf optical properties spectra , 1990 .
[47] Peter M. Atkinson,et al. The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India , 2010 .
[48] N. Goel,et al. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .
[49] A. Gitelson,et al. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .
[50] Jean-Philippe Gastellu-Etchegorry,et al. DART: a 3D model for simulating satellite images and studying surface radiation budget , 2004 .
[51] Yuri A. Gritz,et al. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.
[52] Yuri Knyazikhin,et al. Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .
[53] Olga Sykioti,et al. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations , 2010 .
[54] John R. Miller,et al. Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level , 2008 .
[55] R. Myneni. Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies , 1991 .
[56] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[57] J. Pisek,et al. Estimation of vegetation clumping index using MODIS BRDF data , 2011 .
[58] J. Grace,et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation , 2007 .
[59] A. Skidmore,et al. Spectral discrimination of vegetation types in a coastal wetland , 2003 .
[60] D S Kimes,et al. Radiative transfer model for heterogeneous 3-D scenes. , 1982, Applied optics.
[61] R. J. Porra,et al. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy , 1989 .
[62] A. Gitelson,et al. Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .
[63] N. Broge,et al. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .
[64] P. Stenberg,et al. A method to account for shoot scale clumping in coniferous canopy reflectance models , 2003 .
[65] Michael E. Schaepman,et al. Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution , 2008 .