Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.

[1]  S. T. Gower,et al.  Worldwide Historical Estimates of Leaf Area Index, 1932-2000 , 2001 .

[2]  Bing-Fang Wu,et al.  [Accuracy improvement of spectral classification of crop using microwave backscatter data]. , 2011, Guang pu xue yu guang pu fen xi = Guang pu.

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

[4]  Bernard Pinty,et al.  Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data , 1994 .

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

[6]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

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

[8]  Hongyan Zhang,et al.  Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the PROSAIL model from Landsat 8 OLI data , 2016 .

[9]  D. Roberts,et al.  A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper , 2004 .

[10]  Ruben Van De Kerchove,et al.  Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Álvaro Moreno-Martínez,et al.  Numerical Terradynamic Simulation Group 7-2018 Global Estimation of Biophysical Variables from Google Earth Engine Platform , 2018 .

[12]  F. Baret,et al.  TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects On LAI And APAR Estimation , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[13]  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.

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

[15]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

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

[17]  Clement Atzberger,et al.  Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based LAI Estimation of Wheat, Sugar Beet, and Maize , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

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

[20]  F. Baret,et al.  GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops , 2010 .

[21]  Luis Alonso,et al.  Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation , 2011, IEEE Geoscience and Remote Sensing Letters.

[22]  Bunkei Matsushita,et al.  Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest , 2007, Sensors.

[23]  Quan Sun,et al.  ractional vegetation cover estimation in arid and semi-arid environments using J-1 satellite hyperspectral data , 2012 .

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

[25]  Jian Yang,et al.  Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis , 2012 .

[26]  M. Cho,et al.  Towards red‐edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data , 2008 .

[27]  Dehai Zhu,et al.  Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .

[28]  R. Tateishi,et al.  Relationships between percent vegetation cover and vegetation indices , 1998 .

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

[30]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

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

[32]  T. Nilson.  A theoretical analysis of the frequency of gaps in plant stands , 1971 .

[33]  Andrew K. Skidmore,et al.  Advances in remote sensing of vegetation function and traits , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[34]  F. Baret,et al.  Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies , 2002 .

[35]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[36]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

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

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

[39]  Enrico Cadau,et al.  SENTINEL-2 SEN2COR: L2A Processor for Users , 2016 .

[40]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[41]  Suhong Liu,et al.  Estimation of fractional vegetation cover using mean-based spectral unmixing method , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

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

[43]  M. Marqués,et al.  Vegetation cover reduces erosion and enhances soil organic carbon in a vineyard in the central Spain , 2013 .

[44]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[45]  K. McGwire,et al.  Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. , 2000 .

[46]  Patrick Hostert,et al.  Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques , 2014, Remote. Sens..

[47]  Yafit Cohen,et al.  SWIR-based spectral indices for assessing nitrogen content in potato fields , 2010 .

[48]  Yanhong Tang,et al.  A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems , 2017 .

[49]  M. Zribi,et al.  Derivation of wild vegetation cover density in semi-arid regions: ERS2/SAR evaluation , 2003 .

[50]  F. López-Granados,et al.  Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .

[51]  Xi Chen,et al.  A comparison of methods for estimating fractional vegetation cover in arid regions , 2011 .

[52]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[53]  Jindi Wang,et al.  Advanced remote sensing : terrestrial information extraction and applications , 2012 .

[54]  C. R. S. Filho,et al.  Investigating the capability of WorldView-3 superspectral data for direct hydrocarbon detection , 2016 .

[55]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[56]  Minghua Zhang,et al.  A review of vegetated buffers and a meta-analysis of their mitigation efficacy in reducing nonpoint source pollution. , 2009, Journal of environmental quality.

[57]  Anis Ben Ishak,et al.  Variable selection using support vector regression and random forests: A comparative study , 2016, Intell. Data Anal..

[58]  Guangjian Yan,et al.  Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[59]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[60]  Guangjian Yan,et al.  Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC) , 2015, Remote. Sens..

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

[62]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[63]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[64]  Emma Izquierdo-Verdiguier,et al.  Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[65]  Jingfeng Xiao,et al.  A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .

[66]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[67]  R. Lacaze,et al.  Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation , 2002 .

[68]  F. Boochs,et al.  Shape of the red edge as vitality indicator for plants , 1990 .

[69]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[70]  Tao Jiang,et al.  Comparison of fractional vegetation cover estimating methods using in-situ measurements and the PROSAIL model , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[71]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

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

[73]  Xiaotong Zhang,et al.  A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data , 2017, Remote. Sens..

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

[75]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[76]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[77]  Keith D. Shepherd,et al.  Rapid characterization of Organic Resource Quality for Soil and Livestock Management in Tropical Agroecosystems Using Near Infrared Spectroscopy. , 2003 .

[78]  P. Atkinson,et al.  Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .

[79]  U. Grömping.  Dependence of Variable Importance in Random Forests on the Shape of the Regressor Space , 2009 .

[80]  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 .

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

[82]  Heather Reese,et al.  Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[83]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.