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DIVISION S-5—PEDOLOGY

An Automated Soil Line Identification Routine for Remotely Sensed Images

^a 208 Carrier Hall, Dep. of Civil Engineering, Univ. of Mississippi, University, MS 38677-1848
^b Bayer CropScience, 17745 S. Metcalf, Stilwell, KS 66085
^c Dep. of Biological & Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117
^d USDA-ARS, Kika de la Garza, Subtropical Agricultural Research Unit, 2413E Hwy 83, Weslaco, TX 78596

^* Corresponding author (gafox{at}olemiss.edu).

The soil line is a linear relationship between the near-infrared (NIR) and red (R) reflectance of bare soil as characterized by slope and intercept parameters. Vegetation indices use soil line parameters extensively in crop growth analyses. Research indicates that the soil line can be related to site-specific soil conditions within a field, especially organic C content. This relationship may provide a means for directing soil sampling. However, these soil and crop growth remotely sensed predictions require accurate estimates of soil line parameters. Determining soil line parameters by manually extracting reflectance characteristics of bare soil pixels can be cumbersome. This research proposes an automated soil line identification routine capable of deriving soil line parameters from bare soil or vegetated remotely sensed images. The automated routine estimates soil line parameters by deriving a set of minimum NIR digital numbers across the R band range. Pixels that contradict soil line theory are removed through an iterative process. The routine was evaluated using bare soil images of two fields in the Midwest USA and 15 multispectral digital video images of South Texas grain sorghum fields dominated by vegetated cover. This research compared soil line parameters derived from the automated routine to actual soil line parameters obtained by extracting R and NIR digital numbers from identifiable bare soil pixels within the images and also by manually inspecting plots of R versus NIR digital numbers for all pixels within an image. The routine performed reasonably well in matching the estimated actual soil line parameters with minimal adjustment between images.

Abbreviations: LAI, leaf area index • NDVI, normalized difference vegetation index • NIR, near-infrared • PVI, perpendicular vegetation index • R, red • SAVI, soil adjusted vegetative index • TSAVI, transformed soil adjusted vegetative index

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				G. A. Fox and R. Metla Soil Property Analysis using Principal Components Analysis, Soil Line, and Regression Models Soil Sci. Soc. Am. J., September 29, 2005; 69(6): 1782 - 1788. [Abstract] [Full Text] [PDF]