Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy

ABSTRACT The fraction of absorbed photosynthetically active radiation (FPAR) by the vegetation canopy (FPARcanopy) is an important parameter for vegetation productivity estimation using remote-sensing data. FPARcanopy is widely estimated using many different spectral vegetation indices (VIs), especially the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI). However, there have been few studies into which VIs are most suitable for this estimation or into their sensitivities to the leaf area index and the observation geometry of remote-sensing data, which are very important for the accurate estimation of FPARcanopy based on the plant growth stage and satellite imagery. In this study, nine main VIs calculated from field-measured spectra were evaluated and it was found that the SR and NDVI underestimated and overestimated FPARcanopy, respectively. It was also found that the enhanced vegetation index produced lesser errors and a higher agreement than other broadband VIs used to estimate FPARcanopy. Among all the selected VIs, the photochemical reflectance index (PRI) turned out to have the lowest root mean square error of 0.17. The SR produced the highest errors (about 0.37) and lowest index of agreement (about 0.50) compared to the measured values of FPARcanopy. Except for carotenoid reflectance index (CRI), FPARcanopy estimated by VIs are evidently sensitive to the leaf area index (LAI), especially for FPARcanopy (SR), which are also most sensitive to solar zenith angles (SZA). SR, CRI, PRI, and EVI have remarked variations with view zenith angles. Our study shows that FPARcanopy can be simply and accurately estimated using the most suitable VIs – i.e. EVI and PRI – with broadband and hyperspectral remote-sensing data, respectively, and that the nadir reflectance or nadir bidirectional reflectance distribution function adjusted reflectance should be used to calculate these VIs.

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

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

[3]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[4]  M. Bauer,et al.  Spectral estimators of absorbed photosynthetically active radiation in corn canopies. , 1985 .

[5]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[6]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[7]  B. Pinty,et al.  A simple bidirectional reflectance model for terrestrial surfaces , 1986 .

[8]  P. Sellers.  Canopy reflectance, photosynthesis, and transpiration. II. the role of biophysics in the linearity of their interdependence , 1987 .

[9]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[10]  C. Daughtry,et al.  Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies , 1992 .

[11]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

[12]  P. Pinter.  Solar angle independence in the relationship between absorbed PAR and remotely sensed data for Alfalfa , 1993 .

[13]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[14]  A. Huete,et al.  Dependence of NDVI and SAVI on sun/sensor geometry and its effect on fAPAR relationships in Alfalfa , 1995 .

[15]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[16]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[17]  Jing M. Chen,et al.  Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests , 1996, IEEE Trans. Geosci. Remote. Sens..

[18]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[19]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[20]  S. Goward,et al.  Vegetation canopy PAR absorptance and NDVI: An assessment for ten tree species with the SAIL model , 1997 .

[21]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[22]  G. Asner,et al.  Scaling PAR absorption from the leaf to landscape level in spatially heterogeneous ecosystems , 1997 .

[23]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[24]  J. Gamon,et al.  The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels , 1997, Oecologia.

[25]  T. Eck,et al.  Absorbed photosynthetically active radiation and sun‐view geometry effects on remote sensing relationships∗ , 1998 .

[26]  C. V. M. Bartona,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index Model and sensitivity analysis , 2000 .

[27]  C. Tucker,et al.  A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .

[28]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[29]  S. Verma,et al.  Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption , 2002 .

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

[31]  A. Gitelson,et al.  Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy¶ , 2002, Photochemistry and photobiology.

[32]  A. Viña,et al.  Remote estimation of leaf area index and green leaf biomass in maize canopies , 2003 .

[33]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[34]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[35]  R. Dickinson,et al.  Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model , 2004 .

[36]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

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

[38]  A. Viña,et al.  New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops , 2005 .

[39]  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 .

[40]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[41]  S. Piao,et al.  Changes in vegetation net primary productivity from 1982 to 1999 in China , 2005 .

[42]  L. Ji,et al.  Performance evaluation of spectral vegetation indices using a statistical sensitivity function , 2007 .

[43]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[44]  John R. Miller,et al.  Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model , 2010 .

[45]  Abd. Rahman As-syakur,et al.  Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban Area , 2010, Remote. Sens..

[46]  K. Weber,et al.  Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho , 2010 .

[47]  Dailiang Peng,et al.  Detection and estimation of mixed paddy rice cropping patterns with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[48]  J. G. Lyon,et al.  Hyperspectral Remote Sensing of Vegetation , 2011 .

[49]  Jiahua Zhang,et al.  Estimating fraction of photosynthetically active radiation of corn with vegetation indices and neural network from hyperspectral data , 2012, Chinese Geographical Science.

[50]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[51]  Bo-Cai Gao,et al.  Using EO-1 Hyperion to Simulate HyspIRI Products for a Coniferous Forest: The Fraction of PAR Absorbed by Chlorophyll $(\hbox{fAPAR}_{\rm chl})$ and Leaf Water Content (LWC) , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[52]  H. Fang,et al.  Characteristics and drivers of global NDVI‐based FPAR from 1982 to 2006 , 2012 .

[53]  Elizabeth M. Middleton,et al.  Variations of Foliage Chlorophyll fAPAR and Foliage Non-Chlorophyll fAPAR (fAPAR$_{\rm chl}$, fAPAR$_{\rm non\mathchar "702D chl}$) at the Harvard Forest , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[54]  Jinwei Dong,et al.  Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought , 2015 .

[55]  D. Lamb,et al.  The impact of solar illumination angle when using active optical sensing of NDVI to infer fAPAR in a pasture canopy , 2015 .

[56]  H. Fang,et al.  Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground‐based estimates over six FLUXNET sites , 2015 .

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

[58]  Bingfang Wu,et al.  Sensitivity analysis of retrieving fraction of absorbed photosynthetically active radiation (FPAR) using remote sensing data , 2016 .

[59]  David W. Lamb,et al.  Trigonometric correction factors renders the fAPAR-NDVI relationship from active optical reflectance sensors insensitive to solar elevation angle , 2016, Comput. Electron. Agric..

[60]  Chaoyang Wu,et al.  Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States , 2017 .

[61]  L. Ji.  Performance evaluation of spectral vegetation indices using a statistical sensitivity function , 2017 .