Digital Soil Mapping Of Landscape Units And Salinity Across The Bourke Irrigation District Ehsan Zare A thesis submitted in fulfilment of the requirements for the degree of Master of Philosophy School of Biological, Earth and Environmental Sciences March 2017 Abstract In Bourke Irrigation district (BID), one of the most productive agricultural soil types is the Vertosol which has been extensively developed for irrigated agricultural production. However, there have been increasing instances of decline in productivity as a result of secondary soil salinization due to injudicious location of water storages and conveyance channels in irrigated farmlands. Therefore, it is important to map the soil in this area to understand the long-term sustainability issues and spatial variability in soil types. A great deal of soil data is required to characterise large areas such as BID. One approach is to use digital soil mapping (DSM) methods because they rely on the use of low-cost ancillary data to value add to limited soil information via the use of spatial and non-spatial numerical methods. In this thesis, two different DSM approaches are used in order to identify soil landscape units and a soil property (i.e. ECe dS/m). Firstly ancillary data, including remotely sensed air-borne gamma-ray (-ray) spectrometer (i.e. potassium-K, uranium-U, thorium-Th and total counts-TC) and proximal sensed EM38 in the horizontal (EM38h) and vertical (EM38v) mode of operation are used with a non-spatial numerical clustering algorithm (fuzzy k-means: FKM). The FKM analysis (using Mahalanobis metric) of the kriged ancillary (i.e. common 100 m grid) data revealed a fuzziness exponent () of 1.4 was suitable for further analysis and that k = 4 classes was smallest for the fuzziness performance index (FPI) and normalised classification entropy (NCE). Using laboratory measured physical (i.e. clay) and chemical (i.e. CEC, ECe and pH) 2 properties revealed k = 4 was minimized in terms of mean squared prediction error (i.e. p,C) when considering topsoil (0-0.3 m) and subsoil (0.9-1.2 m) Clay, CEC, ECe and pH (i.e. only for topsoil). Secondly, in order to map a soil property limiting agricultural productivity, ancillary data, including remote and proximal, were used with a spatial numerical model (linear mixed modelling-LMM and restricted likely - REML) to map the spatial distribution of the saturated soil paste –extract (ECe dS/m). DSM of ECe using the same sources of ancillary data and empirical best linear unbiased prediction (E-BLUP) showed elevation, radioelement of thorium (Th) and logEM38v were the most statistically useful ancillary data. It was also found that the development of an error budget procedure, enabled the quantification of the relative contributions that model, input, combined and covariate error made to the prediction error of the map of ECe. The combined error is approximately 4.44 dS/m, which is relatively large compared to the standard deviation of measured ECe (3.61 dS/m). Of this error, most of it is attributable to the input error (1.38 dS/m) which is larger than the model error (0.02 dS/m). In terms of the input error, it is determined that the larger standard deviation is attributable to the lack of ancillary data, namely the ECa in areas adjacent to the Darling River and also on the aeolian dune where data collection was difficult owing to dense native vegetation. ACKNOWLEDGEMENTS I would like to acknowledge my supervisor Dr. John Triantafilis for his fabulous guidance, persistence, patience and contribution to the ideas and the writing of this thesis and during the past two years and whilst I have been enrolled as an MPhil student within the School of Biological, Earth and Environmental Sciences (BEES) in the Faculty of Science, UNSW Australia. I would also like to greatly thank Mr. Jingyi Huang (Ph.D. student, UNSW) for his invaluable contributions in the statistical analysis of the results of the FKM analysis, LMM, and REML. I also thank him for introducing me to the R software package and for providing me with the necessary templates and ideas to analyse and interpret the results in terms of calculating the expected value of the mean squared prediction error from classes (i.e. 2 p,C) and regression (2 p,R). I am also indebted to Mr. Triven Koganti (MPhil student, UNSW) for his contribution to the ideas of the thesis structure and format. Finally, I would like to thank two examiners, Professor Cristine L. S. Morgan (Texas A&M Univesity, USA) and Professor Eric C. Brevik (Dickinson State University, USA) for their kind suggestions on the revisions of the thesis. Table of Contents 1 Introduction ................................................................................................ 7 1.1 References .................................................................................................................. 10 2 Literature Review ..................................................................................... 11 2.1 Introduction ................................................................................................................ 11 2.1.1 Ancillary data used in DSM .......................................................................................... 12 2.1.2 Remote sensing systems ................................................................................................ 14 2.1.3 Proximal sensing systems .............................................................................................. 15 2.2 Review of -ray spectrometry .................................................................................... 17 2.2.1 The -ray theory ............................................................................................................. 17 2.2.1.1 Isotopes .................................................................................................................... 18 2.2.1.2 Basic radioactivity ................................................................................................... 19 2.2.1.3 The -Radiometric .................................................................................................... 20 2.2.1.4 Airborne -ray spectrometry survey ......................................................................... 25 2.2.2 The -ray applications ................................................................................................... 26 2.2.2.1 Soil Texture .............................................................................................................. 27 2.2.2.2 Salinity (ECe) ........................................................................................................... 29 2.2.2.3 Parent materials and soil types ................................................................................. 29 2.2.2.4 Soil management zones and classes ......................................................................... 32 2.2.2.5 Other soil properties ................................................................................................. 33 2.3 Review of electromagnetic methods .......................................................................... 33 2.3.1 Theory of electromagnetic induction ............................................................................. 33 2.3.2 EM instruments ............................................................................................................. 35 2.3.2.1 Geonics Series .......................................................................................................... 35 2.3.2.2 EM 38 ...................................................................................................................... 37 2.3.2.3 DUALEM ................................................................................................................ 38 2.3.3 EM Applications ............................................................................................................ 40 2.3.3.1 Salinity (ECe) ........................................................................................................... 40 2.3.3.2 Clay content and soil texture .................................................................................... 44 2.3.3.3 Soil moisture ............................................................................................................ 47 2.3.3.4 Soil Acidity (pH)...................................................................................................... 49 2.3.3.5 Soil cation exchange capacity (CEC)....................................................................... 50 2.3.3.6 Soil management classes .......................................................................................... 52 2.3.3.7 Other properties ....................................................................................................... 55 2.4 Conclusions ................................................................................................................ 56 2.5 References .................................................................................................................. 57 3 Study area ................................................................................................. 80 3.1 Location ..................................................................................................................... 80 1 3.2 Climate ....................................................................................................................... 81 3.3 Land use ....................................................................................................................