Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach

Optical remote sensing provides information on important vegetation variables such as leaf area index (LAI), biomass, and chlorophyll content. In this study, rice crops, which are rarely studied, were selected because of their high economic importance and the role they play in food security in the study area. The aim was to obtain a reliable estimate of canopy chlorophyll content as an important indicator for the evaluation of the plant status. PROSAIL radiative transfer model and the multispectral image data of ALOS AVNIR-2 were used. A field campaign was carried out in July 2010 in the northern part of Iran, Amol. Sixty sample plots of 20 × 20 m-2 were randomly selected, and their chlorophyll content was measured using a SPAD-502 chlorophyll meter. The PROSAIL was inverted using a lookup-table (LUT) approach. The LUTs were generated in different sizes. The effect of the LUT size on the retrieval accuracy of the canopy's chlorophyll content was studied using analysis of variance (ANOVA). The outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The obtained results demonstrate the ability of PROSAIL to estimate rice plant chlorophyll content using ALOS AVNIR-2 multispectral data (R2= 0.65; RMSE = 0.45). The results also confirmed the usefulness of such an approach for crop monitoring and ecological applications.

[1]  Clement Atzberger,et al.  Geostatistical regularization of inverse models for the retrieval of vegetation biophysical variables , 2009, Remote Sensing.

[2]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[3]  S. Liang,et al.  A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies , 2005 .

[4]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[5]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[6]  W. Verhoef.  Earth observation modelling based on layer scattering matrices , 1984 .

[7]  Linzhang Yang,et al.  Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance , 2009 .

[8]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[9]  A. Kuusk.  The Hot Spot Effect in Plant Canopy Reflectance , 1991 .

[10]  F. M. Danson,et al.  Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .

[11]  H. Gausman,et al.  Interaction of Isotropic Light with a Compact Plant Leaf , 1969 .

[12]  G. Carter.  Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[13]  J. Hill,et al.  Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .

[14]  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 .

[15]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[16]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[17]  Emile S. Gardiner,et al.  Modeling the relationship between extractable chlorophyll and SPAD-502 readings for endangered plant species research , 2009 .

[18]  R. Houborg,et al.  Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data , 2008 .

[19]  N. Goel,et al.  Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .

[20]  C. Atzberger.  Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models , 2004 .

[21]  F. Baret,et al.  Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems , 1996 .

[22]  Martha C. Anderson,et al.  Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale , 2009 .

[23]  G. D’Urso,et al.  Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize , 2009 .

[24]  Frédéric Baret,et al.  Crop biomass evaluation using radiometric measurements , 1989 .

[25]  Wouter A. Dorigo,et al.  Improving the Robustness of Cotton Status Characterisation by Radiative Transfer Model Inversion of Multi-Angular CHRIS/PROBA Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  M. Schlerf,et al.  Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data , 2006 .

[27]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[28]  Liangyun Liu,et al.  Detection of Vegetation Light-Use Efficiency Based on Solar-Induced Chlorophyll Fluorescence Separated From Canopy Radiance Spectrum , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Clement Atzberger,et al.  Retrieval of wheat bio - physical attributes from hyperspectral data and SAILH + PROSPECT radiative transfer model , 2003 .

[30]  Wouter Dorigo,et al.  Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT + SAIL , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[31]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[32]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[33]  Karen M. Barry,et al.  Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT , 2009 .

[34]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[35]  Z. Malenovský,et al.  Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. , 2009, Journal of experimental botany.

[36]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Iolanda Filella,et al.  Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought , 2004 .

[38]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

[39]  Ranga B. Myneni,et al.  Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies , 1998 .

[40]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[41]  S. Liang.  Quantitative Remote Sensing of Land Surfaces , 2003 .

[42]  David P. Miller,et al.  Status of atmospheric correction using a MODTRAN4-based algorithm , 2000, SPIE Defense + Commercial Sensing.

[43]  F. Baret,et al.  Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model , 2005 .

[44]  F. L. Dimet,et al.  Multitemporal-patch ensemble inversion of coupled surface-atmosphere radiative transfer models for land surface characterization , 2008 .

[45]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[46]  Jennifer L. Dungan,et al.  Seasonal LAI in slash pine estimated with landsat TM , 1992 .

[47]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[48]  G. Asner.  Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[49]  R. Colombo,et al.  Retrieval of leaf area index in different vegetation types using high resolution satellite data , 2003 .

[50]  P.J. Zarco-Tejada,et al.  Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[51]  A. Skidmore,et al.  Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models , 2011 .

[52]  G. Suits.  The calculation of the directional reflectance of a vegetative canopy , 1971 .

[53]  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 .

[54]  Clement Atzberger,et al.  Retrieval of wheat biophysical attributes from hyperspectral data and SAILH + PROSPECT radiative transfer model , 2003 .

[55]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

[56]  Susan L. Ustin,et al.  Remote sensing of wetland conditions in West Coast salt marshes , 2004, SPIE Optics + Photonics.