ISPA Account

2018 ICPA Photos

Suggestions For 2020 Conference Location
Have an idea for the location of the 2020 conference? Let us know by filling out this form.
Select the city to host the 15th ICPA Conference.
St. Louis, Missouri, USA (In conjunction with InfoAg 2020)
Minneapolis, Minnesota, USA
If OTHER selected, please enter the name of the city.
Enter the name and email of the contact for this city.
Enter your reason for suggesting this city for the 2020 conference.
Thank you for your time and interest. Your responses have been transmitted succesfully.


Access to the proceedings of the 14th ICPA is available online:
All the oral and poster presentation abstracts are available to the public. Access to the full papers and extended abstracts is limited to ISPA members and registered attendees of the 14th ICPA. Full papers and extended abstracts are also available for a fee to the public.
ISPA members need to login to the website using the panel on the left side of the home page. Use your email address associated with your membership.
If you attended the 14th ICPA Conference and do not know your password, or have not yet set up an account, you can click here.

Proceeding Spotlight

14th International Conference on Precision Agriculture Presentation

Estimating Cotton Water Requirements Using Sentinel-2

Crop coefficient (Kc)-based estimation of crop water consumption is one of the most commonly used methods for irrigation management.  Spectral modeling of Kc is possible due to the high correlations between Kc and the crop phenologic development and spectral reflectance.  In this study, cotton evapotranspiration was measured in the field using several methods, including eddy covariance, surface renewal, and heat pulse.  Kc was estimated as the ratio between reference evapotranspiration and the measured cotton evapotranspiration.  In addition, a time series of Sentinel-2 imagery was processed to produce 22 vegetation indices (VIs) based on the sensor’s unique spectral bands.  Empirical Kc – VI models were derived and ranked according to their prediction error.  In accordance with previous studies, we found a strong correlation between the normalized difference vegetation index (NDVI) and Kc (R2 = 0.94), and yet, we also identified other spectral indices that are more strongly correlated to Kc.  The indices that were found to be the most suitable for Kc prediction were based on the red and red-edge bands (MTCI, REP, and S2REP).  This progress in estimating cotton water consumption using satellite imagery that are available at no cost is a leap forward towards the development of crop irrigation requirements models.  Consequently, this work sets the scene for near-real-time irrigation decision support systems.