Digital soil mapping and GlobalSoilMap. Main advances and ways forward

Journal Pre-proof Digital soil mapping and GlobalSoilMap. Main advances and ways forward

Dominique Arrouays, Laura Poggio, Osvaldo A. Salazar Guerrero, Vera Laetitia Mulder PII:

S2352-0094(20)30014-6

DOI:

https://doi.org/10.1016/j.geodrs.2020.e00265

Reference:

GEODRS 265

To appear in:

Geoderma Regional

Received date:

13 January 2020

Revised date:

27 February 2020

Accepted date:

28 February 2020

Please cite this article as: D. Arrouays, L. Poggio, O.A. Salazar Guerrero, et al., Digital soil mapping and GlobalSoilMap. Main advances and ways forward, Geoderma Regional(2020), https://doi.org/10.1016/j.geodrs.2020.e00265

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Journal Pre-proof Digital Soil Mapping and GlobalSoilMap. Main advances and ways forward Dominique Arrouays 1, Laura Poggio2 , Osvaldo A. Salazar Guerrero3, Vera Laetitia Mulder4 1. INRAE, InfoSol Unit, F-45075 Orléans, France 2. ISRIC-World Soil Information, PO Box 353, 6700 AJ, Wageningen, The Netherlands 3. University of Chile, Faculty of Agricultural Sciences, Campus Antumapu, Santa Rosa 11315, La Pintana, Santiago, Chile

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4. Soil Geography and Landscape group, Wageningen University, PO Box 47 6700 AA

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Wageningen, The Netherlands

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Abstract

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In this concluding paper, we summarize the main advances coming forward from the joint conference of the International Union of Soil Sciences (IUSS) Working Groups (WG) “Digital Soil

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mapping” (DSM) and “GlobalSoilMap”. We outline the increased availability of data and covariates.

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Large efforts to rescue legacy data and to put them in a harmonized format are ongoing in many parts of the world. New countries are joining the GlobalSoilMap initiative. During the same time,

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significant progress have been made in the countries which were among the first to develop GlobalSoilMap products. We stress the recent trends in tools used for predictive mapping of soil properties. Some solutions were proposed to solve issues about data privacy. We give examples on how to move from DSM soil digital soil mapping assessment. Aligning our research with ongoing activities within the Global Soil Partnership of the FAO has been proven successful. A need was expressed to work on the uncertainty of indicators of prediction performances and to re-evaluate validation strategies. It is necessary to develop more intuitive metrics for uncertainty assessment for interpreting and evaluating soil maps. The main progresses, remaining issues and challenges and the way forward are summarized and we propose ambitious working plans and road-maps for the two WGs and stress their complementarities.

Journal Pre-proof Keywords: Digital Soil Mapping, GlobalSoilMap, achievements, challenges, working plans, multiple soil classes 1. Introduction The International Union of Soil Sciences (IUSS) Working Groups (WG) “Digital Soil mapping” (DSM) and “GlobalSoilMap” have strong links. The DSM WG aims at providing scientific advances in the field of DSM at all scales and spatial extents; to exchange and share new methods, tools and concepts and

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to suggest how to use DSM products for further assessment of soil quality. The GlobalSoilMap WG

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has a more operational objective, in particular to support the production of global soil grids of soil

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properties. These are based on ‘bottom-up’ (from countries to globe) approaches where possible, integrated with ‘top-down’ (from globe to countries) approaches when necessary. This integration

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allows to fill the gaps where countries products are missing, to help in solving the border effects and

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to ultimately achieve a harmonized common product. Both WGs have common scientific and methodological interests, such as uncertainty assessments, spatial predictions and integration of

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data, predictions and models. Therefore, the WGs held a joint workshop in 2019 in Santiago, Chile,

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on 12-16 March 2019 (https://sites.google.com/view/soilmapping2019/). This joint workshop

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gathered 65 participants from 14 countries from 5 continents. The presentations included two keynotes, one by Laura Poggio (abstract at: https://sites.google.com/view/soilmapping2019/) and the other one by Axel Schmidt (Owens et al., this special issue). Overall, the workshop included 32 oral and 11 poster presentations. Some of the results are presented in this special issue of Geoderma Regional, together with other papers related to the same topics. The aims of this concluding paper are: i) to summarize the main recent advances and achievements that emerged from the workshop, ii) to outline the main issues and challenges to tackle in the near future and iii) to define a road map with priorities for the next few years. 2.

Main recent advances and achievements

2.1. Increased availability of data and covariates

Journal Pre-proof Legacy and new soil data are becoming available, at global (worldwide), continental and national scales. Large efforts to rescue legacy data and to put them in a harmonized format are ongoing in many parts of the world. The amount of soil profile data available for global projects such as SoilGrids is continuously increasing (from about 118,000 in 2015 to about 230,000 in 2019). Tremendous efforts have been made in some Latin American countries (e.g., Brazil (Samuel-Rosa et al., 2020), Chile (Padarian et al., 2017, Olmedo et al., 2019), and many other countries from South and Central America (Guevara et al., 2018) to rescue legacy data. The European Union is

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achieving a new campaign of LUCAS-SOIL, which will support the updating of E.U. maps of soil properties, provide further data and initiate the possibility to monitor changes at continental

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scale. New campaigns for soil monitoring and/or updating soil maps are ongoing at national scale

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in countries such as India, China, France and the Netherlands. New covariates are becoming

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available, especially from earth observation data and derived products with high spatial and temporal resolutions (see Section 3.5). These new covariates represent a huge amount of

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potential useful information to map soils.

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2.2. Machine learning

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The most prominent trend in the methods used in DSM and GlobalSoilMap is the increased use of machine learning methods. That includes even the use of deep learning for predicting soil attributes (Wadoux, 2019a; Wadoux et al., 2019a; Padarian et al. 2019a, 2019b). 2.3. Issues about data privacy Issues about data privacy are crucial, especially in the context of building global -scale models and products. These issues arise from different legislations between countries, or even between subregions of a country. Aside from legislation issues, it also arises from the perception of data producers about losing the intellectual property of their data (Arrouays et al., 2020). Some solutions were proposed during the workshop, including merging predictions without sharing the original point data (Caubet et al., 2019), or merging models using a block-chain approach (Padarian et al., 2019c).

Journal Pre-proof 2.4. Moving from DSM to Digital Soil Assessment The impact of DSM would be larger, if we are capable to translate our primary soil property products into information that is adjusted to end-user needs. This can be achieved through Digital Soil Assessments (DSA). Although the idea of moving from DSM to DSA is not new (e.g., Carré et al., 2007; Minasny et al., 2012), practical examples remain quite few (e.g., Grenier et al., 2018, Kidd et al., 2018, Chen et al., 2019a, Artz et al., 2019) and are, to date, almost absent at the global level. Some examples of DSA related to soil functions were presented aiming at applying DSA at a global level,

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using for instance SoilGrids (Hengl et al., 2017) and its updated version

(https://files.isric.org/soilgrids/data/recent/) as an input for soil data. Indeed, DSA is strongly context

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and client dependent, and there is a need to develop global DSA products in line with international

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conventions and treaties and to show how soil information can help international organizations such

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as IPCC and IPBES.

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2.5. GlobalSoilMap progress

Outreach to the global soil science community has been a priority for the GlobalSoilMap WG.

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Therefore, the WG was pleased to welcome the involvement of new very large countries, India

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(Dharumarajan et al., 2019), China (e.g. Liang et al., 2019; Liu et al., in press) and of smaller ones (e.g., The Netherlands, Van den Berg et al., 2017). This strong involvement of new countries indicates that new country-based products will be delivered and that the GlobalSoilMap WG will enlarge its participants and its geographical coverage. Note that during the same time, significant progress have been made in the countries which were among the first to develop GlobalSoilMap products (e.g., USA (e.g., Chaney et al., 2020), Australia (e.g., Kidd et al., 2018; Searle et al., this issue), France (e.g., Roman Dobarco et al., 2019; Chen et al., 2019b, 2020), Denmark (e.g., Moller et al., 2019; Tabatabai et al., 2019)). 2.6. Agreement with the Global Soil Partnership of the UN-FAO

Journal Pre-proof Aligning our research with ongoing activities within the Global Soil Partnership has proven successful. The pillar 4 of the Global Soil Partnership (GSP-P4) aims to deliver, among other products, global fine grids of soil properties. The first example of this kind of fine grids was the launch of the Global Soil Organic Carbon map (GSOC, http://www.fao.org/global-soil-partnership/pillars-action/4information-and-data-new/global-soil-organic-carbon-gsoc-map/en/) at the end of 2017, in support of the Sustainable Development Goal Indicator 15.3.1: Proportion of land that is degraded over total land area. The quality of soil carbon information at the global level is still limited because a lot of

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existing national information has not yet been shared. Furthermore, there is a lack of data harmonization and uncertainty information is often not available. During the workshop, an in -depth

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discussion took place to enable more integration of science into institutional workflows and to

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ensure that the GlobalSoilMap WG and GSP-P4 will develop harmonized products for a mutual

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benefit. This led to the endorsement by the GSP plenary of a text stipulating that the GlobalSoilMap WG is now officially part of the GSP-P4 WG and in charge of defining the specifications and the

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‘cookbooks’ for delivering new fine gridded products. A representative of the GlobalSoilMap WG will

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also be invited to the meetings of the International Network of Soil Information Institutions (INSII, http://www.fao.org/global-soil-partnership/pillars-action/4-information-data/insii/en/) annual

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meetings. Conversely, a representative of GSP-P4 WG will be invited to GlobalSoilMap WG workshops. This strong connection between these two bodies is essential for several reasons i) providing scientific advice about the GSP-P4 WG projects, ii) avoiding the multiplicity of global maps for the same properties, and, iii) ensuring that bottom-up country maps of soil properties produced following GlobalSoilMap specifications can be integrated into global predictions released under the framework of the GSP-P4 and vice-versa.. 3. Challenges and way forward 3.1. Better connecting with society, end users and policy-makers

Journal Pre-proof The discussions outlined the need for a better connectivity with society, end-users and policy makers. This should include better communication and collaboration with social sciences, for improved linkages with scenarios and policy makers. Several communications (e.g., Arrouays et al., 2020; Voltz et al., this issue) pointed out the need for a real interaction and collaboration with all categories of end-users to connect soil map products with their conceptual models. This includes establishing efficient connections between policy-makers and soil scientists. Moreover, we as soil scientists should move to better promote the role of soil sustainable management for achieving the

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Sustainable Development Goals (Keestra et al., 2016, Bouma, 2019) and better integrate soil in international conventions and treaties. Another general framework for communicating to policy

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makers could be the concept of soil security (McBratney et al., 2013; Koch et al., 2014) . Soil security

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connects the soil with six major global issues (food security, water security, climate change,

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sustainable energy, biodiversity protection, and human health). For more local levels of policy action, like land use planning or urban development, the multi-functionality of soils for delivering ecosystem

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services could be a key to facilitate communication and raising soil awareness in land-use decision

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process. For local actors, such as farmers, interactive tools helping them to foresee the consequences of changes in land use and management appeared useful and feasible to increase their soil

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awareness. Story maps were also proposed as a way to generating inquiry and visualizing information (Egiebor and Foster, 2019) and as a tool to empower and engage stakeholders in planning (Scott et al., 2016). A general consensus, outlined by several communications (e.g., Arrouays et al., 2020; Voltz et al., this issue) was the difficulty to communicate about uncertainty of DSM products. For this issue there is a clear need to better explain what each performance indicator means from a practical point of view and to carry out works on map semiology to give a clearer picture of uncertainty and what it means. 3.2. Facilitate the production of digital soil map

Journal Pre-proof DSM has progressively moved from a scientific to an operational activity (e.g. Minasny and McBratney, 2016). However, there is still a crucial need to facilitate its local production by capacity building. A lot of efforts have been made by some institutions (e.g., ISRIC-World Soil Information, University of Sydney, NRCS-USDA, GSP-P4) to disseminate knowledge and know-how in DSM, but these efforts should be pursued and more actors should be involved in capacity building. In addition, there is also a need to integrate more DSM and pedometrics in the universities’ curriculum.

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3.3. Integrate and generate pedological knowledge

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There was a general concern that using more and more machine learning and deep-learning,

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methods seemed to focus more on prediction performance and forget the importance of pedological knowledge for DSM and the use of DSM for understanding the role of the controlling factors of soil

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properties distribution. A need was expressed to open the “black boxes” of machine learning

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methods and to integrate more pedological knowledge, especially in the choice of the co-variates used for prediction. The general agreement was that DSM offers the room both for ”Data Science”

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evolution (Wadoux et al., 2019b).

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for soil attributes predictions and more fundamental research about the factors governing soil

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3.4. The need for new, and up to date data Some discussions focussed on the strategies of sampling design for acquiring new data. This is especially the case when no previous data is available. As stated by Wadoux (2019b) there is no single optimal sampling design, even if we may assume that systematic grids completed by pairs of closest points may be preferable when 1) we establish a national network from scratch without knowing which properties of interest will be measured and 2) we can assume that the model of spatial variation will be different depending of the soil properties. On the other hand, the strategy for the location of new data in already mapped areas may depend on the objectives of the campaign, such as to refine the maps in the more uncertain areas or to establish a design enabling soil monitoring. There is also still a big effort to pursue the rescuing of legacy data ( eg., Arrouays et al.,

Journal Pre-proof 2017; Guevara et al., 2018). This will need methods for updating outdated data, including their uncertainty and for a better harmonization of both laboratory measurements and soil classification. Acquiring data from the past and in the future should open the door to 4-D DSM, adding a temporal component to the three x, y, z, components. Data interoperability and harmonization should be done in close collaboration with the activities of the GSP Pillar 5 on data harmonization and the Global Soil Laboratory Network (GLOSOLAN, http://www.fao.org/global-soil-partnership/pillars-action/5harmonization/glosolan/en/) established by the GSP.

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3.5. Sensing data integration

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There is need to explore the potential of new high resolution and high temporal remote sensing data, offered by satellites such as Sentinel (Loiseau et al., 2019; Calstadi et al., 2019; Vaudour et al., 2019a,

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2019b) or HySIS (https://www.isro.gov.in/Spacecraft/hysis). Hyperspectral data from HySIS should

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become available soon for DSM and digital soil monitoring of some soil properties. Also, DSM typically doesn’t often make use of airborne hyperspectral data although various publications having

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proven the complementary information it may offer (e.g. Ben-Dor et al. 2009; Castaldi et al. 2018).

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Airborne gamma-ray spectrometry, although not available in many parts of the world, appears to be

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also a promising co-variate (Reinhardt and Herrmann, 2019). Progress in proximal sensing and the development of (national) spectral libraries should increase the number of observations which would benefit soil monitoring. Nevertheless, such data does have higher uncertainty and if this data would be used for soil mapping and monitoring, methods are required that take this uncertainty into account in DSM and DSA. 3.6. Uncertainty and Validation A need was expressed to work on the uncertainty of indicators of predi ction performances (Lagacherie et al., 2019) and to re-evaluate validation strategies. It is necessary to develop more intuitive metrics for uncertainty assessment for interpreting and evaluating soil maps. Moreover, this allows to better communicate uncertainties to stakeholders and policymakers to reduce the risk of

Journal Pre-proof failure in projects and policymaking. Validation of maps could benefit from incorporating pedological knowledge into the map evaluation. This may be achieved, for instance by looking at spatial correlations between mapped soil properties, and checking if the spatial patterns make sense from a soil science point of view. In some cases, people who make the maps do not have the knowledge to evaluate the maps the way a trained soil scientist would do. This would allow a more comprehensive strategy for map validation. More generally, databases used for DSM typically contain soil data originating from various sources, including different projects and (old) soil maps. This results in a

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database with soil information having a large diversity in uncertainty, which will have to be taken into account for spatial modelling.

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3.7. Covariates

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There is significant work required to improve the rational base behind the use of covariates. E.g. the

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number of co-variates used versus the number of calibration points, the spatial resolution of the covariates, and especially the pedological significance of the co-variates. In some cases quite

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satisfactory indicators of the prediction performance of a property could be obtained by using totally

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irrelevant co-variates (Wadoux et al., 2019b). New co-variates are also needed to capture historical

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changes in landscape and land-use, especially for soil properties having quite rapid changes with time (e.g. soil P, soil pH, soil organic carbon). The use of other databases, such as livestock densities should be explored too.

4. Working Groups working plans and road-maps In this section we synthetize the main actions identified following the general final discussion of the workshop. In order to keep this paper short we summarize them as bullet points. 4.1. GlobalSoilMap Working Group

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Update the basic specifications of GlobalSoilMap and revise the specifications of new GSP-P4 products.

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Work on harmonization and standardization of both point data and between national, continental and global products, in close collaboration with GSP-P5 and GLOSOLAN.

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Compare bottom up (country to globe) and top down (globe to country) products, assess their complementarity and options to merge them.

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Move from global soil properties to global soil functions and threats to soil, following the requirements of GSP-P4

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Develop some user cases and success stories.

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Get more countries involved into the initiative.

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Connect the INSII with GlobalSoilMap on a national level (which may be a way to achieve the

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afore mentioned target).

Define a strategy for data sharing & data repository, such as for instance using GLOSIS.

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Get a national endorsement of country based GlobalSoilMap products so as they can be

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submitted to GSP as national official contributions. 

Invest in capacity building and strengthen educational programmes.

4.2. Digital Soil Mapping Working Group

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Improve uncertainty assessment, measures, evaluation and its communication to end users.

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Explore and explain the meaning and interpretation of machine learning models, from the scientific and technical sides, but especially from the “pedological” one.

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Develop methods for a qualitative evaluation of the results.

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Develop and assess methods to evaluate the similarity of spatial patterns in maps produced with different methods and data, or legacy maps produced with traditional soil mapping approaches.

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Focus on digital soil assessment of soil and functional soil maps derived from soil property maps. Develop guidelines and best-practices on covariates selection and validation strategies.

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Address the time-dimension challenges,and ensure a better connection to the IUSS WG on

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Soil Monitoring

Integrate remote and proximal sensing into DSM and sampling methodologies

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5. Conclusions

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Significant progress and achievements have been reached during the last years and have been

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presented and discussed in-depth during the joint conference of the IUSS WGs “Digital Soil mapping” (DSM) and “GlobalSoilMap”. Both WGs have common scientific and methodological goals and

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significant advances were achieved. These advances include progress in science but also in

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organizational and policy issues. To our present knowledge, it is the f irst time that a final discussion was focused on the main remaining challenges and issues and to developed a detailed road-map for these two WGs for the next years. Making significant advances on all the topics mentioned in the road-maps is really challenging and needs to reinforce the links between these two WGs and also to ensure the link with the GSP-P4 and P5 initiatives. The success of the Santiago workshop should be confirmed by further joint meetings that are already planned (the next one being planned in Goa, India, in 4-18 December 2020; https://sites.google.com/view/soilmapping2020/) and by dedicated sessions in various international conferences. Acknowledgements

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Journal Pre-proof Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., Martin, M.P., Saby, N.P.A., 2019. How far can the uncertainty on a Digital Soil Map be known?: a numerical experiment using pseudo values of clay content obtained from Vis-SWIR Hyperspectral imagery. Geoderma. 337, 1320-1328. Liang, Z., Chen, S., Yang, Y., Zhou, Y., Shi, Z., 2019. High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling. Science of The Total Environment. 685, 480-489.

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B., 2019. Improved disaggregation of conventional soil maps. Geoderma 341, 148-160. Olmedo, G., Pfeiffer, M., Guevara, M., Osorio, R., Bustamante, N., 2019. Building the Chilean soil organic carbon dataset. Abstracts of the Soil Mapping 2019 Conference, Santiago, Chile, 12-16 March 2019. https://sites.google.com/view/soilmapping2019/ Owens, P.R., Dorantes, M.J., Fuentes, B.A., Libohova Z., Schmidt, A., This special issue. Taking Digital Soil Mapping to the Field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America. Geoderma Regional, This special issue. Padarian, J., Minasny, B., McBratney, A.B., 2017. Chile and the Chilean soil grid: A contribution to GlobalSoilMap. Geoderma Regional, 9, 17-28.

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Searle, R., McBratney, A.B., Grundy, M., Kidd, D., Malone, B., Arrouays, D., Stockman., U, Zund, P., Wilson, P., Wilford, P., Van Gool, D., Triantafilis, J., Thomas, M., Stower, E., Slater, B., Robinson, N., Ringrose-Voase, A., Padarian, J., Payne, J., Orton, T., Odgers, N., O’Brien, L., Minasny, B., Meier, E., McLean Bennett, J., Liddicoat, C., Jones, E., Holmes, K., Harms, B., Gray, J., Bui, E., This special issue. Digital Soil Mapping and Assessment for Australia and Beyond: A Propitious Future. Geoderma Regional, this special issue. Scott M.S., Edwards, S., Dayan, S., Nguyen, T., Cragle, J, 2016. GIS story maps: A tool to engage stakeholders in planning sustainable places. Final Report. MATS-UTC, Charlottesville, VA, USA.

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a prospective analysis in France. Geoderma Regional. This special issue. Wadoux, A.M.J-C., 2019a. Using deep learning for multivariate mapping of soil with quantified uncertainty. Geoderma, 351, 59-70.

Wadoux, A.M.J-C., 2019b. Sampling design optimization for geostatistical modelling and prediction. PhD Thesis. Wageningen University. 178 p. Wadoux, A.M. J-C., Padarian, J., Minasny, B., 2019a. Multi-source data integration for soil mapping using deep learning. SOIL, 5, 1, 107-119.

Journal Pre-proof Wadoux, A.M.J‐C., Samuel‐Rosa, A., Poggio, L., Mulder, V.L., 2019b. A note on knowledge discovery and machine learning in digital soil mapping. Eur J Soil Sci. Accepted Author Manuscript.

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Journal Pre-proof Highlights

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Summary of the main advances and ways forward of DSM and GlobalSoilMap Working Groups Increased use of machine learning for mapping soil properties Better connection with society, users and policy-makers is one of the main challenge Working plans and road-maps are proposed for DSM and GlobalSoilMap WGs

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