Read Cassia F, Duncan David H, Ho Chiu Yee Catherine, White Matt, Vesk Peter A
School of BioSciences University of Melbourne Parkville VIC Australia.
Department of Environment, Land, Water and Planning Arthur Rylah Institute for Environmental Research Heidelberg VIC Australia.
Ecol Evol. 2018 Jan 16;8(4):1974-1983. doi: 10.1002/ece3.3417. eCollection 2018 Feb.
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 km with soil physicochemical data collected over a period of 30 years and demonstrate a strong relationship between gamma radiometric potassium (K), 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.
植物生态学家需要有关土壤功能特性的空间信息，但常常面临一些土壤分类，这些分类无法直接解释或用于统计模型。砂和黏土含量是重要的土壤特性，因为它们表明了土壤的持水能力和养分含量，但大部分地区都没有这些数据。遥感土壤辐射数据为开发适用于大面积区域的土壤功能特性统计模型带来了希望。在此，我们构建了将40000平方公里区域的辐射数据与30年期间收集的土壤理化数据相联系的模型，并证明了伽马辐射钾（K）、钍（²³²Th）与土壤砂和黏土含量之间存在很强的关系。我们的模型在内部交叉验证（对留出的数据）中的预测性能为43%，对独立测试数据集的外部验证约为30%。这项工作有助于更广泛地获取和应用遥感产品，以解释不同景观中植物分布和性能的模式。
