Alternative wetting and drying (AWD) is an increasingly popular water-saving practice in rice production in the Vietnamese Mekong River Delta, especially considering the impact of projected climate change and reduced water availability. Unfortunately, it is very difficult to determine adoption without deploying thousands of costly household surveys. This research used European Space Agency Sentinel-1a and 1b radar data, combined with in-situ moisture readings, to determine AWD adoption through change detection of a time series wetness index (WI). By using a beta coefficient of the radar data, the WI avoided the pitfalls of cloud cover, surface roughness, and vegetative interference that arise from the sigma coefficient data. The analysis illustrated an AWD adoption likelihood scale across the delta and it showed potential for the use of remotely sensed data to detect adoption. Trends across the Vietnamese delta showed higher adoption rates inland, with lower adoption of AWD in the coastal provinces. These results were supported by a simultaneous effort to collect household level adoption data as part of the same project. However, correlation between the WI values and in situ soil moisture meter readings were most accurate in alluvial soils, illustrating a particularly strong relationship between soil type and WI model robustness. The research suggests that future change detection efforts should focus on retrieving a multi-season dataset and employing a power density analysis on the time series data to fully understand the periodicity of dry down patterns.
Identifying Alternative Wetting and Drying Adoption (AWD) in the Vietnamese Mekong River Delta: A Change Detection Approach
