IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0170478.html
   My bibliography  Save this article

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

Author

Listed:
  • Gerald Forkuor
  • Ozias K L Hounkpatin
  • Gerhard Welp
  • Michael Thiel

Abstract

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Suggested Citation

  • Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    • Handle: RePEc:plo:pone00:0170478
    DOI: 10.1371/journal.pone.0170478
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0170478
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0170478&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0170478?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Bationo, Andre & Kihara, Job & Vanlauwe, Bernard & Waswa, Boaz & Kimetu, Joseph, 2007. "Soil organic carbon dynamics, functions and management in West African agro-ecosystems," Agricultural Systems, Elsevier, vol. 94(1), pages 13-25, April.
    3. Mareike Ließ & Johannes Schmidt & Bruno Glaser, 2016. "Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-22, April.
    4. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    5. Tomislav Hengl & Gerard B M Heuvelink & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Keith D Shepherd & Andrew Sila & Robert A MacMillan & Jorge Mendes de Jesus & Lulseged Tamene & Jérôme E Tond, 2015. "Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chang Meng & Mei Hong & Yuncai Hu & Fei Li, 2024. "Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas," Sustainability, MDPI, vol. 16(10), pages 1-23, May.
    2. Kangbéni Dimobe & Jean Léandre N’djoré Kouakou & Jérôme E. Tondoh & Benewinde J.-B. Zoungrana & Gerald Forkuor & Korotimi Ouédraogo, 2018. "Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas," Land, MDPI, vol. 7(4), pages 1-21, October.
    3. Shiny Abraham & Chau Huynh & Huy Vu, 2019. "Classification of Soils into Hydrologic Groups Using Machine Learning," Data, MDPI, vol. 5(1), pages 1-14, December.
    4. Chenxia Hu & Alan L Wright & Gang Lian, 2019. "Estimating the Spatial Distribution of Soil Properties Using Environmental Variables at a Catchment Scale in the Loess Hilly Area, China," IJERPH, MDPI, vol. 16(3), pages 1-14, February.
    5. Jasmina Defterdarović & Lana Filipović & Filip Kranjčec & Gabrijel Ondrašek & Diana Kikić & Alen Novosel & Ivan Mustać & Vedran Krevh & Ivan Magdić & Vedran Rubinić & Igor Bogunović & Ivan Dugan & Kre, 2021. "Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    6. Fatemeh Sadat Hosseini & Myoung Bae Seo & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Mohammad Jamshidi & Soo-Mi Choi, 2023. "Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting," Sustainability, MDPI, vol. 15(19), pages 1-25, September.
    7. Wu Xiao & Wenqi Chen & Tingting He & Linlin Ruan & Jiwang Guo, 2020. "Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    8. Fahao Wang & Weidong Lu & Jingyun Zheng & Shicheng Li & Xuezhen Zhang, 2020. "Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    9. Ramalingam Kumaraperumal & Sellaperumal Pazhanivelan & Vellingiri Geethalakshmi & Moorthi Nivas Raj & Dhanaraju Muthumanickam & Ragunath Kaliaperumal & Vishnu Shankar & Athira Manikandan Nair & Manoj , 2022. "Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India," Land, MDPI, vol. 11(12), pages 1-26, December.
    10. Mi Tian & Chao Wu & Xin Zhu & Qinghai Hu & Xueqiu Wang & Binbin Sun & Jian Zhou & Wei Wang & Qinghua Chi & Hanliang Liu & Yuheng Liu & Jiwu Yang & Xurong Li, 2024. "Spatial–Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009–2023)," Land, MDPI, vol. 13(7), pages 1-18, July.
    11. Guillermo Martínez Pastur & Marie-Claire Aravena Acuña & Jimena E. Chaves & Juan M. Cellini & Eduarda M. O. Silveira & Julián Rodriguez-Souilla & Axel von Müller & Ludmila La Manna & María V. Lencinas, 2023. "Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics," Land, MDPI, vol. 12(5), pages 1-18, April.
    12. Nausheen Mazhar & Safdar Ali Shirazi, 2023. "Community perceptions of the impacts of desertification as related to adaptive capacity in drylands of South Punjab, Pakistan," Asia-Pacific Journal of Regional Science, Springer, vol. 7(2), pages 549-568, June.
    13. Clement Nyamekye & Michael Thiel & Sarah Schönbrodt-Stitt & Benewinde J.-B. Zoungrana & Leonard K. Amekudzi, 2018. "Soil and Water Conservation in Burkina Faso, West Africa," Sustainability, MDPI, vol. 10(9), pages 1-24, September.
    14. Seyedalireza Khatibi & Azadeh Aghajanpour, 2020. "Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field," Energies, MDPI, vol. 13(14), pages 1-16, July.
    15. Jules Degila & Ida Sèmévo Tognisse & Anne-Carole Honfoga & Sèton Calmette Ariane Houetohossou & Fréjus Ariel Kpedetin Sodedji & Hospice Gérard Gracias Avakoudjo & Souand Peace Gloria Tahi & Achille Ep, 2023. "A Survey on Digital Agriculture in Five West African Countries," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    16. Kingsley JOHN & Isong Abraham Isong & Ndiye Michael Kebonye & Esther Okon Ayito & Prince Chapman Agyeman & Sunday Marcus Afu, 2020. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil," Land, MDPI, vol. 9(12), pages 1-20, December.
    17. Fuat Kaya & Gaurav Mishra & Rosa Francaviglia & Ali Keshavarzi, 2023. "Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity," Land, MDPI, vol. 12(4), pages 1-20, April.
    18. Sawadogo, Alidou & Dossou-Yovo, Elliott R. & Kouadio, Louis & Zwart, Sander J. & Traoré, Farid & Gündoğdu, Kemal S., 2023. "Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information," Agricultural Water Management, Elsevier, vol. 278(C).
    19. Sumin Park & Haemi Park & Jungho Im & Cheolhee Yoo & Jinyoung Rhee & Byungdoo Lee & ChunGeun Kwon, 2019. "Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.
    20. Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    21. Showmitra Kumar Sarkar & Saifullah Bin Ansar & Khondaker Mohammed Mohiuddin Ekram & Mehedi Hasan Khan & Swapan Talukdar & Mohd Waseem Naikoo & Abu Reza Towfiqul Islam & Atiqur Rahman & Amir Mosavi, 2022. "Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    22. Zhihui Li & Yang Yang & Siyu Gu & Boyu Tang & Jing Zhang, 2021. "Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    23. Chipidza, Wallace & Yan, Jie, 2020. "Does Flagging POTUS’s Tweets Lead to Fewer or More Retweets? Preliminary Evidence from Machine Learning Models," SocArXiv 69hkb, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masoud Zolfaghari Nia & Mostafa Moradi & Gholamhosein Moradi & Ruhollah Taghizadeh-Mehrjardi, 2022. "Machine Learning Models for Prediction of Soil Properties in the Riparian Forests," Land, MDPI, vol. 12(1), pages 1-15, December.
    2. Fuat Kaya & Gaurav Mishra & Rosa Francaviglia & Ali Keshavarzi, 2023. "Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity," Land, MDPI, vol. 12(4), pages 1-20, April.
    3. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    4. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    5. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
    6. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    7. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    8. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    9. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    10. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    11. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    12. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    13. Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," GLO Discussion Paper Series 1445, Global Labor Organization (GLO).
    14. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    15. Meyer, Maximilian & Hulke, Carolin & Kamwi, Jonathan & Kolem, Hannah & Börner, Jan, 2022. "Spatially heterogeneous effects of collective action on environmental dependence in Namibia’s Zambezi region," World Development, Elsevier, vol. 159(C).
    16. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    17. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    18. Johannes Schmidt & Nik Usmar & Leon Westphal & Max Werner & Stephan Roller & Reinhard Rademacher & Peter Kühn & Lukas Werther & Aline Kottmann, 2023. "Erosion Modelling Indicates a Decrease in Erosion Susceptibility of Historic Ridge and Furrow Fields near Albershausen, Southern Germany," Land, MDPI, vol. 12(3), pages 1-11, February.
    19. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    20. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0170478. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.