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An Approach for Estimating Soil Organic Matter Content Using Synthetic IRS Satellite Data in Tropical Soils of Lop Buri, Thailand From each sample points of the satellite image, the pixel values of G, R, and NIR were extracted to form the database for off-the-image modeling purpose. Then the intraspectral relationship developed through the spectrometer-driven SBC were imported and mimicked by the IRS. This helped to generate the synthetic IRS bands for latter modeling of SOM (Figure 2). This is through stepwise regression of several indexes developed from synthetic bands. To test the performance of the SOM estimating model, both measured and predicted layers of SOM were interpolated on IDW. The model from measured SOM refers to “what should have been produced”, and makes the “terrain nominal” (Vauglin 1999) that serves as a reference SOM surface of higher accuracy. The predicted layer, generated from interpolation of synthetically estimated SOM, refers to “what has actually been produced”. The accuracy of the prediction was undertaken by measuring the discrepancy between what should have been produced and what has actually been produced.  Figure 2. Structural overview of SOM modeling from Synthetic IRS bands 3. Results In prior studies, Daniel et al.. (2002, 2001) reported the mean distribution of SOM was 12.53%, with a standard deviation of 3.33, and considered “too much” concentration. The spectral reflectance measured at the laboratory ranged from 20 to 50%. Sites of higher SOM concentration have correlated with low reflectance measures. |