Abstract
We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.
Highlights
IntroductionClay content is a primary attribute that has a physical relationship with electromagnetic radiation [6]
To compare the results of the data obtained by the spectroradiometer, considered standard equipment for obtaining spectral information when working with remote sensing [13,38,39], hyperspectral aerial images were obtained by the AisaFENIX hyperspectral image sensor, with the Vis-NIR–SWIR spectral range, and spectral resolution of 3.5 nm between 380 and 970 nm and 12 nm between 970 and 2500 nm
The bias valuedemonstrate was low, showing that there to was bias in the generated models the potential benoapplied in areas validation of prediction models. These values can still suggest good with similar geological and edaphoclimatic characteristics, and further investigations are performance for the content estimate of clay, sand and soil organic matter (SOM) [64]. These results demonstrate necessary to evaluate the applicability of this method in areas with different characteristhat Vis—NIR—SWIR spectroscopy is suitable for the multidimensional evaluation of soil tics.attributes, just as reported by Paz-Kagan et al [18] and Nanni et al [47]
Summary
Clay content is a primary attribute that has a physical relationship with electromagnetic radiation [6] For this reason, remote sensing techniques, e.g., diffuse reflectance spectroscopy (DRS), have been evaluated for rapid estimation of soil texture in a non-destructive and low cost, providing rapid response compared to conventional laboratory analyses [7,8,9,10,11]. Soil data collected by airborne hyperspectral sensors has shown to be a promising technique for understanding soil attributes and spatial patterns [18,21,22] The use of this technique for prediction of the soil characteristics and properties has demonstrated challenges such as large datasets, spectral instability effects, and atmospheric interference [23,24,25,26]. This study evaluates use of hyperspectral imaging and non-imaging sensors for predicting particle size and soil organic matter in a selected soil landscape
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