Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data

Abstract With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R2 = 0.79, p

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

[2]  J. Qi,et al.  A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy , 2007 .

[3]  José F. Moreno,et al.  Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  J. Clevers,et al.  Estimating potato leaf chlorophyll content using ratio vegetation indices , 2016 .

[5]  Richard L. Thompson,et al.  A snapshot of canopy reflectance models and a universal model for the radiation regime , 2000 .

[6]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[7]  A. Gitelson,et al.  Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC) , 2015 .

[8]  S. Verzakov,et al.  Estimating grassland biomass using SVM band shaving of hyperspectral data , 2007 .

[9]  Xu Wang,et al.  Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods , 2016, Remote. Sens..

[10]  D. Arnon.  COPPER ENZYMES IN ISOLATED CHLOROPLASTS. POLYPHENOLOXIDASE IN BETA VULGARIS. , 1949, Plant physiology.

[11]  C. Woodcock,et al.  Measuring Gap Fraction, Element Clumping Index and LAI in Sierra Forest Stands Using a Full-Waveform Ground-Based Lidar , 2012 .

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

[13]  Jan G. P. W. Clevers,et al.  Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop , 2017, Remote. Sens..

[14]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[15]  IDENTIFYING SPECTRA IMPORTANT FOR PREDICTION OF SENESCENT GRASSLAND CANOPY STRUCTURE , 2012 .

[16]  T. Quaife,et al.  Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model , 2018, Remote Sensing of Environment.

[17]  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 .

[18]  Clement Atzberger,et al.  Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Clement Atzberger,et al.  Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture , 2008, Int. J. Appl. Earth Obs. Geoinformation.

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

[21]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Xing Li,et al.  A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[23]  R. Jensen,et al.  Estimating Urban Leaf Area Index (LAI) of Individual Trees with Hyperspectral Data , 2012 .

[24]  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 .

[25]  Jing M. Chen,et al.  Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods , 1995, IEEE Trans. Geosci. Remote. Sens..

[26]  Vinay Kumar Sehgal,et al.  Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements , 2016 .

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

[28]  S. T. Gower,et al.  Leaf area index of boreal forests: theory, techniques, and measurements , 1997 .

[29]  A. Gonsamo.  Normalized sensitivity measures for leaf area index estimation using three-band spectral vegetation indices , 2011 .

[30]  W. Verhoef,et al.  Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image , 2014 .

[31]  Luis Alonso,et al.  Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC) , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[32]  R. Houborg,et al.  Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop and grasslands in five European landscapes , 2012 .

[33]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

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

[35]  Wenjiang Huang,et al.  Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Qiang Liu,et al.  Inversion of a Radiative Transfer Model for Estimating Forest LAI From Multisource and Multiangular Optical Remote Sensing Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[38]  Xin Li,et al.  A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat , 2017 .

[39]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[40]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[41]  Gustau Camps-Valls,et al.  Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods , 2018, Surveys in Geophysics.

[42]  Holly Croft,et al.  Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework , 2015 .

[43]  S. Ustin,et al.  Estimation of tree canopy leaf area index by gap fraction analysis , 1993 .

[44]  Andres Kuusk,et al.  Comparison of measured boreal forest characteristics with estimates from TM data and limited ancillary information using reflectance model inversion , 2002 .

[45]  S. Kushwaha,et al.  Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data , 2014 .

[46]  N. Krishnayya,et al.  Canopy level estimations of chlorophyll and LAI for two tropical species (teak and bamboo) from Hyperion (EO1) data , 2013 .

[47]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[48]  Anatoly A. Gitelson,et al.  Nondestructive Estimation of Leaf Chlorophyll Content in Grapes , 2008, American Journal of Enology and Viticulture.

[49]  Dong Liang,et al.  Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  Damiano Gianelle,et al.  Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission , 2016, J. Sensors.

[51]  Xuejian Li,et al.  Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model , 2017 .

[52]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

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

[54]  S. Leblanc,et al.  Tracing Radiation and Architecture of Canopies TRAC MANUAL Version 2.1 , 2002 .

[55]  A. Huete,et al.  Estimating biophysical parameters of rice with remote sensing data using support vector machines , 2011, Science China Life Sciences.

[56]  Luis Alonso,et al.  Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[57]  Binbin He,et al.  Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data , 2016 .

[58]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[59]  Olga Sykioti,et al.  Band depth analysis of CHRIS/PROBA data for the study of a Mediterranean natural ecosystem. Correlations with leaf optical properties and ecophysiological parameters , 2011 .

[60]  Richard H. Waring,et al.  Forest Ecosystem Analysis at Multiple Time and Space Scales , 2007 .

[61]  Gary R. Watmough,et al.  Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation , 2013 .

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

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