Time series of remote sensing and water deficit to predict the occurrence of soil water repellency in New Zealand pastures

Abstract

Soil water repellency (SWR) is a natural phenomenon occurring in soils throughout the world, which impacts upon ecosystem services at multiple temporal and spatial scales (nano to ecosystem scale). In pastures, the development of SWR is primarily determined by the cycling of hydrophobic materials at the soil surface, and is controlled by climate, soil and water management, and soil properties. The complex interactions between these factors make it an intricate system to understand and model. Detailed spatiotemporal characterization of the surface moisture and biomass in pastoral ecosystems would allow for a better understanding of this phenomenon. Normalized Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) backscatter are good predictors for surface biomass and soil moisture, respectively. Machine learning on remote sensing time series (TS) data shows promise to predict the occurrence of SWR in pastures. This study evaluates the ability of remote sensing TS data to predict the occurrence of SWR in New Zealand pastures, using three machine learning algorithms. Soil water repellency data were collected from 58 pastoral sites. Machine learning models were trained and cross-validated on a monthly aggregated remote sensing and water deficit TS data to predict SWR level. Prediction output from artificial neural networks (ANN), random forest (RF), and support vector machine (SVM) were compared using root mean squared error (RMSE). When using NDVI TS data from 58 site as predictors of SWR, SVM and RF (RMSE = 0.82 and 0.87, respectively) outperformed ANN (RMSE = 1.23). Random forest was used to map SWR magnitude over Hawke's Bay region in the North Island of New Zealand, and the overall accuracy was equal to 86%. This study is the first investigation implicating remote sensing TS data to predict the occurrence of SWR at the regional scale. Mapping the potential SWR will aid in identifying critical zones of SWR, to attenuate its effect on pastures through adapted management.