Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency

Abstract

The Multi-angle Imaging SpectroRadiometer (MISR) instrument provides global imagery at nine discrete viewing angles and four visible/nearinfrared spectral bands. MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). This paper describes the research basis for transitioning the MISR LAI/FPAR products from provisional to validation status. The efforts included not only comparisons to field data but also analyses of relationships, consistency and complementarity between various MISR products derived by independent algorithms. For example, we show how the energy absorbed by the ground below vegetation can be estimated from two independent MISR products, FPAR and BHRPAR (bi-hemispheric reflectance at PAR wavelengths). Further, we show that this information can be used to derive at least three measures of canopy structure -Beer's law extinction coefficient, mean leaf inclination and the gap fraction or vegetation ground cover. The spatial and temporal coverage of the LAI/FPAR product is mainly limited by cloud contamination. However, when a successful aerosol retrieval is performed, typically 95% of pixels have surface reflectance retrievals suitable as input to the LAI/FPAR algorithm. The algorithm provides LAI/FPAR retrievals in 50-80% of these pixels with suitable input. The early versions of the algorithm overestimated LAI values in grasses and broadleaf crops. The MISR LAI product from the recalibrated algorithm (version 3.3) is assessed by comparison with field data collected in a 3 × 3 km agricultural area (grasses and cereal crops) near Avignon, France. LAI retrievals in other biomes are compared to MODIS LAI product of known accuracy. The MISR LAI product shows structural and phenological variability in agreement with data. Our results suggest that the product is accurate to within 0.66 LAI in herbaceous vegetation and savannas and is an overestimate by about 1 LAI in broadleaf forests. LAI retrievals over needle leaf forests remain at provisional quality level.

On the measurability of change in Amazon vegetation from MODIS

“…Nonetheless, sensitivity with respect to changes in vegetation still depends on the ability of individual bands to detect these changes. The increased sensitivity of EVI to bidirectional reflectance effects originates from the constant (empirical) weight factors present in the index (Moura, Galvão, dos Santos, Roberts, & Breunig, 2012): In case of a normalized difference index, such as NDVI, changes in forward and backscattering are less pronounced, particularly in dense vegetation (Hu et al, 2007), because changes in one band are expressed relative to changes in the other (Kaufmann et al, 2000). However, the directional effects in EVI are dominated by the absolute changes in the NIR band (because of the higher weight in the denominator compared to the nominator).…”

Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

“…Global biophysical variable data such as leaf area index (LAI), fractional vegetation cover (fCover), or fractional of photo-synthetically active radiation absorbed by the canopy (fAPAR) derived from MODIS, CYCLOPES, MISR, or GLOBCARBON have been developed (Ganguly et al 2008;Baret et al 2007;Hu et al 2007;Zhao and Popescu 2009) and made available to the scientific community. LAI is generally defined as the one-sided green leaf area per unit ground area in broadleaf canopies (Myneni et al 1997).…”

Multi-site evaluation of the JULES land surface model using global and local data

“…For offline simulations, the model requires downward short-wave and long-wave radiation (W m −2 ), rainfall and snowfall rate (kg m −2 s −1 ), air temperature (K), wind speed (m s −1 ), surface pressure (Pa) and specific humidity (kg kg −1 ) ( Table 1). Gap-filled meteorological forcing data at the local scale was obtained from the FLUXNET network and data at the global scale was obtained from two gridded data sets: WFDEI (Weedon et al, 2014(Weedon et al, , 2011 and that developed by Sheffield et al (2006) There are several global LAI data sets available, such as ECOCLIMAP (1992) (Masson et al, 2003), CYCLOPES (1997CYCLOPES ( -2007 (Baret et al, 2007), GLOBCARBON (1998GLOBCARBON ( -2003 (Deng et al, 2006), MOD15 (2000-present) (Yang et al, 2006) and MISR LAI (2000-present) (Diner et al, 2008;Hu et al, 2007). For the majority of sites used in this study, gap-filled meteorological data and GPP flux observations are only available for the 2000s and therefore, a global data set of satellite LAI was required that covered this period.…”