TITLE:
Evaluating Reflected GPS Signal as a Potential Tool for Cotton Irrigation Scheduling
AUTHORS:
Xin Qiao, Ahmad Khalilian, Jose O. Payero, Joe Mari Maja, Charles V. Privette, Young J. Han
KEYWORDS:
Remote Sensing, GPS, Soil Volumetric Water Content, Cotton, Irrigation
JOURNAL NAME:
Advances in Remote Sensing,
Vol.5 No.3,
July
20,
2016
ABSTRACT: Accurate soil moisture content measurements are vital to precision irrigation management. Remote
sensing using the microwave spectrum (such as GPS signals) has been used for measuring
large area soil moisture contents. In our previous work, we estimated surface soil moisture contents
for bare soil using a GPS Delay Mapping Receiver (DMR) developed by NASA. However, the
effect of vegetation was not considered in these studies. Hence the objectives of this study were to:
1) investigate the feasibility of using DMR to determine soil moisture content in cotton production
fields; 2) evaluate the attenuation effect of vegetation (cotton) on reflected GPS signal. Field experiments
were conducted during the 2013 and 2014 growing seasons in South Carolina. GPS antennas
were mounted at three heights (1.6, 2.7, and 4.2 m) over cotton fields to measure reflected
GPS signals (DMR readings). DMR readings, soil core samples, and plant measurements were collected
about once a week and attenuation effect of plant canopy was calculated. Results showed
that DMR was able to detect soil moisture changes within one week after precipitation events that
were larger than 25 mm per day. However, the DMR readings were poorly correlated with soil volumetric
water content during dry periods. Attenuation effect of plant canopy was not significant.
For irrigation purpose, the results suggested that the sensitivity of reflected GPS signals to soil
moisture changes needed to be further studied before this technology could be utilized for irrigation
scheduling in cotton production. Refinement of this technology will expand the use of advanced
remote sensing technology for site-specific and timely irrigation scheduling. This would
eliminate the need to install moisture sensors in production fields, which can interfere with farming
operations and increase production costs.