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20 August 2009 Partial least squares modeling of Hyperion image spectra for mapping agricultural soil properties
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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.
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Tingting Zhang, Lin Li, and Baojuan Zheng "Partial least squares modeling of Hyperion image spectra for mapping agricultural soil properties", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 74540P (20 August 2009);

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