Tom Hengl
Sessions
Description will be updated soon! Please, revisit this page.
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, geopandas, eumap).
Description will be updated soon! Please, revisit this page.
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, geopandas, eumap).
Description will be updated soon! Please, revisit this page.
Software requirements: opengeohub/py-geo docker image (gdal, rasterio, geopandas, eumap).
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.