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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

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  • 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
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    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
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