Soil texture prediction through stratification of a regional soil spectral library

Knowing the spatial distribution of soil texture,which is a physical property,is essential to support agricultural and environmental decision making.Soil texture can be estimated using visible,near infrared,and shortwave infrared(Vis-NIR-SWIR)spectroscopy.However,the performance of spectroscopic models is variable because of soil heterogeneity.Currently,few studies address the effects of soil sample variability on the performance of the models,especially for larger spectral libraries that include soils that are more heterogeneous.Therefore,the objectives of this study were to:i)apply Vis-based color parameters on the stratification of a regional soil spectral library;ii)evaluate the performance of the predictive models generated from the spectral library stratification;iii)compare the performance of stratified models(SMs)and the model without stratification(WSM),and iv)explain possible changes in prediction accuracy based on the SMs.Thus,a regional soil spectral library with 1535 samples from the State of Santa Catarina,Brazil was used.Soil reflectance data were obtained by Vis-NIR-SWIR spectroscopy in the laboratory using a spectroradiometer covering the 350–2500 nm spectral range.Sand,silt,and clay fractions were determined using the pipette method.Twenty-two components of color parameters were derived from the Vis spectrum using the colorimetric models.A cubist regression algorithm was used to assess the accuracy of the applicability of the initial models(SMs and WSM)and of the validation between the clusters.Fractional order derivatives(FODs)at 0.5,1.5,and 2 intervals were used to explain possible changes in the performance of the SMs.The SMs with higher contents of clay and iron oxides obtained the highest accuracy,and the most important spectral bands were identified,mainly in the 480–550 and 850–900 nm ranges and the 1400,1900,and 2200 nm bands.Therefore,stratification of soil spectral libraries is a good strategy to improve regional assessments of soil resources,reducing prediction errors in the qualitative determination of soil properties.