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This study aimed at examining effective sample treatments and spectral processing for an alternate method of soil nitrate determination using the attenuated total reflectance (ATR) of Fourier transform infrared (FTIR) spectroscopy. Prior to FTIR measurements, soil samples were prepared as paste to enhance adhesion between the ATR crystal and sample. The similar nitrate peak heights of soil pastes and their supernatants indicated that the nitrate in the liquid portion of the soil paste mainly responded to the FTIR signal. Using a 0.01-M CaSO 4 solution for the soil paste, which has no interference bands in the characteristic spectra of the analyte, increased the concentration of the nitrates to be measured. Secondorder derivatives were used in the prediction model to minimize the interference effects and enhance the performance. The second-order derivative spectra contained a unique nitrate peak in a range of 1,400-1,200 cm −1 without interference of carbonate. A partial least square regression model using secondorder derivative spectra performed well (R 2 =0.995, root mean square error (RMSE) = 23.5, ratio of prediction to deviation (RPD)=13.8) on laboratory samples. Prediction results were also good for a test set of agricultural field soils with a CaCO 3 concentration of 6% to 8% (R 2 =0.97, RMSE=18.6, RPD= 3.5). Application of the prediction model based on soil paste samples to nitrate stock solution resulted in an increased RMSE (62.3); however, validation measures were still satisfactory (R 2 =0.99, RPD=3.0).

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Remote sensing has potential to provide a cost-efficient and fast tool to map soil properties across large areas. Especially, hyperspectral image can potentially discriminate between crop residues and soils as well as vegetation. Satellite hyperspectral image has very narrow spectral bands but a coarse spatial resolution to detect soil properties and vegetation in small parcels of croplands. This study focused on improving spatial resolution of the satellite hyperspectral image preserving fine-spectral resolution through the integrated down-scaling algorithms. The spatially down-scaled hyperspectral image could show better analysis results of soil properties, crop residues, and vegetation types and enhance their mapping accuracy with less loss of spectral information.