SOIL PROPERTIES MAPPING USING PROXIMAL AND REMOTE SENSING AS COVARIATE

Obtaining knowledge about the distribution of spatial variability soil properties is crucial to proper site-specific management. One way improve quality mapping by using auxiliary information (covariate). The objective this study was test whether remote and proximal sensing data can assist in through geostatistical prediction. We worked an experimental area cultivated with sugarcane located Sao Paulo State, Brazil, selected five properties: organic matter, CEC, base saturation, K P availability. Two covariates often used express variation were chosen, one obtained (SWIR2 band) other (apparent electrical conductivity – ECa). These individually together interpolation method (kriging external drift). found that ECa a more promising covariate than SWIR2 band from orbital imaging. Such identify short-range variability. However, when property well explained sampling procedure, multivariate methods may not accuracy.