Development of a New BRDF-Resistant Vegetation Index for Improving the Estimation of Leaf Area Index

The leaf area index (LAI) is one of the most important Earth surface parameters used in the modeling of ecosystems and their interaction with climate. Numerous vegetation indices have been developed to estimate the LAI. However, because of the effects of the bi-directional reflectance distribution function (BRDF), most of these vegetation indices are also sensitive to the effect of BRDF. In this study, we aim to present a new BRDF-resistant vegetation index (BRVI), which is sensitive to the LAI but insensitive to the effect of BRDF. Firstly, the BRDF effects of different bands were investigated using both simulated data and in-situ measurements of winter wheat made at different growth stages. We found bi-directional shape similarity in the solar principal plane between the green and the near-infrared (NIR) bands and between the blue and red bands for farmland soil conditions and with medium chlorophyll content level. Secondly, the consistency of the shape of the BRDF across different bands was employed to develop a new BRDF-resistant vegetation index for estimating the LAI. The reflectance ratios of the NIR band to the green band and the blue band to the red band were reasonably assumed to be resistant to the BRDF effects. Nevertheless, the variation amplitude of the bi-directional reflectance in the solar principal plane was different for different bands. The divisors in the two reflectance ratios were improved by combining the reflectances at the red and green bands. The new BRVI was defined as a normalized combination of the two improved reflectance ratios. Finally, the potential of the proposed BRVI for estimation of the LAI was evaluated using both simulated data and in-situ measurements and also compared to other popular vegetation indices. The results showed that the influence of the BRDF on the BRVI was the weakest and that the BRVI retrieved LAI values well, with a coefficient of determination (R2) of 0.84 and an RMSE of 0.83 for the field data and with an R2 of 0.97 and an RMSE of 0.25 for the simulated data. It was concluded, therefore, that the new BRVI is resistant to BRDF effect and is also promising for use in estimating the LAI.

[1]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[2]  Yuri Knyazikhin,et al.  Small-sclae study of three-dimensional distribution of photosynthetically active radiation in a forest , 1997 .

[3]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[4]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[5]  R. Lacaze,et al.  Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption , 2003 .

[6]  F. E. Nicodemus.  Directional Reflectance and Emissivity of an Opaque Surface , 1965 .

[7]  D. Diner,et al.  Surface albedo retrieval from Meteosat: 1. Theory , 2000 .

[8]  Hiroshi Matsuyama,et al.  Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures , 2010 .

[9]  Kenlo Nishida Nasahara,et al.  Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest , 2013 .

[10]  C. Tucker.  Remote sensing of leaf water content in the near infrared , 1980 .

[11]  Shunlin Liang,et al.  Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator , 2015 .

[12]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[13]  Zhihao Qin,et al.  Estimation of Crop LAI using hyperspectral vegetation indices and a hybrid inversion method , 2015 .

[14]  Wuming Zhang,et al.  A portable Multi-Angle Observation System , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Sandra Díaz,et al.  Specific leaf area and dry matter content estimate thickness in laminar leaves. , 2005, Annals of botany.

[16]  R. Jensen,et al.  The effect of urban leaf area on summertime urban surface kinetic temperatures: A Terre Haute case study , 2007 .

[17]  J. Harlan,et al.  Spectral estimation of Green leaf area index of oats , 1985 .

[18]  L. Alonso,et al.  A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems , 2013 .

[19]  D. Xie,et al.  LAI inversion algorithm based on directional reflectance kernels. , 2007, Journal of environmental management.

[20]  A. Gonsamo,et al.  The sensitivity based estimation of leaf area index from spectral vegetation indices , 2012 .

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

[22]  Maria Manuela Chaves,et al.  Sugar metabolism in developing lupin seeds is affected by a short-term water deficit. , 2005, Journal of experimental botany.

[23]  P. J. Curran,et al.  Multispectral remote sensing for the estimation of green leaf area index , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[24]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[25]  M. Dennett,et al.  Use of the Expolinear Growth Model to Analyse the Growth of Faba bean, Peas and Lentils at Three Densities: Predictive Use of the Model , 1998 .

[26]  N. Breda.  Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[27]  Roselyne Lacaze,et al.  Retrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument , 2002 .

[28]  Guangjian Yan,et al.  Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements , 2016, Remote. Sens..

[29]  Heather McNairn,et al.  International Journal of Applied Earth Observation and Geoinformation , 2014 .

[30]  A. Viña,et al.  Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .

[31]  H. Rahman,et al.  Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA advanced very high resolution radiometer data , 1993 .

[32]  Lee A. Vierling,et al.  Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study , 2005 .

[33]  Bing Zhang,et al.  Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[35]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[36]  V. N. Sridhar,et al.  Estimation and validation of LAI using physical and semi‐empirical BRDF models , 2008 .

[37]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[38]  S. Jacquemoud.  Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study , 1993 .