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

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

Enabling Precision Fertilisers Application Using Digital Soil Mapping in Australian Sugarcane Areas

Sugar is Australia's second largest export crop after wheat, generating a total annual revenue of almost $2 billion. It is produced from sugarcane, with approximately 95% grown in Queensland. While highly productive and contributing to the area’s economic sustainability, the soils in these areas have low fertility. The soils typically contain sand content > 60%, low organic carbon (SOC 6%). Hence, sugarcane farmers need to apply fertilisers and ameliorants to maintain soil quality and productivity. Unfortunately, the high intensity rainfall in the region results in sediments, nutrients, and ameliorants run-off from these farms, resulting in environmental degradation and threats to marine ecology in the adjacent World Heritage Listed Great Barrier Reef. To mitigate these issues, the Australian sugarcane industry introduced the Six-Easy-Step Nutrient Management Guidelines. To apply these guidelines, a labour-intensive high-density soil sampling is typically required at the field level, followed by expensive laboratory analysis, spanning the myriad of biological, physical, and chemical properties of soils that need to be determined. To assist in sampling site selection, remote (e.g., Landsat-8, Sentinel-2, and DEM-based terrain attributes) and/or proximal sensing (e.g., electromagnetic [EM] induction and gamma-ray [γ-ray] spectrometry) digital data are increasingly being used. Moreover, the soil and digital data can be modelled using geostatistical (e.g., ordinary kriging [OK]), linear (e.g., linear mixed model [LMM]), machine learning (e.g., random forest [RF], quantile regression forest [QRF], support vector machine [SVM], and Cubist) and hybrid (e.g., RFRK, SVMRK, and CubistRK) approaches to enable prediction of soil properties from the rich source of digital data. However, there are many questions that need to be answered to determine appropriate recommendations including but not limited to i) which modelling approach is optimal, ii) which source of digital data is optimal and does fusion of various sources of digital data improve prediction accuracy, iii) which methods can be used to combine these digital data, iv) what is a minimum number of samples to establish a suitable calibration, v) which soil sampling designs could be used, and vi) what approaches are available to enable prediction of soil properties at various depths simultaneously? In this thesis, Chapter 1 introduces the research questions and defines the problems facing the Australian Sugarcane Industry in terms of the applications of the Six-Easy-Steps Nutrient Management Guidelines, research aims and thesis structure. Chapter 2 is a systematic literature review on various facets of DSM, which includes digital and soil data, models and outputs, and their application across various spatial scales and properties. In Chapter 3, prediction of topsoil (0-0.3 m) SOC is examined in the context of comparing predictive models (i.e., geostatistical, linear, machine learning [ML], and hybrid) using various digital data (i.e., remote [Landsat-8] and proximal sensors [EM and γ-ray]) either individually or in combination and determining minimum number of calibration samples. Chapter 4 shows to predict top- (0-0.3 m) and subsoil (0.6-0.9 m) Ca and Mg, various sampling designs (simple random [SRS], spatial coverage [SCS], feature space coverage [FSCS], and conditioned Latin hypercube sampling [cLHS]) were assessed, with different modelling approaches (i.e., OK, LMM, QRF, SVM, and CubistRK) and calibration sample size effect evaluated, using a combination of proximal data (EM and γ-ray) and terrain (e.g., elevation, slope, and aspect, etc.) attributes. Chapter 5 shows to enable the three-dimensional mapping of CEC and pH at topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow- (0.6-0.9 m) and deep-subsoil (0.9-1.2 m), an equal-area spline depth function can be used, with remote (Sentinel-2) and proximal data (EM and γ-ray) used alone or fused together, and various fusion methods (i.e., concatenation, simple averaging [SA], Bates-Granger averaging [BGA], Granger-Ramanathan averaging [GRA], and bias-corrected eigenvector averaging [BC-EA]) investigated. Chapter 6 explored the synergistic use of proximal (EM and γ-ray), and time-series of remote data (Landsat-8 and Sentinel-2) to map top- (0-0.15 m) and subsoil (0.30-0.45 m) ESP. The results show that, across these case studies, hybrid and ML models generally achieved higher prediction accuracy. The fusion of remote and proximal data produced better predictions, compared to single source of sensors. Granger-Ramanathan averaging (GRA) and concatenation were the most effective methods to combine digital data. A minimum of less than 1 sample ha-1 would be required to calibrate a good predictive model. There were differences in prediction accuracy amongst the sampling designs. The application of depth function splines enables the simultaneous mapping of soil properties from various depths. The produced DSM of soil properties can be used to inform farmers of spatial variability of soils and enable them to precisely apply fertilisers and/or ameliorants based on the Six-Easy-Step Nutrient Management Guidelines

Digital soil mapping using proximal and remote sensed geophysical data at field, farm and district levels in Thailand

The Northeast of Thailand is located in a tropical climate and dominated by sandy soil types. Owing to the high rainfall (1,162 mm/annum) and low clay content, the soil has low inherent fertility (e.g. pH < 5.5) and soil re-activity (i.e. CEC < 10 cmol(+)kg-1). In order to improve productivity of the main agricultural land uses (e.g. rice), addition of amendments and fertilisers are required to increase pH and exchangeable cations, respectively. Moreover, irrigation and associated infrastructure (e.g. cannals) have been introduced to supplement the variable rainfall. However, the canals are leaking through the low reactive soil, causing recharge water to interact with an underlying sequence of rock salts. The result has been extensive secondary salinization.To better understand the nature and extent of the sandier textured soil, as well as how to manage their infertility and improve water retention in the canals, detailed soil information is required. However, the traditional survey techniques and laboratory analysis are cost and time consuming. In order to develop information to enable soil improvement in terms of adding lime to increase pH and identify leakage areas, more technologically advanced method of digital soil mapping can be considered. Specifically, use easier to measure and acquire digital data to couple this to a limited number of measured soil chemical and physical properties via the use of predictive spatially mathematical models. This thesis therefore focuses on developing digital soil maps (DSM) to create base line information of soil properties including; clay content (%), cation exchange capacity (CEC), and soil salinity.In Chapter 1 the natural resources and land uses of northeast Thailand are described with the need to improve soil condition described. Chapter 2 introduced previous literature on the theory and application of mathematical models, to relate various soil physical and chemical properties to digital data including proximal and remote sensed. In Chapter 3, DSM of clay and CEC are developed at the field scale by inverting EM38 ECa and using a quasi-3D inversion algorithm. The approach is compared with a more commonly used approach of considering DSM of each layer independently. In Chapter 4, a similar approach is used to map soil salinity (ECe) in 3-D, but across multiple fields with a comparison of different EM instruments simulated. In Chapter 5, soil salinity is mapped adjacent to a canal to identity leakage by various ECa data (i.e. EM38, Dualem-421S and EM34) and using quasi-2D inversion modelling.The results confirmed DSM approach can be effectively used to map the spatial detailed soil properties even if in the field or farm scales. Practical guidelines for fulfilment mapping quality over those various morphological properties (e.g. sandy, salinity and acidity profiles) were informatively discussed in addressing and promising the future research. Specifically, for the combination use of available proximally and remotely sensed data with mathematical models including linear regression and machine learning algorithm. Overall, the accurately and economically in producing soil base line information are utmost required to this research