Assessment and mapping of soil water repellency using remote sensing and prediction of its effect on surface runoff and phosphorus losses : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Abstract

The soil water repellency spatial and temporal dynamics remain ambiguous. Water repellency is an inherent soil property that refers to the impedance in dry soil wetting. This phenomenon was ascribable to the hydrophobic compounds coating the soil particles and has emerged as a recalcitrant issue impacting multiple processes upon agroecosystems. The apprehensions around soil water repellency include its impact on surface runoff, plant growth, and nutrients losses (e.g. phosphorus). The soil hydrophobic compounds, which are intrinsic constituents of the soil carbon pool, have different sources including plant leaves and roots, soil microbial communities and fungi. Previous methods for water repellency measurements are laborious, time-consuming and costly. The raison d'être of this thesis was to i) explore and test novel approaches for estimation of soil water repellency in pastoral ecosystems, and ii) study the factors controlling soil water repellency and assess its impact on surface runoff volumes and phosphorus losses in surface runoff. In the present work, multiple remote sensing approaches were tested to assess and map soil water repellency at multiple scales. The liaison between water repellency and soil surface reflectance was exploited to access the water repellency using the satellite multispectral reflectance and hyperspectral satellite data. A novel approach implicating the use of time series of surface reflectance and water deficit data was used to study the impact of both surface biomass and soil moisture temporal dynamics on the occurrence of water repellency and carbon content in pastoral systems. Multispectral broadband data from both Landsat-7 and Sentinel-2 satellites showed big potential for assessing soil water repellency and carbon content in permanent pastures. Partial least square regression models were calibrated and cross-validated using topsoil measurement of water repellency and soil carbon from 41 and 35 pastoral sites that were matched with reflectance spectra from Landsat-7 and Sentinel-2, respectively. Soil carbon showed higher predictability compared to water repellency with R2v=0.50, RMSEv=2.58 when using Landsat-7 spectra. The higher predictability performance for water repellency persistence was reached using Sentinel-2 spectral (R2v=0.45; RMSEv=0.98). However, using hyperspectral narrowband data from the Hyperion satellite showed a higher prediction accuracy (R2v=0.78; RMSEv=0.58). Prediction performance was generally higher when using the calibration sets, indicating the possibility of improving these prediction models when using larger datasets. A novel approach was tested using multiple predictors for soil water repellency occurrence. The predictors included time series of surface biomass assessed through normalised difference vegetation index (NDVI) and soil moisture data estimated through water deficit and synthetic aperture radar satellite data. The results showed an attractive opportunity for water repellency and soil carbon mapping. Three machine learning algorithms including artificial neural networks, random forest, and support vector machine were trained and cross-validated using multiple configurations of satellite time-series data and topsoil measurement from 58 pastoral sites. Random forest and support vector machine (RMSEv=0.82 and 0.87, respectively) outperformed artificial neural networks (RMSEv=1.23). With increasingly available remote sensing data, the use of satellite time-series data will open unprecedented opportunities for soil carbon, water repellency mapping, and potentially other functional chemical and physical soil attributes. To understand water repellency dynamics and evaluate their impact on surface runoff and phosphorus losses in pastoral soils, two experiments were conducted. The first experiment aimed to understand the relationship between the actual water repellency persistence and water content in drying hydrophobic soils. The second experiment had the objective to evaluate the impact of soil water repellency on the surface runoff and phosphorus losses in runoff. Results from the first experiment showed that the actual water repellency increased dramatically when water content decreased, especially when moisture dropped below a critical value. Using lab measurements, the actual water repellency was modelled using a simple sigmoidal model, as a function of water content, the potential water repellency, and two characteristic parameters related to the response curve shape. Results from the runoff trial showed that the surface runoff was influenced by soil water repellency to some extent (R2=0.46). Although more than 90 % of phosphorus losses happened in incidental losses following fertiliser application, the data point to non-incidental phosphorus loads being related to soil water repellency (R2=0.56). These results bespoke the effect of soil water repellency on background phosphorus losses through surface runoff during post-summer runoff events in pastoral ecosystems.