Spectroscopic solutions for generating new global soil information
While global efforts to operationalize soil spectroscopy are progressing, cooperation is needed to fully leverage its potential for generating digital soil information to support sustainable soil management worldwide. The Global Soil Laboratory Network’s soil spectroscopy initiative (GLOSOLAN-Spec), led by the Food and Agriculture Organization of the United Nations (FAO) through its Global Soil Partnership (GSP), is dedicated to the further development and adoption of soil spectroscopy by fostering international collaboration via a scientific community of practice to produce accurate and reliable soil information for sustainable soil management and decision-making. To support this effort, we, a global consortium of soil scientists under the auspices of the International Union of Soil Sciences (IUSS) and GLOSOLAN-Spec, aim to address seven key challenges hindering the adoption of soil spectroscopy worldwide. Here, we offer perspectives on what is needed to advance soil spectroscopy as a routine soil analysis method, emphasizing its potential to generate new and reliable spatial and temporal soil data.
Main text
As one of Earth’s most vital natural capitals, soil provides food, fiber, and fresh water. The world’s soil contributes to energy sustainability, climate stability, biodiversity, and the delivery of essential ecosystem services.1 However, for securing the world's soil and ensuring its continued productivity, establishing comprehensive soil databases and spatial information systems representative of various spatial and temporal scales for evidence-based decision-making remains challenging. There is a pressing need for rapid and cost-effective soil analysis solutions worldwide.2 Decades of research have demonstrated that soil spectroscopy in the visible-near infrared (vis-NIR) and mid-infrared (mid-IR) portions of the electromagnetic spectrum can help alleviate this urgent need.3
Soil spectra respond to the soil’s mineral and organic composition and are affected by soil water and texture. Spectroscopy combined with multivariate statistics or machine learning can hence be used to model and accurately estimate a range of soil properties that depend on the composition of the soil matrix, e.g., soil organic carbon, clay, silt and sand contents, cation exchange capacity (CEC), pH, and CaCO3. Thus, soil spectroscopy offers a cost-effective complement to conventional soil analytical methods for measuring chemical, physical, and biological properties. Such information is essential for monitoring, sustaining, and improving soil health, providing the foundation to address future challenges once soil spectroscopy becomes more widely adopted and firmly established (Figure 1).
