Publications

Wang, WL; Dungan, J; Genovese, V; Shinozuka, Y; Yang, QG; Liu, X; Poulter, B; Brosnan, I (2023). Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 128(6), e2022JG007363.

Abstract
This study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8-day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission. Plain Language Summary This paper develops a emulated (or synthetic) global hyperspectral data set, based on existing multispectral satellite products, to support the NASA Surface Biology and Geology (SBG) mission development. The challenge of the task is to find a simple yet robust algorithm to interpolate multispectral observations to (unobserved) hyperspectral bands. Based on theoretic analyses and numerical model simulations, we develop an accurate and efficient algorithm that accomplishes this task by combining information from the optical properties of the surface elements and the structural properties of the landscape and vegetation canopies. The former are obtained from existing spectral libraries and the latter can be estimated from the multispectral satellite products. We apply the algorithm to generate a scientifically coherent and consistent global hyperspectral data set, which are publicly available to serve the needs of the science community of SBG and other global hyperspectral remote sensing satellite missions.

DOI:
10.1029/2022JG007363

ISSN:
2169-8961