Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

• Published in: Applied Sciences
• Main Authors: Anita Masenyama, Onisimo Mutanga, Timothy Dube, Mbulisi Sibanda, Omosalewa Odebiri, Tafadzwanashe Mabhaudhi
• Format: Article
• Language: English
• Published: MDPI AG 2023-02-01
• Subjects: grass water content; Google Earth Engine; optical remote sensing; shuttle radar topography mission; topo-climatic variables
• Online Access: https://www.mdpi.com/2076-3417/13/5/3117

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m⁻² and R² of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m⁻² and R² of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R² = 0.86) when compared to the RMSE of 0.03 mm and R² of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m⁻² and R² of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m⁻² and R² = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m⁻² and R² = 0.91 as compared to the wet season (RMSE = 10.75 g/m⁻² and R² = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.