Useful surrogates of soil texture for plant ecologists from airborne gamma‐ray detection

Abstract

Abstract Plant ecologists require spatial information on functional soil properties but are often faced with soil classifications that are not directly interpretable or useful for statistical models. Sand and clay content are important soil properties because they indicate soil water‐holding capacity and nutrient content, yet these data are not available for much of the landscape. Remotely sensed soil radiometric data offer promise for developing statistical models of functional soil properties applicable over large areas. Here, we build models linking radiometric data for an area of 40,000 km2 with soil physicochemical data collected over a period of 30 years and demonstrate a strong relationship between gamma radiometric potassium (40K), thorium (²³²Th), and soil sand and clay content. Our models showed predictive performance of 43% with internal cross‐validation (to held‐out data) and ~30% for external validation to an independent test dataset. This work contributes to broader availability and uptake of remote sensing products for explaining patterns in plant distribution and performance across landscapes.

Keywords

• boosted regression tree models
• clay
• field estimation
• Gamma radiometric data
• particle size analysis
• potassium