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An Approach for Estimating Soil Organic Matter Content Using Synthetic IRS Satellite Data in Tropical Soils of Lop Buri, Thailand
K.W. Daniel
Email: rsp007193@ait.ac.th
N.K. Tripathi and K. Honda
Space Technology Applications and Research (STAR)
Asian Institute of Technology (AIT)
P. Box 4 Klong Luang, Pathumthani 12120
Fax: 66-2-524-5597
Thailand
Abstract
Timely information on the content and distribution of key soil nutrients is vital to support precision agriculture. This paper describes the newly developed approach, “Spectral Band Cloning” (here after, SBC). The purpose is to enhance the IRS satellite data for soil nutrient estimation, which otherwise is unattainable. The idea emanated from sensors integration, where the intraspectral relationships of spectrometer channels are mimicked by the corresponding IRS bands. New and synthetic bands were generated, which are competent to estimate soil organic matter (SOM). Forty-two samples from topmost soil layer, collected during a satellite-synchronized field survey in Lop Buri, Thailand, were analyzed chemically and spectrally in a laboratory. From raw spectrometer-driven spectra, SOM was successfully modeled from bands R410, R460, and R480 (R2 = 0.85), which are unobtainable in IRS. The SBC enabled decent modeling of SOM from synthetic IRS bands (R2 = 0.72). The model was implemented and verified on a GIS platform and generated a predicted SOM surface with a reasonable degree of accuracy. SBC is a promising to estimate other indiscernible biophysical parameters, which enhance precision farming, and could be employed to other satellite sensors.
1. Introduction
Satellite sensors are faster, cheaper, and objective data providers than conventional field-based surveys. This is due to wide area coverage and possibility of concurrent information recording represented in a digital energy pattern. Most satellite sensors, however, have a small number of satellite channels and are limited to extract satisfactory reflectance from objects, which vary with location, time, geometry of observation and waveband (Curran and Kupiec 1995). Soils show both spatial and temporal variability, and dictate a fine-tuned management at farm level, leading to precision agriculture (Bouma 1995). Nutrients of soils are not easy-to-detect variables from remote sensors (Palacios-Orueta and Ustin 1998, Clark 1999). However, several researchers have made significant contributions for the detection from remote sensing reflectance spectra (in situ measurements) and from radiance data recorded by satellite sensors. Recently, precision agriculture has gained interest from satellite data with a special preference for higher spatial and spectral resolutions (Mulla 1995, Strachan et al. 2002), where most existing satellites are handicapped.
The hyperspectral remote sensing data using AVIRIS sensor has been widely used for soil mineral detection (Hoffbeck and Landgrebe 1996, Palacios-Orueta and Ustin 1998, Price 1998, Galvao et al.. 2001). This wide application is due to extreme potential to detect at numerous narrow bands in a wide spectral range. However, the platform of AVIRIS is airborne, and requires special flight arrangement, as opposed to the regular observation by satellite sensors. Field and laboratory based spectrometers are endowed with high-resolution reflectance spectra, and have wider opportunity to capture the reflectance response of many objects (Milton et al. 1995, Palacios-Orueta and Ustin 1998, Clark 1999, Leone and Sommer 2000). Satellite sensors, which are composed of spectral bands with broad bandwidth, can benefit from these satellite sensors through sensor integration (Hoffbeck and Landgrebe 1996, Palacios-Orueta and Ustin 1998, Clark 1999)
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