  Soil organic carbon (SOC) sequestration is the transfer and storage of
atmospheric carbon dioxide in soils, which plays an important role in climate
change mitigation. SOC concentration can be improved by proper land use, thus
it is beneficial if SOC can be estimated at a regional or global scale. As
multispectral satellite data can provide SOC-related information such as
vegetation and soil properties at a global scale, estimation of SOC through
satellite data has been explored as an alternative to manual soil sampling.
Although existing studies show promising results, they are mainly based on
pixel-based approaches with traditional machine learning methods, and
convolutional neural networks (CNNs) are uncommon. To study the use of CNNs on
SOC remote sensing, here we propose the FNO-DenseNet based on the Fourier
neural operator (FNO). By combining the advantages of the FNO and DenseNet, the
FNO-DenseNet outperformed the FNO in our experiments with hundreds of times
fewer parameters. The FNO-DenseNet also outperformed a pixel-based random
forest by 18% in the mean absolute percentage error.

Image and Video Processing;Computer Vision and Pattern Recognition