ABSTRACT Developing strategies for monitoring concentrations of rare earth elements (REEs) in soils is an urgent issue. Using near-infrared spectroscopy (NIR) as an alternative method could optimize the assessment of REEs across large areas. This study evaluated NIR performance for predicting REEs in soils from Piauí State (about 241,755 km 2 ), one of the largest producers of agricultural commodities in Northeast Brazil. To cover the pedological variability across the entire state, 243 composite topsoil samples were collected. Samples were ground and sieved to ≤150 µm, then analyzed for REE concentrations using inductively coupled plasma optical emission spectroscopy (ICP-OES). Spectra were obtained in the NIR range (1000–2500 nm) from soil samples with particle sizes ≤2 mm using an FT-IR/NIR spectrometer. To reveal relationships between local REE characteristics and the performance of prediction models, the samples were subdivided into three distinct regions within Piauí State: (1) North, (2) Southeast, and (3) Southwest. To provide a comprehensive view of the data, the model performance across the entire state of Piauí was also evaluated. Soil spectra were preprocessed, and models were built using partial least squares (PLS) and random forest (RF) regression algorithms. Soil samples from Piauí state showed high spatial variability in terms of REE concentrations. The overall performance of prediction models was improved by reducing the scale to smaller areas. Reasonable results were found for dysprosium (Dy), erbium (Er), and ytterbium (Yb) in the Southwest and for the average concentration of heavy rare earth elements (∑HREE) in both the Southeast and Southwest. Our findings indicated that reducing the sampling scale area could lead to better modeling results and that NIR spectroscopy is a viable alternative method for assessing REEs. We suggest future similar studies in Piauí State should focus on localized areas with more homogeneous environmental settings.
MAIA et al. (Thu,) studied this question.