European high resolution soil quality products 

Abstract

High resolution reliable and quality controlled soil information products are fundamental for a range of applications related to climate change and sustainable management. Integration of remote sensing information is a key startegy to obtain more accurate and relevant products.   This contribution provides an overview about input-products, covariates and soil products including image examples and methodologies (digital soil mapping, DSM).  DSM uses a statistical model to integrate products derived from Copernicus satellites Sentinel-1 and 2, environmental covariates such as digital elevation models, ERA-5 products and soil ground truth information from the LUCAS survey and other available sources. Several data products are derived from the Sentinel-2 time series using the Soil Composite Mapping Processor (SCMaP). It comprises mean reflectance composites as well as specific soil reflectance composites (SRC) that contain undisturbed and bare soils. Additionally, the bare soil frequency is a measure for the visibility of the bare soil within the whole observed time period in percent. EO based soil products were generated for primary soil properties (SOC, pH, texture), derived soil properties and some basic soil health indicators. The soil observations were split in 10 equally sized folds for cross-validation. Random Forest models were obtained with the ranger package, with the option to build Quantile Random Forests (QRF) to obtain pixel-based uncertainty.    This contribution will discuss the advantages and pitfalls of using high resolution remote sensing products with a sparse point data sampling such as LUCAS.