Preventing waterlogging in irrigated agriculture with a multi-satellite sensor approach

Satellite sensors have been used widely to determine water shortages to detect crop stress, with special emphasis on water stress. However, stress resulting from waterlogging has so far received little attention. This is surprising because approximately twenty percent of the global agricultural land suffers from the consequences of waterlogging and secondary soil salinization. While irrigation is expected to increase productivity, excess water can hamper the crop growth and decrease water use efficiency.

Traditionally, satellite driven water accounting for irrigation assistance uses optical and/or thermal sensors that can detect crop stress. The observed crop stress is often interpreted as water stress, whereby stress resulting from waterlogging cannot be distinguished. We hypothesize that a multi-sensor approach is required to distinguish waterlogging from water shortage, by including microwave observations that can determine the soil moisture status. However, localizing a small-scale phenomena as waterlogging with multi-sensor data with different resolutions is a major challenge.

In our research we focus on an irrigated sugarcane plantation along the river Incomati in Xinavane, Mozambique. Waterlogging is a common issue in the estate and is threatening productivity. We assess and combine optical and passive microwave data for a large drought (2016) and flooding event (2012) to look at the possibility of downscaling the data for detection of waterlogging. We find that optical indices are able to localize waterlogged areas. Additionally, the built up of the drought event and retreat of the flooding event are clearly visible in the brightness temperature in different frequencies. We demonstrate a procedure to combine brightness temperature with optical data to detect waterlogging at a higher spatial resolution. 

The results show that a combination of optical and passive microwave data can detect regions within the sugarcane plantation of waterlogging. However, high resolution topographic data is required to enhance the detection of waterlogging to finer scales.