• Challenges in the Geo-Processing of Big Soil Spatial Data

    Liakos, L.; Panagos, P. Challenges in the Geo-Processing of Big Soil Spatial Data. Land 2022, 11, 2287. https://doi.org/10.3390/land11122287

    European Commission, Joint Research Centre (JRC), IT-21027 Ispra, Italy

    This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using advanced programming languages. Challenges to this study included the reproducibility of the results, their presentation in a communicative way, and the harmonization of complex heterogeneous data in space and time based on high standards of accuracy. Accuracy was especially important as the results needed to be identical at all spatial scales (from point counts to aggregated countrywide data). The geospatial modeling of soil requires analysis at multiple spatial scales, from the pixel level, through multiple territorial units (national or regional), and river catchments, to the global scale. Advanced mapping methods (e.g., zonal statistics, map algebra, choropleth maps, and proportional symbols) were used to convey comprehensive and substantial information that would be of use to policymakers. More specifically, a variety of cartographic practices were employed, including vector and raster visualization and hexagon grid maps at the global or European scale and in several cartographic projections. The information was rendered in both grid format and as aggregated statistics per polygon (zonal statistics), combined with diagrams and an advanced graphical interface. The uncertainty was estimated and the results were validated in order to present the outputs in the most robust way. The study was also interdisciplinary in nature, requiring large-scale datasets to be integrated from different scientific domains, such as soil science, geography, hydrology, chemistry, climate change, and agriculture.

  • European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies

    Panagos, P.a, Van Liedekerke, M.a, Borrelli, P.b, Köninger, J.a, Ballabio, C.a, Orgiazzi, A.a, Lugato, E.a, Liakos, L.a, Hervas, J.a, Jones, A.a, Montanarella, L.a, 2022. European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies. European J Soil Science. https://doi.org/10.1111/ejss.13315

    aEuropean Commission, Joint Research Centre (JRC), Ispra, Italy
    bDepartment of Science, Roma Tre University, Rome, Italy

    The European Soil Data Centre (ESDAC), hosted by the European Commission's Joint Research Centre (JRC), is the focal point for soil data, support to policy making and awareness raising for the European Union (EU). Established in 2006 to provide harmonised soil-related information for the EU Member States, and ESDAC currently hosts 88 datasets, 6000 maps, six atlases, 500 scientific publications, and a copious amount of soil-related material. Through its data repository publishing activity, ESDAC has licensed over 50,000 datasets during the past 15 years; 8500 of them in 2021 alone. It has published 140 monthly newsletters and is followed by more than 12,000 subscribed users, which receive regular updates. This article addresses the use, usability, and usefulness of ESDAC. About 75% of the ESDAC users come from academia and the research community while the remaining 25% includes public administration (at EU, national, regional, and local level) and the private sector. In addition, we provide some insights of the datasets evaluation and how they have been developed. The general ESDAC vision is to provide evidence underlying EU soil-relevant policies and to facilitate the access to relevant data for research. ESDAC is an integral part of the recently established European Union Soil Observatory (EUSO), with a target to have an even stronger role in supporting EU and regional policies.


    • ESDAC is a central place from where to find European wide relevant soil data.
    • ESDAC is an integral part of the EU Soil Observatory in creating data and knowledge for policy support.
    • The ESDAC website shows a high volume of traffic; 10,000 of user licenses are granted per year.
    • ESDAC key success: open access data policy, documentation, operational helpdesk and regular updates
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  • Improving the phosphorus budget of European agricultural soils

    Phosphorus budget per each region as kg P ha−1. The vertical bars show the annual sum of P inputs (violet) and outputs (green) per country (expressed in tonnes).

    Panagos, P.a, Köningner, J.a, Ballabio, C.a, Liakos, L.a, Muntwyler, A.a, Borrelli, P.b , Lugato, E.a, 2022. Improving the phosphorus budget of European agricultural soils. Science of The Total Environment 158706. https://doi.org/10.1016/j.scitotenv.2022.158706

    a European Commission, Joint Research Centre (JRC), Ispra, Italy
    b Department of Science, Roma Tre University, Rome, Italy

    Despite phosphorus (P) being crucial for plant nutrition and thus food security, excessive P fertilization harms soil and aquatic ecosystems. Accordingly, the European Green Deal and derived strategies aim to reduce P losses and fertilizer consumption in agricultural soils. The objective of this study is to calculate a soil P budget, allowing the quantification of the P surpluses/deficits in the European Union (EU) and the UK, considering the major inputs (inorganic fertilizers, manure, atmospheric deposition, and chemical weathering) and outputs (crop production, plant residues removal, losses by erosion) for the period 2011–2019.

    The Land Use/Cover Area frame Survey (LUCAS) topsoil data include measured values for almost 22,000 samples for both available and total P. With advanced machine learning models, we developed maps for both attributes at 500 m resolution. We estimated the available P for crops at a mean value of 83 kg ha−1 with a clear distinction between North and South. The ratio of available P to the total P is about 1:17.

    The inorganic fertilizers and manure contribute almost equally as P inputs (mean 16 ± 2 kg P ha−1 yr−1 at 90 % confidence level) to agricultural soils, with high regional variations depending on farming practices, livestock density, and cropping systems. The P outputs came mainly from the exportation by the harvest of crop products and residues (97.5 %) and, secondly, by erosion. Using a sediment distribution model, we quantified the P fluxes to river basins and sea outlets.

    In the EU and UK, we estimated an average surplus of 0.8 kg P ha−1 yr−1 with high variability between countries with some regional variations. The P annual budget at regional scale showed ample possibility to improve P management by both reducing inputs in regions with high surplus (and P soil available) and rebalancing fertilization in those at risk of soil fertility depletion.

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  • Global rainfall erosivity projections for 2050 and 2070

    Panagos, P.a, Borrelli, P.b, Matthews, F.a, Liakos, L.a, Bezak, N.c, Diodato, N.d, Ballabio, C.a, 2022. Global rainfall erosivity projections for 2050 and 2070. Journal of Hydrology 610, 127865. https://doi.org/10.1016/j.jhydrol.2022.127865

    aEuropean Commission, Joint Research Centre (JRC), Ispra, Italy
    bDepartment of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy
    cUniversity of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
    dMet European Research Observatory – International Affiliates Program of the University Corporation for Atmospheric Research, Benevento, Italy

    The erosive force of rainfall (rainfall erosivity) is a major driver of soil, nutrient losses worldwide and an important input for soil erosion assessments models. Here, we present a comprehensive set of future erosivity projections at a 30 arc-second (∼1 km2) spatial scale using 19 downscaled General Circulation Models (GCMs) simulating three Representative Concentration Pathways (RCPs) for the periods 2041–2060 and 2061–2080. The future rainfall erosivity projections were obtained based on a Gaussian Process Regression (GPR) approach relating rainfall depth to rainfall erosivity through a series of (bio)climatic covariates. Compared to the 2010 Global Rainfall erosivity baseline, we estimate a potential average increase in global rainfall erosivity between 26.2 and 28.8% for 2050 and 27–34.3% for 2070. Therefore, climate change and the consequential increase in rainfall erosivity is the main driver of the projected + 30–66% increase in soil erosion rates by 2070.

    Our results were successfully compared with 20 regional studies addressing the rainfall erosivity projections. We release the whole dataset of future rainfall erosivity projections composed of 102 simulation scenarios, with the aim to support further research activities on soil erosion, soil conservation and climate change communities. We expect these datasets to address the needs of both the Earth system modeling community and policy makers. In addition, we introduce a modeling approach to estimate future erosivity and make further assessments at global and continental scales.

  • Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity

    Nejc Bezak1, Pasquale Borrelli2,3, and Panos Panagos4

    1University of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
    2Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy
    3Department of Biological Environment, Kangwon National University, Chuncheon 24341, Republic of Korea
    4European Commission, Joint Research Centre (JRC), Ispra, Italy


    Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

    Mean global rainfall erosivity map for the 1998–2019 period based on the CMORPH product (a) and ED concept using ERA5 (b).
  • Phosphorus plant removal from European agricultural land

    Total phosphorus removal per country and region

    Panagos, Panosa, Anna Muntwylera, Leonidas Liakosa, Pasquale Borrellib, Irene Biavettia, Mariia Bogonosc, and Emanuele Lugatoa. 2022. “Phosphorus Plant Removal from European Agricultural Land.” Journal of Consumer Protection and Food Safety, February. https://doi.org/10.1007/s00003-022-01363-3.

    a European Commission, Joint Research Centre (JRC), Ispra, Italy
    b Dipartimento di Scienze della Terra e dell’Ambiente, Università degli Studi di Pavia, Pavia, Italy
    c European Commission, Joint Research Centre (JRC), Seville, Spain

    Phosphorus (P) is an important nutrient for all plant growth and it has become a critical and often imbalanced element in modern agriculture. A proper crop fertilization is crucial for production, farmer profits, and also for ensuring sustainable agriculture. The European Commission has published the Farm to Fork (F2F) Strategy in May 2020, in which the reduction of the use of fertilizers by at least 20% is among one of the main objectives. Therefore, it is important to look for the optimal use of P in order to reduce its pollution effects but also ensure future agricultural production and food security. It is essential to estimate the P budget with the best available data at the highest possible spatial resolution. In this study, we focused on estimating the P removal from soils by crop harvest and removal of crop residues. Specifically, we attempted to estimate the P removal by taking into account the production area and productivity rates of 37 crops for 220 regions in the European Union (EU) and the UK. To estimate the P removal by crops, we included the P concentrations in plant tissues (%), the crop humidity rates, the crop residues production, and the removal rates of the crop residues. The total P removal was about 2.55 million tonnes (Mt) (± 0.23 Mt), with crop harvesting having the larger contribution (ca. 94%) compared to the crop residues removal. A Monte-Carlo analysis estimated a ± 9% uncertainty. In addition, we performed a projection of P removal from agricultural fields in 2030. By providing this picture, we aim to improve the current P balances in the EU and explore the feasibility of F2F objectives.

    Total phosphorus removal per country and region
    Total phosphorus removal per country and region. Green bars aggregate P crop removal per country and brown ones are the aggregated P removal with residues
  • Mercury in European topsoils: Anthropogenic sources, stocks and fluxes

    Map of Hg stock (g ha−1) in European topsoils.

    Panos Panagosa, Martin Jiskrab, Pasquale Borrellic, Leonidas Liakosa, Cristiano Ballabioa. 2021. “Mercury in European Topsoils: Anthropogenic Sources, Stocks and Fluxes.” Environmental Research, June, 111556. https://doi.org/10.1016/j.envres.2021.111556.

    a. European Commission, Joint Research Centre (JRC), Ispra, Italy
    b. Environmental Geosciences, University of Basel, Switzerland
    c. Department of Earth and Environmental Sciences, University of Pavia, 27100, Pavia, Italy

    Mercury (Hg) is one of the most dangerous pollutants worldwide. In the European Union (EU), we recently estimated the Hg distribution in topsoil using 21,591 samples and a series of geo-physical inputs. In this manuscript, we investigate the impact of mining activities, chrol-alkali industries and other diffuse pollution sources as primary anthropogenic sources of Hg hotspots in the EU. Based on Hg measured soil samples, we modelled the Hg pool in EU topsoils, which totals about 44.8 Gg, with an average density of 103 g ha−1. As a following step, we coupled the estimated Hg stocks in topsoil with the pan-European assessment of soil loss due to water erosion and sediment distribution. In the European Union and UK, we estimated that about 43 Mg Hg yr−1 are displaced by water erosion and c. a. 6 Mg Hg yr−1 are transferred with sediments to river basins and eventually released to coastal Oceans. The Mediterranean Sea receives almost half (2.94 Mg yr−1) of the Hg fluxes to coastal oceans and it records the highest quantity of Hg sediments. This is the result of elevated soil Hg concentration and high erosion rates in the catchments draining into the Mediterranean Sea. This work contributes to new knowledge in support of the policy development in the EU on the Zero Pollution Action Plan and the Sustainable Development Goal (SDGs) 3.9 and 14.1, which both have as an objective to reduce soil pollution by 2030.

    Link to Dataset: Mercury content in the European Union topsoil

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  • Further adjustment of the Defense Meteorological Satellite Program-Operational Linescan System using radar data

    Fig. 2

    Stathakis, Demetris, and Leonidas Liakos. 2019. “Further Adjustment of the Defense Meteorological Satellite Program-Operational Linescan System Using Radar Data.” Journal of Applied Remote Sensing 13 (03): 034507/1-9. https://doi.org/10.1117/1.JRS.13.034507.

    University of Thessaly, Spatial Analysis/GIS and Thematic Mapping Laboratory,
    Department of Planning and Regional Development, Sekeri & Alamanas, Volos, Greece

    In the past, the Defense Meteorological Satellite Program—Operational Linescan
    System (DSMP/OLS) data have been successfully adjusted based on the presence of vegetation
    in each pixel. We propose a further adjustment based on radar data obtained by the SeaWinds
    scatterometer. It is shown that the adjustment results in substantial accuracy gains, measured as
    increased correlation with the more accurate Visible Infrared Imaging Radiometer Suite data as
    well as with the existing instances of radiance calibrated DSMP/OLS data. The proposed adjust-
    ment is easy to implement. The improvement of the proposed method is particularly significant
    in arid regions where vegetation is relatively sparse.

  • Median Shift Lunar Correction for VIIRS


    Stathakis, Demetris*, and Leonidas Liakos*. 2020. “Median Shift Lunar Correction for VIIRS.” IEEE Geoscience and Remote Sensing Letters, 1–5. https://doi.org/10.1109/LGRS.2020.3007965.

    * Planning and Development Department, University of Thessaly, 38334 Volos, Greece

    Visible Infrared Imaging Radiometer Suite (VIIRS) 24-h data are substantially affected by lunar illumination. A new method is proposed to correct this distortion and be able to form a consistent time series. The method has the advantage of being relatively simple in its deployment while at the same time effective. The results show that the proposed method removes periodical illumination noise due to the lunar circle while preserving meaningful information, in the test sites applied.

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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
This work by Leonidas Liakos is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported.