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Digital soil mapping of soil physical and chemical properties using proximal and remote sensed data in Australian cotton growing areas

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  • Title:
    Digital soil mapping of soil physical and chemical properties using proximal and remote sensed data in Australian cotton growing areas
  • Author: Zhao, Dongxue
  • Subjects: ENVIRONMENTAL SCIENCES
  • Description: In Australian cotton-growing areas, information of soil physical and chemical properties is required as they decide soil structure, nutrient availability and water holding capacity. However, using conventional laboratory methods to determine these properties is impractical as they are time-consuming and costly. This is especially the case when considering samples from different depths and across heterogenous fields and districts. Thus, there is a need for efficient and affordable methods to enable data generation. To answer this need, digital soil mapping (DSM) can be used, in which limited laboratory measured soil data is coupled with cheaper-to acquire digital data through models and then the model and spatially exhaustive digital data are used to predict soil properties on unsampled locations. This thesis evaluates DSM methods for the prediction of soil physical (e.g., clay content) and chemical (e.g., cation exchange capacity [CEC] and exchangeable [exch.] cations) properties at various depths across cotton growing areas in south-eastern Australia, at field and district scales. Chapter 1 is the general introduction where research problems are defined, and research objectives are introduced. To point out gaps in the application of DSM on the prediction of soil properties, Chapter 2 comprehensively reviews DSM concepts, the applicability of proximally (e.g., electromagnetic induction (EM), visible near-infrared spectroscopy (vis-NIR)) or remotely (e.g., γ-ray spectrometer) sensed digital data for prediction of soil properties at various depths and the modelling techniques. The first research chapter (Chapter 3) compares various strategies to build the vis-NIR spectral library for clay content prediction at two depths across seven cotton growing areas using Cubist model. The results show that the area-specific vis-NIR library achieve the best results. The improvement in model performance is possible using spiking. The Chapter 4 compares multivariate methods for estimating clay content and its uncertainty map at two depths and the effect of weighted model averaging is evaluated. The results show that random forest (RF) model generally performs the best and model averaging could further improve the prediction accuracy. The Chapter 5 evaluates the potential of vis-NIR as a tool for the simultaneous prediction of soil physical and chemical properties across cotton growing areas and considering two calibration models. The results show that satisfactory predictions of clay and CEC are achieved with silt and sand prediction moderate, while the prediction of pH and exchangeable sodium percentage (ESP) are unsatisfactory. A multi-depth vis-NIR library generally performs better than depth-specific libraries on prediction of soil properties. The Chapter 6 builds a topsoil (0 – 0.3 m) vis-NIR spectral library to predict topsoil exch. cations considering four different calibration models and explores the applicability of the topsoil library to predict exch. cations at deeper depths considering spiking or not. The results show that the vis-NIR could provide satisfactory prediction of exch. calcium and magnesium. Topsoil spectral library could be used to predict exch. cations at deeper depth with spiking further improving the result. The Chapter 7 estimates spatial variation of CEC at various depths using quasi-3d joint inversion of EM38 and EM31 data in an irrigated cotton field. The results indicate that the joint-inversion approach developed in this study could generate accurate 3D predictions of soil CEC in the cotton growing field. This thesis explores DSM methods for the prediction of soil physical and chemical properties in Australian cotton growing areas and the results deliver new evidence of the potential to use proximally and remotely sensed digital data and state-to-art models for rapid and efficient generation of soil information. New findings will serve to advance the existing knowledge on application of DSM at field and district scales. Source: TROVE
  • Creation Date: 2021
  • Language: English
  • Source: Trove Australian Thesis (Full Text Open Access)
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