Tomislav Hengl

Workshop room 2 - C223 Introduction to RStudio, starting packages and loading data, Introduction to spatiotemporal datasets, eumap spatiotemporal datasets example: landcover 2000-2020 training dataset (Witjes et al, 2021), Visualizing spatiotemporal data and data summaries,

Workshop room 2 - C223 Introduction to Ensemble Machine Learning: the mlr3 framework, Selecting learners, fine-tuning, feature selection and model stacking, Using Machine Learning with spatial and spatiotemporal data: Using ML for spatial interpolation: landmap package (vs geoR and similar geostatistical software), Adding geographical distances and features to spatial interpolation, Fitting and using EML for predicting eumap land cover data (Witjes et al, 2021),

Workshop room 2 - C223 Accessing COG files using QGIS, Using gdaltiler to produce plots in Google Earth (local copy), Finding trend lines using stacks of rasters,

Although number of pan-EU predictions of soil properties already exist (Toth et al, 2017; Ballabio et al., 2019) these are based on still relatively coarse resolutions (250-m) and ignore the time-component of soil variation and which is highly relevant for many chemical soil properties. For example, soil pH (Huang et al., 2022) and soil organic carbon (Knotters et al., 2022) have changed significantly in the last 20–30 years; mainly due to land use intensification and conversion of natural wetlands and similar. In addition, predictions produced using only LUCAS are top-soil focused (0–20 cm) even incompatible with international standards such as 0–30 cm used by UNCCD / IPCC. We have hence built methodology for mapping and monitoring soil nutrients using cutting-edge machine learning methods and state-of-the-art publicly available Earth Observation data (GLAD Landsat; Sentinel-2). Our initial models for predicting soil variables fitted using spatiotemporal overlays and results of cross-validation show that these models are significant. Several originally prepared 30-m resolution covariates (landsat products especially Red, NIR and SWIR bands, NDVI, SAVI) seem to correlate significantly with dynamic soil pH, carbon content and hence can be used to provide predictions at unprecedented levels of detail. The soil component of the Open Data Science Cube is referred to as ODSE-SOIL.