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An Approach for Estimating Soil Organic Matter Content Using Synthetic IRS Satellite Data in Tropical Soils of Lop Buri, Thailand Remote sensing of soils using optical satellite sensors, at spatial resolution of 30 meters, like Landsat-ETM, SPOT, and ASTER are suitable for small-scale farming system. However, their detection are restricted to the VNIR spectral regions (Plummer et al. 1995, Jensen 2000). They might have limited bands at SWIR region, but at a very lower spatial resolution ( 60 meters). Secondly, The spectral features of soils are monotonous and featureless within the VNIR region (Daniel et al. 2002). Hence, applying absorption features for the identification of soil nutrient purpose (Valeriano et al. 1995, Palacios-Orueta and Ustin 1996) is extremely difficult in VNIR. Some studies have addressed this problem by conducting spectrometric-based soil nutrients estimation from a wide range of spectral bands (Galvao and Vetorello 1998, Palacios-Orueta and Ustin 1998, Price 1998). However, the most commonly used spectrophotometers are limited to VNIR spectral range (Milton et al. 1995). SOM is considered a test nutrient. It is highly variable and react quickly to external changes and its decomposition rates show spatial variability (Palacios-Orueta and Ustin 1996). Therefore, the prime objective of this study is to integrate the spectroradiometer and satellite sensor (IRS-1C) through a newly developed method called, SBC, and estimate and classify SOM. 2. Methodology 2.1. The Data Set The study area is found in Thailand, Lop Buri District, at geographical coordinates of 14°45' – 15°00' N and 100°50' – 101°10' E. Soils were developed over the parent material of sandstone and limestone. An intensive satellite-synchronized field survey was undertaken on April 2001. It was during “pedo-window” period: where there was cloud-free and uncovered soil condition, before the growing season. Forty-two soil samples from the top most layers were collected and geo-referenced with GPS. Soil samples (in three replicates) were brought to the laboratory for SOM determination through conventional dry ash method. The samples were also spectrally assessed using StellarNet spectroradiometer, over a 400 nm to 1290 nm spectrum at 10 nm intervals. Descriptions of the study area and laboratory procedures are found in Daniel et al. (2002, 2001). The satellite data used is IRS 1C scene (path 121, row 63), dated 08 April 2001. Both geometric the correction and conversion of original DN measures to the surface reflectance values was carried out in conjunction with the atmospheric correction. 2.2. Spectral Band Cloning The Encyclopedia of Britannica (2002 edition) defines cloning as a process of generating identical genes enough for further study. In this study, cloning is tailored to the duplication of unknown spectral data from the known ones. Hence “spectral band cloning” is defined as, the process of duplicating satellite bands, assisted by intraspectral relationships from spectrometer channels. Daniel et al. (2002) reported the successful estimation of SOM (R2 = 0.85) from the laboratory-based spectral bands, using Artificial Neural Network (ANN). The SOM-sensitive bands, which were chosen from the spectrometer-driven models, were different from IRS-driven bands, which make the prediction of SOM (Eq-1) from the real IRS channels (R550, R650, and R815) impossible. Figure 1 shows the overview of SBS. From the 80 narrow channels of the spectroradiometer-driven spectral data, the IRS compatible bands (R550, R650, and R815) were chosen as predictors of the SOM-sensitive bands, which are identified by ANN. This is called the intra-spectral relationship of spectroradiometer bands.  Figure 1. Hypothetical Structure of SBS (Spectral Band Cloning) for IRS data Yi = f (Xij) (Eq-1) Where Y is SOM-sensitive band. i is ANN chosen bands of R460, R460, and R480. j refers to the IRS channels of R550, R650, and R815. |