The PROSOIL project was financed by the Belgian Science Policy in the framework of the STEREO III programme. The project activities were made in collaboration between UCL and the Helmotz centre of Potsdam.
Project type: Innovation project
Duration: 24 months
Bas van Wesemael, Earth and Life Institute, Universitéc atholique de Louvain, Belgium
Fabio Castaldi, Earth and Life Institute, Université catholique de Louvain, Belgium
Sabine Chabrillat, Helmholtz-Zentrum Potsdam Deutsches GeoForschungs Zentrum (GFZ)
Study area: Belair, Hesbania, Central Belgium; Luxembourg; Demmin, Northern Germany
Remote sensing data used: APEX, HySpex, Sentinel 2, simulated EnMap
Context and objectives
Understanding the response of soils to external drivers to assist decision making at all scales requires precise spatially referenced soil data and maps. The first generation of hyperspectral satellites was characterized by low signal to noise ratios (e.g. Hyperion 190-40:1 as the wavelength increases) and their application for predicting soil properties was limited. Now that the Hyperion project will be shut down in 2016, the launch of a future generation of hyperspectral and multispectral imagers is being finalized. These satellites have the spectral resolution and signal to noise ratio (500-180:1 depending on the wavelength) required to provide high resolution data on a number of topsoil properties. Once the satellites are operational one can foresee an increasing demand for a robust methodology to derive soil products covering the size of one or a couple of tiles (e.g. for EnMAP: 30 x 30 km) throughout Europe, using imagery acquired under optimal soil and weather conditions. The objective of the PROSOIL project is to develop methods to produce up-to-date soil property data through Multivariate Calibration (MVC) of the signal from the new generation of hyperspectral and multispectral satellites.
Predicting soil properties from satellites requires calibration based on readily available soil spectral libraries (SSL) containing spectra and analytical parameters for large areas. As the new generation of hyperspectral satellites are not yet launched (or fully operational) and the Hyperion satellite will be shut down in 2016, we will demonstrate the potential to predict topsoil properties and validate this approach for three pilot areas using actual Sentinel-2 and EnMAP simulated satellite imagery derived from airborne HRS acquired during previous projects.
We developed a routine chemometrics approach to estimate SOC over large areas, using a continental scale and harmonized Soil Spectral Library (LUCAS database). This method is the first step of a new approach, named bottom-up, which allows SOC maps to be provided over a large area by airborne data without recourse to chemical analyses or any spectral transfer between laboratory and remote spectra, by exploiting the spectra and ancillary data of the European LUCAS topsoil database. This approach was tested on fields that were bare during the over flight in the three test areas. The results were compared to those provided by a direct calibration of the airborne spectra. The comparison between the independent validations of these two approaches did not show differences in terms of RMSE or RPD. The bottom-up approach was also tested using actual Sentinel-2 and simulated EnMAP data. The spatial resolution and the spectral quality provided by Sentinel-2 are the main assets that allow quantifying the in-field and regional variability of SOC content, while the low spatial resolution (30 m) of the new generation of hyperspectral satellite sensors may be a constraint for in-field characterization. The results obtained by the simulated EnMap showed a lower prediction accuracy than those obtained by airborne data (GSD: 4 m) in the same test site, moreover the EnMap data seem to be very sensitive to uncertainty of the SOC prediction process, especially to georeferencing error. The spatial resolution of the EnMap data could be a constraint to describe the SOC variation at small scale, however the EnMap data could be useful for regional soil characterization and for digital soil mapping. This step aimed to validate the derived soil products and evaluate the effects of degraded satellite signals on soil property prediction accuracy.
Products and services
The ultimate aim of the project is to demonstrate the feasibility of a semi-automated, robust and harmonized calibration for soil organic carbon prediction from hyperspectral satellite systems in areas dominated by croplands. This calibration will then become operational once the next generation of hyperspectral satellites is launched (EnMAP).The Sentinel-2 data proved to be promising for SOC monitoring over large area. In particular, the large amount of Sentinel-2 available data during the year can be exploited to detect croplands within the target image using Sentinel-2 multi-temporal series. In this regard, in order to improve SOC prediction accuracy obtainable by remote sensing data, an automatic processing chain should be developed merging an operational version of the bottom-up approach with algorithms for detecting the best in-field conditions (bare cropland soils, soil moisture, etc.). Although, the hyperspectral satellites have not been yet launched, such a processing chain can be first tested on Sentinel-2 and subsequently be applied to the forthcoming satellites.
The project demonstrated the feasibility and researchers, consultancy firms, and users or producers of soil information could use the proposed methodology to create soil property maps.
The main results of the PROSOIL project were presented during three international conferences and in two papers already published by European Journal of Soil Science and Remote Sensing.
Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., van Wesemael, B., 2018. Soil organic carbon estimation in croplands by hyperspectral remote APEX data using the LUCAS topsoil database. Remote Sens. 10. https://doi.org/10.3390/rs10020153
Castaldi, F., Chabrillat, S., Chartin, C., Genot, V., Jones, A.R., van Wesemael, B., 2018. Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12553
International conferences and workshops:
10thEARSeLSIG Imaging Spectroscopy Workshop 2017 (Zurich 19 –21 April 2017): Soil Organic carbon estimation in croplands by airborne Apex images using LUCAS topsoil database. (Oral presentation)
European Geoscience Union (EGU), General Assembly 2017 (Vienna 23 – 28 April 2017): Using LUCAS topsoil database to estimate soil organic carbon content in local spectral libraries. (Poster)
Pedometrics 2017 (Wageningen, 26th June - 1st July 2017): A routine chemometrics approach to estimate soil organic carbon in croplands exploiting LUCAS topsoil database. (Oral presentation)
EUFAR International Conference on Airborne Research for the Environment – ICARE (DLR Oberpfaffenhofen, 10-13 July 2017): Digital soil mapping from hyperspectral imagery: Potential and challenges (invited oral presentation)
European Geoscience Union (EGU), General Assembly 2018 (Vienna 8-13 April 2018): Soil organic carbon estimation in croplands by hyperspectral remote data using LUCAS topsoil database. (Oral presentation)
Whispers Conference, Amsterdam (23-26 September 2018): A comparison between Sentinel 2 and airborne hyperspectral data for soil organic carbon prediction in croplands (Poster)
The European LUCAS topsoil database in Ispra (Italy)
Soil sampling in Demmin (Germany)