Browsing by Author "Lagacherie, Philippe"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Digital mapping of GlobalSoilMap soil properties at a broad scale: a review
    (Elsevier, 2021-11-30) Chen, Songchao; Arrouays, Dominique; Mulder, Vera Leatitia; Poggio, Laura; Minasny, Budiman; Roudier, Pierre; Libohova, Zamir; Lagacherie, Philippe; Shi, Zhou; Hannam, Jacqueline A.; Meersmans, Jeroen; Richer-de-Forges, Anne C.; Walter, Christian
    Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.
  • Thumbnail Image
    ItemOpen Access
    Soil legacy data rescue via GlobalSoilMap and other international and national initiatives
    (Elsevier, 2017-06-15) Arrouays, Dominique; Leenaars, Johan G. B.; Richer-de-Forges, Anne C.; Adhikari, Kabindra; Ballabio, Cristiano; Greve, Mogens; Grundy, Mike; Guerrero, Eliseo; Hempel, Jon; Hengl, Tomislav; Heuvelink, Gerard; Batjes, Niels; Carvalho, Eloi; Hartemink, Alfred; Hewitt, Alan; Hong, Suk-Young; Krasilnikov, Pavel; Lagacherie, Philippe; Lelyk, Glen; Libohova, Zamir; Lilly, Allan; McBratney, Alex; McKenzie, Neil; Vasquez, Gustavo M.; Mulder, Vera Leatitia; Minasny, Budiman; Montanarella, Luca; Odeh, Inakwu; Padarian, Jose; Poggio, Laura; Roudier, Pierre; Saby, Nicolas; Savin, Igor; Searle, Ross; Solbovoy, Vladimir; Thompson, James; Smith, Scott; Sulaeman, Yiyi; Vintila, Ruxandra; Viscarra Rossel, Raphael; Wilson, Peter; Zhang, Gan-Lin; Swerts, Martine; Oorts, Katrien; Karklins, Aldis; Feng, Liu; Ibelles Navarro, Alexandro R.; Levin, Arkadiy; Laktionova, Tetiana; Dell'Acqua, Martin; Suvannang, Nopmanee; Ruam, Waew; Prasad, Jagdish; Patil, Nitin; Husnjak, Stjepan; Pásztor, László; Okx, Joop; Hallett, Stephen H.; Keay, Caroline; Farewell, Timothy; Lilja, Harri; Juilleret, Jérôme; Marx, Simone; Takata, Yusuke; Kazuyuki, Yagi; Mansuy, Nicolas; Panagos, Panos; van Liedekerke, Mark; Skalsky, Rastislav; Sobocka, Jaroslava; Kobza, Josef; Eftekhari, Kamran; Kacem Alavipanah, Seyed; Moussadek, Rachid; Badraoui, Mohamed; Da Silva, Mayesse; Paterson, Garry; da Conceição Gonçalves, Maria; Theocharopoulos, Sid; Yemefack, Martin; Tedou, Silatsa; Vrscaj, Borut; Grob, Urs; Kozák, Josef; Boruvka, Lubos; Dobos, Endre; Taboada, Miguel; Moretti, Lucas; Rodriguez, Dario
    Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.