Two EO-1 Hyperion images covering a Cicero Creek reservoir of central Indiana were analyzed using partial least squares (PLS) regression to estimate soil properties, including soil moisture, soil organic matter (SOM), total carbon (C), total phosphorus (P), total nitrogen (N), and clay content. PLS results for Hyperion image spectra were compared with those for laboratory measured spectra using several statistics, including the coefficient of determination (R 2 ) and RPD (the ratio of standard deviation of sample chemical concentration to root mean square error). PLS was conducted in two phases: phase-1 used all samples for calibration to determine outliers and then models were recalibrated after outlier removal; phase-2 split the resulting samples from phase 1 into two subsets for calibration and validation, respectively. Based on R 2 and RPD values, the results from the phase-1 calibration indicate that PLS can estimate all soil properties from laboratory spectra and some soil properties from Hyperion spectra, and the phase 2 results suggest that PLS can predict SOM, total C, and total N using Hyperion reflectance spectra. It was found that spectral resolution has impacts on the PLS performance in estimating the soil properties considered in this investigation.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.