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.
Highlights
Achieving food security with the given limited soil and water resources and a rising world population set to breach breaching more than 9.5 billion within the two decades will requireRemote Sens. 2017, 9, 293; doi:10.3390/rs9040293 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 293 the adoption and expansion of digital soil mapping (DSM), precision agriculture, best management practices, and geospatial spectral technologies [1]
Once visible and near-infrared (VIS-near infrared (NIR)) soil models have been successfully built through calibration and validation, they offer future cost-effective and less laborious pathways for soil assessment
Diffuse reflectance spectroscopy is an effective technique with high applicability in quantitative analysis of tropical Central Amazon soils, especially for prediction of properties useful in soil survey, classification, fertility management, climate change monitoring and soil security
Summary
Achieving food security with the given limited soil and water resources and a rising world population set to breach breaching more than 9.5 billion within the two decades will requireRemote Sens. 2017, 9, 293; doi:10.3390/rs9040293 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 293 the adoption and expansion of digital soil mapping (DSM), precision agriculture, best management practices, and geospatial spectral technologies [1]. 2017, 9, 293 the adoption and expansion of digital soil mapping (DSM), precision agriculture, best management practices, and geospatial spectral technologies [1]. Among the latter, visible and near-infrared (VIS-NIR). The approach allows estimation multiple soil properties from the same spectral data, but is limited by the need for a reasonable number of samples to calibrate and validate the predictive models. This is usually conducted using data-craving parametric and non-parametric multivariate methods. Proximal sensors offer advantages for soil measurement over remote sensing or traditional sampling and laboratory analysis [8,11]
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