On the Feasibility of Characterizing Soil Properties From AVIRIS Data

Abstract

We evaluate the feasibility of quantifying surface soil properties over large areas and at a fine spatial resolution using high-resolution airborne imaging spectroscopy. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected by the National Aeronautics and Space Administration immediately after the large 2011 Mississippi River flood at the Birds Point New Madrid (BPNM, $\approx 700 \mathrm{km}^{2} $ ) floodway in Missouri, USA, was used in a data mining lasso framework for mapping of soil textural properties such as percentages of sand, silt, clay, soil-organic matter, and many other soil chemicals constituents. The modeling results show that the approach is feasible and provide insights in the accuracy and uncertainty of the approach for both soil textural properties and chemical constituents. These models were further used for a pixel-by-pixel prediction of each the soil constituent, resulting in high-resolution (7.6 m) quantitative spatial maps in the entire floodway. These maps reveal coherent spatial correlations with historical meander patterns of Mississippi River and fine-scale features such as erosional gullies, represented by difference in constituent concentration, e.g., low soil organic matter, with the underlying topography immediately disturbed by the large flooding event. Further, we have argued and established that the independent validation results are better represented as a probability density function as compared with a single calibration–validation set. It is also found that modeled soil constituents are sensitive to NDVI and the calibration sample sizes, and the results improve with stricter (lower) NDVI thresholds and larger calibration sets.