A Framework for Global Characterization of Soil Properties Using Repeat Hyperspectral Satellite Data

Abstract

Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched “dynamic soil spectral library” which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.