Estimation of soil properties at the field scale from satellite data: a comparison between spatial and non-spatial techniques

A study was carried out to investigate the usefulness of multispectral and hyperspectral satellite information for the estimation of soil properties of agronomic importance such as soil texture and organic matter (SOM) in cultivated fields by comparing different estimation procedures. Images acquired from the Advanced Land Imager (ALI) and Hyperion sensors on board the EO-1 satellite were used, in combination with ground-sampling data from an agricultural field in central Italy, to evaluate the advantage of taking into account the spatial correlation between pixels. For this purpose, partial least squares regression (PLSR), ordinary least square (OLS) regression, regression with correlated errors (restricted maximum likelihood; REML) and ordinary kriging (OK) were compared through leave-one-out cross-validation. In order to predict soil variables by different models, the predictors of OLS and REML regressions were obtained from principal component analysis (PCA), PLSR and the minimum noise fraction (MNF) transformations of spectral data on bare soil or vegetation images. The PLSR did not provide satisfactory results in terms of root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) statistics, even with hyperspectral data, mainly because of the poor signal to noise ratio (SNR) of the Hyperion sensor. The estimation accuracy increased by using the MNF method in combination with a linear mixed effect model. A multivariate approach was sometimes better than univariate ordinary kriging (OK), demonstrating the value of including Hyperion bare soil or vegetation data in the estimation procedure. Hyperspectral data provided better results than multispectral data for clay, sand and especially for SOM estimation, highlighting the value of high-resolution spectral data for soil-related applications.