Combining Proximal and Remote Sensors in Spatial Prediction of Five Micronutrients and Soil Texture in a Case Study at Farmland Scale in Southeastern Brazil

• Published in: Agronomy (Basel) Vol. 12; no. 11; p. 2699
• Main Authors: Pierangeli, Luiza Maria Pereira, Silva, Sérgio Henrique Godinho, Teixeira, Anita Fernanda dos Santos, Mancini, Marcelo, Andrade, Renata, de Menezes, Michele Duarte, Marques, João José, Weindorf, David C., Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2022
• Subjects: Agricultural land; Algorithms; Case studies; Crops; Datasets; Digital Elevation Models; digital soil mapping; Farms; Laboratories; Machine learning; Magnetic permeability; Magnetic susceptibility; Micronutrients; Nutrient availability; Oxisols; Predictions; pXRF; Remote sensing; Remote sensors; Root-mean-square errors; Sensors; Soil acidity; Soil fertility; Soil management; Soil properties; Soil texture; Soils; Spatial discrimination; Spatial resolution; Spectrometry; Sustainability management; terrain attributes; Texture; Topography; tropical soils; Ultisols; X-ray fluorescence; Brazil; Netherlands; United States > US
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12112699

Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices.