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Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover

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  • Mulenga Kalumba

    (Department of Earth and Environmental Sciences, University of Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
    Department of Agricultural Engineering, School of Engineering, The University of Zambia, Lusaka P.O. Box 32379, Zambia)

  • Stefaan Dondeyne

    (Department of Geography, Ghent University, Krijgslaan 281 S8, 9000 Gent, Belgium)

  • Eline Vanuytrecht

    (Department of Earth and Environmental Sciences, University of Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
    Environmental Modelling, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium)

  • Edwin Nyirenda

    (Department of Civil and Environmental Engineering, School of Engineering, The University of Zambia, Lusaka P.O. Box 32379, Zambia)

  • Jos Van Orshoven

    (Department of Earth and Environmental Sciences, University of Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium)

Abstract

Soil maps can usefully serve in data scarce regions, for example for yield (gap) assessments using a crop simulation model. The soil property estimates’ contribution to inaccuracy and uncertainty can be functionally evaluated by comparing model results using the estimates as input against independent observations. We conducted a functional evaluation of digital maps of soil hydraulic properties of the Zambezi River Basin using a crop growth model AquaCrop. AquaCrop was run, alimented with local meteorological data, and with soil hydraulic properties derived from the digital maps of digital soil mapping (DSM) techniques, as opposed to estimations from the widely used Saxton and Rawls pedotransfer functions. The two simulated time series of canopy cover (CC) (AquaCrop-CC-DSM and AquaCrop-CC-Saxton), which were compared against canopy cover data derived from the remotely sensed Leaf Area Index (LAI) from the MODIS archive (MODIS-CC). A pairwise comparison of the time series resulted in a root mean squared error (RMSE) of 0.07 and a co-efficient of determination (R 2 ) of 0.93 for AquaCrop-CC-DSM versus MODIS-CC, and an RMSE of 0.08 and R 2 of 0.88 for AquaCrop-CC-Saxton versus MODIS-CC. In dry years, the AquaCrop-CC-DSM deviated less from the MODIS-CC than the AquaCrop-CC-Saxton ( p < 0.001), although this difference was not significant in wet years. The functional evaluation showed that soil hydraulic property estimates based on digital soil mapping outperformed those based on Saxton and Rawls when used for simulating crop growth in dry years in the Zambezi River Basin. This study also shows the value of conducting a functional evaluation of estimated (static) soil hydraulic properties in terms of dynamic model output.

Suggested Citation

  • Mulenga Kalumba & Stefaan Dondeyne & Eline Vanuytrecht & Edwin Nyirenda & Jos Van Orshoven, 2022. "Functional Evaluation of Digital Soil Hydraulic Property Maps through Comparison of Simulated and Remotely Sensed Maize Canopy Cover," Land, MDPI, vol. 11(5), pages 1-15, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:618-:d:799408
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    References listed on IDEAS

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    1. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    2. Mulenga Kalumba & Edwin Nyirenda & Imasiku Nyambe & Stefaan Dondeyne & Jos Van Orshoven, 2022. "Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin," Land, MDPI, vol. 11(4), pages 1-22, April.
    3. Timlin, D. J. & Pachepsky, Ya. A. & Acock, B. & Whisler, F., 1996. "Indirect estimation of soil hydraulic properties to predict soybean yield using GLYCIM," Agricultural Systems, Elsevier, vol. 52(2-3), pages 331-353.
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