- Angélica Santos Rabelo de Souza Bahia *a,
- José Marques Júniora,
- Newton La Scala Júniorb,
- Carlos Eduardo Pellegrino Cerric and
- Livia Arantes Camargod
- a Dep. of Soils and Fertilizers State Univ. of São Paulo (UNESP) Research Group CSME Soil Characterization for Specific Management Jaboticabal, São Paulo, Brazil
b Dep. of Exact Sciences State Univ. of São Paulo (UNESP) Research Group CSME Soil Characterization for Specific Management Jaboticabal, São Paulo, Brazil
c Dep. of Soil Science São Paulo Univ. Piracicaba, São Paulo, Brazil
d Dep. of Soils and Fertilizers State Univ. of São Paulo (UNESP) Research Group CSME Soil Characterization for Specific Management Jaboticabal, São Paulo, Brazil
- We used VIS-NIR spectroscopy and magnetic susceptibility to predict soil attributes.
- This paper shows the importance these tools for mapping large areas with detail scale.
- Indirect measurements are useful to characterize spatial variability of attributes.
- The maps predicted showed the same spatial pattern that the observed maps.
- The methodologies used are much simpler and faster than conventional methods.
The development of fast, accurate and low-cost methods to quantify soil attributes is of paramount importance to enable detailed mapping, mainly in tropical regions where there is great variation of the chemical, physical and mineralogical attributes. Therefore, the aims of this paper were (i) to investigate if visible and near infrared (VIS-NIR) spectroscopy and magnetic susceptibility (MS) can be applied to determine soil attributes at the sandstone-basaltic transition and (ii) evaluate and map their spatial distribution. Calibration models based on VIS-NIR spectroscopy and MS were developed separately for each attribute. Soil samples (0–25 cm depth) were collected at 446 sites, air-dried and passed through a 2-mm sieve and analyzed in the laboratory. To develop models based on soil spectra and laboratory data, the partial least squares regression (PLSR) was used. Already, the MS-based models were calibrated by linear regression between magnetic and laboratory data. The best prediction accuracy parameters were obtained with MS, later with VIS-NIR and lastly with VIS. The more accurate results between the observed and predicted values were found for iron oxide extracted by dithionite (R2 = 0.89, RRMSE = 0.02), clay (R2 = 0.85, RRMSE = 0.76) and total carbon (R2 = 0.83, RRMSE = 1.18) estimated by MS, revealing that this is a good predictor of key properties of studied soils, even with wide chemical and mineralogical variation. Both tools are very attractive for the strategic planning of land use and occupation, mapping large areas with detailed scale, environmental monitoring and precision agriculture.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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