Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the PROSAIL model from Landsat 8 OLI data

Abstract. Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. Dimidiate pixel models and physical models are widely used to estimate FVC. Six dimidiate pixel models based on different vegetation indices (VI) and four look-up table (LUT) methods were compared to estimate FVC from Landsat 8 OLI data. Comparisons with in situ FVC of steppe and corn showed that the model proposed by Baret et al., which is based on the normalized difference vegetation index (NDVI), predicted FVC most accurately followed by Carlson and Ripley’s method. Gutman and Ignatov’s method overestimated FVC. Modified soil adjusted vegetation index (MSAVI) and the mixture of NDVI and RVI showed potential to replace NDVI in Gutman and Ignatov’s model, whereas the difference vegetation index (DVI) performed less well. At low vegetation cover, the LUT using reflectances to constrain the cost function performed better than LUTs using VI to constrain the cost function, whereas at high vegetation cover, the LUT based on NDVI estimated FVC most accurately. The applications of DVI and MSAVI to constrain the cost function also obtained improvement at high vegetation cover. Overall, the accuracies of LUT methods were a little lower than those of dimidiate pixel models.

[1]  E. Small,et al.  The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI , 2005 .

[2]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

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

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

[5]  T. Carlson,et al.  Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models , 1995 .

[6]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[7]  J. Clevers,et al.  The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches , 1995 .

[8]  Wei Guo,et al.  Real‐time weekly global green vegetation fraction derived from advanced very high resolution radiometer‐based NOAA operational global vegetation index (GVI) system , 2010 .

[9]  R. DeFries,et al.  Derivation and Evaluation of Global 1-km Fractional Vegetation Cover Data for Land Modeling , 2000 .

[10]  J. Deardorff.  Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[11]  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 .

[12]  Fei Li,et al.  Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China , 2014, Remote. Sens..

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

[14]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[15]  Antonio J. Plaza,et al.  Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area , 2009, Sensors.

[16]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[17]  Tao Jiang,et al.  Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent , 2015, Remote. Sens..

[18]  Lei Tian,et al.  CLASSIFICATION OF BROADLEAF AND GRASS WEEDS USING GABOR WAVELETS AND AN ARTIFICIAL NEURAL NETWORK , 2003 .

[19]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[20]  R. Houborg,et al.  Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data , 2007 .

[21]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[22]  J. Hogg.  Quantitative remote sensing of land surfaces , 2004 .

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

[24]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

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

[26]  Frédéric Baret,et al.  Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data , 2016 .

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

[28]  M. Weiss,et al.  Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data , 2002 .

[29]  Ryutaro Tateishi,et al.  Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers , 2012, Remote. Sens..

[30]  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.

[31]  A. Skidmore,et al.  Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model , 2012 .

[32]  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.

[33]  J. Qia,et al.  Spatial and temporal dynamics of vegetation in the San Pedro River basin area , 2000 .

[34]  L. Dini,et al.  Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content , 2008 .

[35]  Bo-Hui Tang,et al.  Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Roshanak Darvishzadeh,et al.  Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  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 .

[38]  Gail P. Anderson,et al.  Analysis of Hyperion data with the FLAASH atmospheric correction algorithm , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[39]  Baohui Zhang,et al.  Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China , 2014, Remote. Sens..

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

[41]  Eric E. Small,et al.  A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions , 2007 .