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Improving Large-Scale, Remote Soil Moisture Sensing

Soil moisture information is just as important to NASA engineers as it is to local farmers. This data is used to monitor climate patterns and predict landslides, and University of Michigan Michigan engineers are working on a system that will make collecting and analyzing it more accurate. Currently, most soil moisture data comes remotely from instruments attached to satellites. This allows for large-scale monitoring, but these systems need on-the-ground feedback for calibration. Electrical engineering professor Mingyan Liu and her team have developed special sensors and are planting them at test sites in Oklahoma and California. This hardware will help make large-scale remote soil moisture sensing more useful. It also offers new opportunities for smaller, local sensing applications.

Topics:
Data Acquisition Data Acquisition Measuring Instruments Monitoring RF & Microwave Electronics Sensors Test & Measurement

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    Transcript

    00:00:00 soil moisture is a very important data type it is an extremely critical data type to NASA for instance it is used in a variety of models that we use for instance to do a weather forecast climate monitoring it is also an important input to lenslight mudslide monitoring traditionally we use Radars and radiometers on board satellites the advantage of this methodology is it has

    00:00:28 very large coverage we just don't have enough ground truth data to validate uh how good that estimate is and this wasn't possible until we have sensors installed in ground to collect uh near ground truth data these sensors are installed at a distance about 10 ft sometimes no more than um 100 ft so you get much higher resolution in terms of the data samples then you would get from

    00:01:01 remote sensing we also get much higher temporal resolution because we can afford to sample continuously on order of once every few minutes the idea is to basically collate all these different data types by satellites collected by the U aircraft flyover and by uh ground sensor data basically put them all together for the validation calibration pures previously most of my work was

    00:01:28 theoretically based we didn't have to go out in the field and install these things make them work and so on when you actually have to build these things for Real uh a lot of things problem challenges uh arose uh that you wouldn't even think of earlier our first deployment was on a cattle farm uh you have to protect them from uh Cows as well as ground mice so a lot of time was

    00:01:54 spent on finding packaging Solutions we started the experiments at uh maai Botanical Garden just because they're they're close by and it's easy to maintain and then our first real fute work was in Canon uh Oklahoma we expanded to California right now we have uh about two three locations in Northern California So eventually we would like to build the uh California site to cover

    00:02:22 a 3X3 uh in 9 Square km area in the end outcome I think uh it's of great significance instant importance to the public for large scale monitoring we serve the purpose of uh calibrating the remote sensing data so we can make remote sensing more useful but for monitoring tasks that are more local smaller scale than what we're building is the exact technology that you would

    00:02:50 use pushing Computing Beyond laptops and mobile devices into our everyday surroundings 10 years from now what you might Envision are devices that that you can wear on your wrist or on your chest or maybe that get integrated