Recent technological advances in both remote sensing and soil mapping approaches and progress in establishing harmonized soil profile datasets have opened up the potential to derive global gridded soil information. This has been possible because worldwide researchers have gained a growing experience in building standardized soil profile datasets with measured physical, chemical data and taxonomical information; filling data gaps; using Earth observation data for soil mapping; optimizing soil sampling strategy; processing big data; applying machine learning algorithms; and assessing uncertainty; which support the preparation of global soil maps with increasing accuracy and spatiotemporal resolution.
Data-intensive computing solutions to process and analyze the exploding amount of environmental information are continuously updated. Machine learning algorithms are among the most frequently used tools for data preprocessing and describing the complex relationship between soil properties and environmental covariates with the ability to assess the uncertainty of the predictions. One of the greatest challenges in deriving global gridded soil information is to make the most of the predictive power of machine learning algorithms with the continuously increasing amount of environmental information. This Special Issue is dedicated to machine learning-based methods in:
proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
preprocessing Earth observation data to feed into global soil mapping;
data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
optimizing temporal resolution to globally track the changes of soil properties,
uncertainty assessment of the derived gridded soil information;
specifying algorithms to local soil specificities in, e.g., proximal soil mapping;
the engagement of remote sensing data with digital soil mapping;
downscaling of large-scale soil feature;
other related topics.
Review contributions on the abovementioned topics are welcomed as well.Dr. Brigitta Szabó (Tóth) Prof.Dr. Eyal Ben-Dor Dr. Yijian Zeng Prof.Dr. Salvatore Manfreda Dr. Madlene Nussbaum Guest Editors
Salvatore Manfreda is Full Professor of Water Management, Hydrology and Hydraulic Constructions at the University of Naples Federico II. He is currently the Chair of the COST Action HARMONIOUS and the Scientific Coordinator of the Research Grant aimed at the Development of the Flood Forecasting System of the Basilicata Region Civil Protection. He has broad interest on distributed modeling, flood risk, stochastic processes in hydrology and UAS-based monitoring.
Featured image: Johansen, K., Duan, Q., Tu, Y.-H., Searle, C., Wu, D., Phinn, S., Robson, A., McCabe, M.F., 2020. Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery. ISPRS J. Photogramm. Remote Sens. 165, 28–40. https://doi.org/10.1016/j.isprsjprs.2020.04.017