Proximal sensing approach for characterization of calcareous soils using multiblock data analysis

Abstract

Application of visible-near infrared (VNIR) or mid-infrared (MIR) proximal sensing for prediction of soil attributes has proven successful in many cases and is currently extensively used. However, in some cases, predictions fail, especially for properties that do not have a strong spectral signature. Multiblock data analysis techniques are an extension of partial least squares regression (PLSR) that allows the incorporation of data obtained from different instruments into a single model. The techniques have not been used in soil characterization particularly in chemical and elemental soil analysis. Therefore, the aim of the study was to analyze the use of two multiblock algorithms, sequential orthogonalized partial least squares (SO-PLS) and response-oriented sequential alternation (ROSA), to improve the prediction of several soil attributes from VNIR and MIR spectra and their 2nd order derivatives in calcareous soils from Iran. A total of 130 soil samples were taken from 0 to 30 cm depth in Kamfiroz (Mulla Sadra Dam Watershed), Fars Province, Iran. The analyzed soil properties included soil organic matter (SOM), electrical conductivity (EC), carbonate calcium equivalent (CCE), clay and sand contents, and plant-available (DTPA-extractable) micronutrients such as manganese (Mn), iron (Fe), nickel (Ni), zinc (Zn), and copper (Cu). Multiblock models (SO-PLS and ROSA) were compared with single-block PLSR models obtained from VNIR, MIR and their 2nd order derivatives. The results indicated that SO-PLS improved the prediction performance for all studied properties compared to single-block PLSR models from VNIR, MIR or their 2nd order derivatives, and multiblock ROSA. The highest improvement was achieved for properties where single-block models were less accurate. However, the ROSA models were less accurate than single-block models. The best SO-PLS models were obtained for CCE (R2 = 0.96, RPIQ = 5.95, and NRMSE = 7.24%), followed by SOM (R2 = 0.91, RPIQ = 3.38, and NRMSE = 11.45%), log Fe (R2 = 0.89, RPIQ = 3.16, and NRMSE = 9.98%), Cu (R2 = 0.87, RPIQ = 3.21, and NRMSE = 15.51%), clay content (R2 = 0.85, RPIQ = 3.57, and NRMSE = 10.14%), Ni (R2 = 0.76, RPIQ = 2.60, and NRMSE = 28.25%), EC (R2 = 0.74, RPIQ = 1.63, and NRMSE = 20.94%), sand (R2 = 0.68, RPIQ = 1.82, and NRMSE = 27.58%), and Zn (R2 = 0.63, RPIQ = 1.65, and NRMSE = 18.14%). However, Mn was poorly predicted by all evaluated methods. The results confirmed the potential of the SO-PLS model to improve the prediction of soil properties using a combination of spectral information obtained with different instruments.