An Automated Soil Line Identification Routine for Remotely Sensed Images

Abstract

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.

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

“…Among these soil-adjusted vegetation indices, the TSAVI, ATSAVI, and PVI were developed to minimize soil background influence by incorporating the slope and intercept of the soil line, which was established by linear regression of soil reflectance in red-NIR spectral portions. Compared with the soiladjusted SAVI, MSAVI, OSAVI, and GSAVI, the algorithms of TSAVI, ATSAVI, and PVI containing the soil line parameters greatly limit their applications due to the difficulty of obtaining the real soil line [Baret et al, 1993;Fox et al, 2004;Liu et al, 2008]. The contribution of the litter is another prominent barrier to the determination of green aboveground biomass in arid and semiarid grassland, where the litter is a large vegetation component [Van Leeuwen and Huete, 1996;Asner et al, 1998;Guo, 2002;He et al, 2006;Ren and Zhou, 2012].…”

Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines

“…The above result, (19), is one of the objectives of this paper. It indicates two facts: (1) The slope of the vegetation isoline is linearly correlated to the amount of difference in the soil line slope, ∆a, (independent of difference in the offset, ∆b); (2) The offset of the vegetation isoline is linear correlated to both ∆a and ∆b in opposite ways (one negative and the other positive).…”

“…Although many studies reported general values of a and b, these two parameters depend on chemical and mineral contents, proportion of organic matter, soil moisture, and hence these are unique to individual soil type [15,19,23,31,32]. It is also known that these parameters vary with band position and band width properties of a multispectral sensor [12,[33][34][35], indicating the uniqueness of these parameters to each sensor.…”

“…A systematic investigation and theoretical formulation on the impact of canopy/soil background has yet to be investigated and would be a critical and meaningful contribution to operational monitoring of terrestrial vegetation with satellite VI time series [7,[12][13][14][15][16]. Investigations are still needed to clarify systematic variations of VI value due to differences in soil optical properties, and that result in a methodology to adjust VI [17,18], e.g., about bias error, for site-specific canopy background influences [19].…”

Soil Line Influences on Two-Band Vegetation Indices and Vegetation Isolines: A Numerical Study

“…This information has intensively been used for characterization of the earth's surface for retrievals of vegetation biophysical parameters [4][5][6][7][8][9][10][11][12] and soil surface status [13][14][15][16][17][18] from remotely sensed reflectance spectra. Several studies have indicated that the soil moisture and organic matter contents relate to changes in soil reflectance along the soil line [2,14,[18][19][20].…”