Water Monitoring on a Global Scale with a Local Impact


Monitoring supply and demand of water resources are becoming challenging.

Water has always been an integral part of our daily lives and in our modern age it has become so widely accessible that we’ve started to take its limited nature for granted. With the ever-increasing global demand for water and changing weather patterns, our supply of fresh and clean water has started to come under serious pressure.

Over the past 12 months the citizens of Cape Town have come face to face with the reality of climate change. Cape Town, one of Africa’s most progressive cities that houses more than 3.7 million people, was on the verge of running out of water. Reservoir levels had dropped to such worryingly low levels that ‘Day Zero’ was announced, the day that these reservoirs would completely run dry of fresh and clean drinking water. The six major reservoirs feeding Cape Town can collectively store 898,000 mega litres of water but were rapidly dwindling down to 13.5% of its capacity, the level where the taps will run dry.




[Caption] Satellite images of the Theewaterskloof dam as captured by Sentinel-2 from October 2017 till July 2018.

Today we have an arsenal of tools available to monitor the global availability of water and determine the impact on a local scale. By monitoring the usage of water via daily satellite imagery, we are able to predict the demand and supply for water more accurately than ever before.

Geospatial analysts are currently building models using artificial intelligence and machine learning techniques together with historical satellite imagery and public available water occurrence data sets as the gold standard. It seems obvious and easy to use satellite imagery, but it’s not. In fact, predicting demand and supply where nature and the human population meet are extremely complex. To optimize the accuracy of these models, we require remote sensing data in the right spectral bands and optimal spatial resolution. Just capturing a pretty picture isn’t enough.

Very specific spectral bands in the blue, green, red and near-infrared spectrums are required. These bands must be optimized to determine vegetation and different water indexes. In remote sensing, the choice of bands determines its specific purpose. At Simera Sense, our sensors are designed for cross-sensor consistency with spectral bands that are selectable and programmable for your specific application. Our multi-spectral 7 band sensor allows you to select bands equivalent to Landsat or Sentinel-2, while the hyperspectral sensor allows you to choose from 154 bands, ideal to monitor complex ecosystems.

High-resolution images aid in the monitoring of urban growth and the catchment terrain to accurately predict demand and supply. Going down to sub 5m Ground Sampling Distance (GSD) will increase the detection and prediction accuracy of your models. Simera Sense has optimized its 3U imager payload to capture sharp and crisp sub 5m GSD images from a 500km orbit.

The frequency of capturing images is also a major driving force behind the quality of your prediction models. Additionally, clouds will also obscure the view of the ground, making the collection of data on regular intervals important to capture rapid changes, however small it may be. Simera Sense’s CubeSat earth observation payloads allow geospatial solutions providers to swiftly and reliably build and scale a remote sensing mission. With a swarm of these satellites it is now possible to obtain daily images of the earth at an affordable price.

Using satellite imagery, we are able to monitor how the water supply of Cape Town’s main reservoir, the Theewaterskloof dam, receded from October 2017 until now. Quality imagery might be just the first step in the modelling of water resources, but is undeniably one of the most important steps.

Are you curious to learn more about how we can assist in developing your resource monitoring applications? Keep an eye on our website.

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