Soil Carbon Remote Sensing: A Meta-Analysis and Systematic Review of Published Results from 1969–2022 - Savannah L. McGuirk; Iver H. Cairns (Geotechnics)
Soil carbon remote sensing has become a popular topic amongst scientists, policy makers, landholders, and others in recent years, as pragmatic perspectives on climate change, land productivity, and food security become increasingly important. Unfortunately, more than fifty years of existing research has not provided clarity or consensus on the best soil carbon remote sensing methods. A reliable, widely applicable, robust, and cost-effective means of soil carbon modelling remains elusive. As evidenced by aggregated data from 259 papers and 503 models published since 1969, much experimentation has been undertaken and soil carbon remote sensing shows promise, but the situation remains unresolved. First, this review and meta-analysis shows that soil carbon remote sensing model accuracy (via Pearson’s correlation coefficient R2) has decreased on average since 1969, and more rapidly since the year 2000. Second, the model R2 does not correlate strongly with the spatial (airborne platforms compared with satellite platforms) or spectral (multispectral compared with hyperspectral) resolution of data. Third, no significant relationship between the model R2 and the number of samples included in the training/test dataset is apparent. Fourth, the R2 of non-parametric models (mean R2 in 2022 = 0.58, n = 117) has declined more rapidly (decrease of 1.3% per year) since 1969 (mean R2 in 1969 = 0.74, n = 1) than the R2 of parametric models (decrease of 0.4% per year), suggesting that the algorithm applied during soil carbon modelling may be of importance. Finally, data compiled in this meta-analysis demonstrate a correlation between declining model R2 and the increased use of satellite multispectral data and non-parametric algorithms, particularly machine learning, since the year 2000. There is no other evidence to suggest that prediction models prepared with multispectral data perform worse than other models, however. Hence, for the purpose of experimentation, it may be valuable to continue experimenting with the use of machine learning models for soil carbon prediction. However, when model performance is the priority, it is recommended that simple, parametric models (such as linear regression) are applied.