Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Abstract

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.

A Regional Legacy Soil Dataset for Prediction of Sand and Clay Content with Vis-Nir-Swir, in Southern Brazil

“…However, SDSG decreased model performance of the remaining multivariate models for the two soil properties studied. These results with spectral derivatives and ensemble-learning algorithms (RF) are in agreement with Vasques et al (2008), Pinheiro et al (2017), Dotto et al (2018), and Santana et al (2018).…”

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

“…The root mean square error and regression coefficients obtained from the PLRS analysis of this study showed that with three latent vectors selected, 74% of the variation of SOC content was explained by this model (R 2 = 0.74), the RPD of 1.47 indicates an acceptable prediction, that could be improved using more samples. Conforti et al [35] found more accurate predictions (R 2 = 0.84; RPD = 2.53) in soils over gneiss and schists; other studies [72] show more similar results (R 2 = 0.71; RPD = 1.84) for acid heterogeneous soils in the Central Amazon region. After searching for publications on the estimation of soil characteristics by VIS/NIR spectroscopy in gypsiferous areas, only a similar approach was found in the study of Babaeian et al [73].…”

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

“…To solve this problem, downsampling is applied to band 8 of every single date imagery to achieve the spatial resolution of 30m. PLSR is a particular form of multivariate linear regression (Wang et al, 2018),which is the most common method used in soil properties prediction (Pinheiro et al, 2017). PLSR is underpinned by the assumption that the dependent variable can be estimated via a linear combination of explanatory variables.The maximum number of latent variables in PLSR is set at 20 and the optimum number of latent variables are determined by 5-fold cross-validation.…”