Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement

作者：José Lucas Safanelli, Tomislav Hengl, Leandro Parente, Robert Minařík, Dellena Evelyn Bloom, Katherine EO Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan Sanderman · 发表于：PLoS ONE · 年份：2025 · DOI：10.1371/journal.pone.0296545 · 被引用次数：50 · 研究领域：Soil Geostatistics and Mapping、Spectroscopy and Chemometric Analyses、Mineral Processing and Grinding

Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good initiative has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extra...