Microwave Imaging That Predicts Yields

By Basist, Alan; Hult, Robin et al. | Futures (Cedar Falls, IA), September 2006 | Go to article overview

Microwave Imaging That Predicts Yields


Basist, Alan, Hult, Robin, Shen, Samuel, Thomas, Neil, Basist, Marc, Futures (Cedar Falls, IA)


New applications of satellite data can better predict growing conditions worldwide. This can be used to forecast crop production that leads the widely followed government reports.

Imagine if you could forecast crop conditions better and faster than the U.S. Department of Agriculture (USDA). Well, it's possible using technology known as special sensing microwave imaging (SSMI).

This technology can objectively calculate changes in growing conditions and yields for major crops at the county-equivalent level throughout the world. This permits independent and objective assessment of yield where limited data previously existed.

Crop models that exploit this data use the statistical relationships between temperature and wetness variations and yield figures at the county level. Running on near real-time SSMI data, the output is highly correlated with yield values supplied by the National Agricultural Statistical Service (NASS), which are followed world-wide as the definitive source of crop data. Moreover, the SSMI derived yield index provides an excellent technique to objectively assess yields without extensive, expensive and subjective field surveys.

The benefit for the trader is clear: faster, accurate, more affordable crop assessments result in better models. These models result in satellite derived accurate forecasts, and ostensibly, more profitable trades.

THE TECHNOLOGY

This technique uses the microwave spectrum to identify changes in surface wetness and temperature. It then incorporates these changes, measured as anomalies, into crop models, which explain variations in yields for soybean, corn, wheat and cotton in the United States. Alternative methods, such as traditional field surveys, are based on few and frequently unrepresentative spot observations and these findings tend to be subjective in nature.

SSMI technology was initially developed to monitor surface temperature and wetness from microwave energy naturally emitted from the land surface. The SSMI can observe, monitor and measure the land surface under almost all sky conditions. Thus, SSMI provides better risk coverage than optical-based satellite methods because clouds can cover much of the earth's surface at any time.

The temperature measurement tool was calibrated on an extensive network of surface stations. The wetness measurement tool is a composite of any source of moisture near the surface. These developed models have been combined and integrated as two inputs to create yield indexes for corn, soybeans, wheat and cotton.

The data come from a satellite platform flown by the Defense Meteorological Satellite Program (DMSP) that orbits the globe 14 times a day, and has been doing so since 1987. The DMSP satellites have sunsynchronized overpasses at 6 a.m. and 6 p.m. These satellite overpasses occur twice daily and are processed into 1/3 χ 1/3 degree "pixels" by the National Environmental Satellite and Data Information and Satellite (NESDIS). These data are archived at NOAA's Satellite Active Archive (SAA) in near real time.

The data received from these satellite observations are processed into three classes of values: the actual, climatology and anomaly. Both the temperature, measured in Celsius, and wetness measurements are available as morning and afternoon observations.

Anomalies are departures from the expected value for that location and time of year. The surface wetness index is derived as the percentage of the radiating surface that is in any form of moisture (liquid water). Anomalies for the wetness product are defined by a cumulative probability function, where low values are extremely dry and high values are extremely wet for that location and time of year.

Using techniques that measure the true spatial structure of the temperature is elusive in most areas of the world because isolated point measurements are smeared across the region, hiding the true spatial structure and gradients. …

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