Regression-kriging for characterizing soils with remote-sensing data

Abstract

In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi, USA. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in soil sodium concentration. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.

Keywords

precision agriculture / regression-kriging / remote sensing / soil sensors

References

[1] Bajwa S G, Tian L (2001). Aerial CIR remote sensing for weed density mapping in a soybean field. Trans ASAE, 44(6): 1965–1974

[2] Barnes E M, Baker M G (2000). Multispectral data for mapping soil texture: Possibilities and limitations. Appl Eng Agric, 16: 731–741

[3] Bishop T F A, McBratney A B (2001). A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma, 103(1-2): 149–160

[4] Cambardella C A, Karlen D L (1999). Spatial analysis of soil fertility parameters. Precis Agric, 1(1): 5–14

[5] Christy C, Drummond P, Laird D (2003). An On-the-go Spectral Reflectance Sensor for Soil. Paper <patent>No. 031044</patent> ASAE, Mich: St. Joseph

[6] Geypens M, Vanongenval L, Vogels N, Meykens J (1999). Spatial variability of agricultural soil fertility parameters in a gleyic podzol of Belgium. Precis Agric, 1(3): 319–326

[7] Hengl T, Heuvelink G B M, Stein A (2003). Comparison of kriging with external drift and regression kriging. Technical Report. International Institute for Geo-information Science and Earth Observation. Available at: Accessed <day>11</day><month>Jun</month>2007

[8] Hengl T, Heuvelink G B M, Stein A (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1-2): 75–93

[9] Hummel J W, Sudduth K A, Hollinger S E (2001). Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensors. Comput Electron Agric, 32(2): 149–165

[10] Iqbal J, Thomasson J A, Jenkins J N, Owens P R, Whisler F D (2005). Spatial variability analysis of soil physical properties of alluvial soils. SSSAJ, 69(4): 1338–1350

[11] Kitanidis P K (1993). Generalized covariance function in estimation. Math Geol, 25(5): 525–540

[12] Knotters M, Brus D J, Voshaar J H O (1995). A comparison of kriging, co-kriging, and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma, 67(3-4): 227–246

[13] Matheron G (1965). Les Variables Re’gionalise’es et leur Estimation: Une Application de la the’orie de Fonctions Ale’atoires aux Sciences de la Nature. Paris: Masson et Cie

[14] McBratney A B, Santosm M L M, Minasney B (2003). On digital soil mapping. Geoderma, 117(1-2): 3–52

[15] Odeh I O A, McBratney A B, Chittleborough D J (1994). Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma, 63(3-4): 197–214

[16] Odeh I O A, McBratney A B, Chittleborough D J (1995). Further results on prediction of soil properties from terrain attributes: Heterotopic co-kriging and regression-kriging. Geoderma, 67(3-4): 215–226

[17] Solie J B, Raun W R, Stone M L (1999). Submeter spatial variability of selected soil and bermudagrass production variables. SSSAJ, 63(6): 1724–1733

[18] Sullivan D G, Shaw J N, Rickman D (2005). IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. SSSAJ, 69: 1789–1798

[19] Thomasson J A, Sui R, Cox M S, Al-Rajehy A (2001). Soil reflectance sensing for determining soil properties in precision agriculture. Trans ASAE, 44(6): 1445–1453

[20] Wollenhaupt N C, Wolkowski R P, Clayton M K (1994). Mapping soil test phosphorous and potassium for variable-rate fertilize application. J Prod Agric, 7(4): 441–447

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg