Abstract
This paper investigated the capacity of Hyperion images coupled with Partial least squares analysis (PLS) for mapping
agricultural soil properties. Soil samples were collected from Cicero Creek Watershed of central Indiana, and analyzed
for soil moisture content (MC), soil organic matter (SOM), total carbon (C), total phosphorus (P), total nitrogen (N) and
clay content. Two scenes of Hyperion images covering the watershed were acquired, calibrated and georeferenced, and
image spectra were extracted from them. Two phases of PLS modeling was conducted: all samples were used and
outliers were identified and removed in phase 1, and in phase 2, the outlier removed dataset were split into two subsets
for calibration and validation. The PLS results for both phases indicate that PLS modeling of Hyperion spectra is
effective to predict MC, SOM, total C, and total N, but resulted in low correlations for total P and clay content. The low
correlation for total P is attributed to low correlation between SOM and total P. The worst correlation for clay content is
due to the low signal-to-noise ratio of Hyperion images in the short wave infrared region. Future work is needed for
improving the estimates of total P and clay content.
