Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation

• Published in: Geoderma Regional Vol. 28; p. e00486
• Main Authors: Silva-Sangoi, Daniely Vaz da, Horst, Taciara Zborowski, Moura-Bueno, Jean Michel, Dalmolin, Ricardo Simão Diniz, Sebem, Elódio, Gebler, Luciano, da Silva Santos, Márcio
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Clay content; Digital soil mapping; Ferralsol; Kriging; Soil organic matter; Spatial structure; Vis-NIR-SWIR spectroscopy; Ferralsol; Digital soil mapping; Vis-NIR-SWIR spectroscopy; Clay content; Soil organic matter; Spatial structure; Kriging
• ISSN: 2352-0094; 2352-0094
• DOI: 10.1016/j.geodrs.2022.e00486

Soil spectroscopy (Vis-NIR-SWIR 350–2500 nm) has known potential to predict clay content and soil organic matter (SOM), which are properties that determine soil quality. It is well known that data predicted from Vis-NIR-SWIR carry prediction errors resulting from modeling process. However, these errors are not spatially correlated and, even being numerically small, they can alter the spatial correlation of the data. Thus, the aim of the study was to evaluate wether clay and SOM data predicted by spectroscopy models preserve the spatial structure of data obtained by traditional chemical analyses. This was done considering a soil-spectral dataset from a small viticultural area (3 ha) in Southern Brazil. Soil samples were collected in 74 sites, in two depths (0.00–0.20 m and 0.20–0.40 m depth). Collected in 74 sites, totaling 148 soil samples. The Vis-NIR-SWIR data were used to train spectral models in raw form, and subjected to three pre-processing techniques (smoothing - SMO; Savitzky-Golay with first derivative - SGD; Binning - BIN). In the calibration step, three machine learning techniques were tested (Cubist –CUB; Random Forest –RF; Partial Least Squares Regression - PLSR). The spatial correlation of the data measured by traditional wet chemistry, as well the data predicted by spectroscopy, was analyzed clay and SOM maps were then generated by ordinary kriging for both data. Among the spectral models, the RF + SGD model presented the best cross-validation performance for clay, with R2cv = 0.95 and RMSEcv = 1.06%, and the PLSR + SGD for SOM, with R2cv = 0.98 and RMSEcv = 0.07%. The data predicted by Vis-NIR-SWIR spectroscopy preserved the spatial structure of the data obtained by the traditional wet chemical analysis. However, our results suggest that spectral modeling was more effective in predicting SOM while spatial interpolation was more effective in predicting clay. Data predicted by Vis-NIR-SWIR spectroscopy with lower accuracy increased the residuals of the spatial predictions. The clay and SOM maps prodiced with spectroscopy predictions show similarities and feasible accuracy for the use and management of agricultural soils in vineyard areas. •Vis-NIR-SWIR and kriging were used to map MOS and clay in a wine-growing area.•Vis-NIR-SWIR predicted data preserve the spatial structure of the reference data from wet chemistry.•Vis-NIR-SWIR predictive errors impaired spatial dependence.•Clay and SOM Vis-NIR-SWIR maps are accurate for use and soil management in vineyard areas.