HUANG, Jingyi
Dr Jingyi Huang
Post-doctoral Fellow
Contact details:
+61 2 9385 3645

Room 517, D26 Building
UNSW, Kensington 2052

Digital soil mapping using gamma-ray spectrometry and electromagnetic induction at the field level

In order to increase the efficiency of data generation at the field level, digital soil mapping (DSM) methods are being developed. In this thesis, proximally sensed gamma-ray (g-ray) spectrometry and electromagnetic induction (EMI) data were combined to map the soil management classes using a fuzzy k-means (FKM) clustering and estimate spatial distribution of soil properties using multiple linear regression (MLR) with Restrict Maximum likelihood (REML) at an arable field and a pasture field at Shelford, Nottingham, UK. In terms of FKM analysis, fuzziness performance index (FPI) and normalized classification entropy (NCE) suggested a suitable fuzzy exponent (f) of 2.0. When class (k) = 3, three main landscape units were broadly identifiable (i.e. Trent alluvium, Trent River terraces, and Triassic Mercia mudstone). Mean squared prediction error (s2p,C) of the classes indicated the ideal k = 7 or 8, whereby, interestingly, the distribution of soil classes were consistent with a soil series map generated using traditional soil profile and a geological model produced by ground-penetrating radar, electrical resistivity tomography and automated resistivity profiling. Digital soil maps generated by MLR showed the spatial distribution of topsoil properties (e.g. clay content, silt content, bulk density and pH). Combination of FKM clustering and MLR with REML provides an example of mapping soil properties and management zones, which has a profound effect on land use and how the soil could be managed to improve agricultural productivity.


Figure1 Digital soil maps for class k = 6, 7 and 8 using FKM clustering.


Figure2 Predicted soil texture, pH and bulk density using multiple linear regression.



17. Huang, J., Barrett-Lennard, E.G., Kilminster, T., Sinnott, A., Triantafilis, J., 2015. An error budget for mapping field-scale soil salinity at various depths using different sources of ancillary data. Soil Science Society of America Journal, in press.
16. Guo, Y., Shi, Z., Huang, J., Zhou, L., Zhou, Y., Wang, L., 2015. Characterization of field scale soil variability using remotely and proximally sensed data and response surface method. Stochastic Environmental Research and Risk Assessment, 1-11.
15. Guo, Y., Huang, J., Shi, Z., Li, H., 2015. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors. PloS one, 10: e0127996.
14. Huang, J., Taghizadeh-Mehrjardi, R., Minasny, B., Triantafilis, J., 2015. Modelling soil salinity along a hill slope in Iran by inversion of EM38 data. Soil Science Society of America Journal, 79: 1142-1153.
13. Huang, J., Shi, Z., Biswas, A., 2015. Characterizing anisotropic scale-specific variations in soil salinity from a reclaimed marshland in China. Catena, 131: 64-73.
12. Huang, J., Zare E., Malik R.S., Triantafilis, J., 2015. An error budget for soil salinity mapping using different ancillary data. Soil Research, 53: 561-575.
11. Huang, J., Subasinghe, R., Malik, R.S., Triantafilis, J., 2015. Salinity hazard and risk mapping of point source salinisation using proximally sensed electromagnetic instruments. Computers and Electronics in Agriculture, 113: 213-224.
10. Huang, J., Mohktari, A.R., Cohen D.R., Monteiro Santos, F.A. Triantafilis, J., 2015. Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data. European Journal of Soil Science, in press.
9. Davies, G.B., Huang, J., Monteiro Santos, F.A., Triantafilis, J., 2015. Modelling coastal salinity using a DUALEM-421 and inversion software. Ground Water, 53: 424-431.
8. Huang, J., Subasinghe, R., Triantafilis, J., 2014. Mapping Particle-Size Fractions as a Composition Using Additive Log-Ratio Transformation and Ancillary Data. Soil Science Society of America Journal, 78: 1967-1976.
7. Ji, W., Shi, Z., Huang, J., Li, S., 2014. In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE, 9: e105708.
6. Goff, A., Huang, J., Wong, V., Monteiro Santos, F.A., Triantafilis, J., 2014. Electromagnetic conductivity imaging of soil salinity in an estuarine-alluvial landscape. Soil Science Society of America Journal, 78: 1686-1693.
5. Huang, J., Nhan, T., Wong, V., Johnston, S., Lark, R.M., Triantafilis, J., 2014. Digital soil mapping of a coastal acid sulfate soil landscape. Soil Research, 52: 327-339.
4. Huang, J., Triantafilis, J., Lark, R.M., Robinson, D.A., Lebron, I., Keith, A.M., Rawlins, B., Tye, A., Kuras, O., Raines, M., 2014. Scope to predict soil properties at within-field scale from small samples using proximal sensed -ray spectrometer and EM induction data, Geoderma, 232-234: 69-80.
3. Huang, J., Wong, V., Triantafilis, J., 2014. Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data. Soil Use and Management, 30: 394-402.
2. Huang, J., Bowd, D.,  Davies, G.B., Monteiro Santos, F.A., Triantafilis, J., 2014. Spatial prediction of exchangeable sodium percentage at multiple depths using electromagnetic inversion modeling. Soil Use and Management, 30: 241-250.
1. Gooley, L., Huang, J., Page, D., Triantafilis, J., 2014. Digital soil mapping of available water content using proximal and remotely sensed data, Soil Use and Management, 30: 139-151.