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Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Article number102182
<mark>Journal publication date</mark>31/10/2020
<mark>Journal</mark>International Journal of Applied Earth Observation and Geoinformation
Number of pages15
Publication StatusPublished
Early online date10/07/20
<mark>Original language</mark>English


Soil organic carbon (SOC) is the largest carbon pool and a key property of ecosystems. Compared with traditional field surveys, remote sensing (RS) represents a more efficient approach to mapping SOC, especially in larger-scale areas. Hyperspectral imagery provides a great potential for SOC prediction, but predicting the SOC content at a regional scale for a given year remains a great challenge. Multispectral RS images (e.g., Landsat images) with middle spatial resolution can be used as an alternative to predict SOC amongst other images. However, multispectral images are commonly affected by cloud cover and lack information on the shallow soil surface and soil surface with vegetation. By contrast, synthetic aperture radar (SAR) images can capture shallow surface information, penetrate through vegetation and are not affected by cloud cover. In this study, a new approach was evaluated for the prediction of SOC content by integrating Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) images. Mainland Spain (hereafter Spain) was used as the study site. Soil samples from the Land Use and Coverage Area frame Survey (LUCAS) European topsoil dataset in 2009 were utilized to fit a random forest (RF) model of the relationships between SOC content and several covariates in Spain. SOC prediction using only the Landsat TM image and only the ALOS PALSAR image was established as benchmarks to verify the efficiency of the proposed approach. The results for Spain showed that the integrated approach holds the highest prediction accuracy, with R2, RPD and RMSE of 0.59, 1.98 and 9.27 g kg−1, respectively. The correlations between the SOC content and various covariates were investigated and discussed, and the derived indices presented high relationships with SOC. The proposed method can accurately map the SOC content covering a large area by combining spectral information on land cover from the Landsat TM and topsoil information from the ALOS PALSAR. Land covers and elevation are closely related to SOC prediction, where lands with high vegetation canopy density have high SOC content, and the SOC content in areas with slopes within 15° is mainly concentrated between 3 and 26 g kg−1.