Ogallala Water

Dr. Chávez's applied research includes using different multispectral remote sensing scales (i.e., satellite-, airborne-, UAV -, and ground-based), micro-met data, soil water data, and algorithms to monitor and determine vegetation water consumption and stress. Some of the algorithms include the land surface energy balance (different models), the dual crop coefficient, and the crop water stress index. Other research efforts focus on discriminating the effects on plants from lack of adequate water in the soil root profile from salinity concentrations. Regarding the Ogallala CAP project, efforts from our part are devoted towards identifying a suitable UAV-based remote sensing of

For the Ogallala Water CAP project, Dr. Chávez's research team's efforts are devoted towards identifying a suitable UAV-based remote sensing of crop evapotranspiration (ETc) model that can effectively quantify the vegetation water use and stress level. With a suitable, very high spatial/temporal resolution ETc model, it will be possible to spatially improve irrigation management by optimizing irrigation scheduling (per irrigation zone, precision irrigation) and set irrigation strategies; thus, improving crop water efficiency while aiding in prolonging the life expectancy of the Ogallala aquifer.

Opportunities:

Using a multispectral Unmanned Aerial System (UAS) to improve irrigation water management (precision irrigation). What success for this project looks like by 2020:Being able to demonstrate the usefulness (contribution) of a multispectral UAS in improving irrigation water management. Identifying guidelines regarding how to use

What success for this project looks like by 2020:

• Being able to demonstrate the usefulness (contribution) of a multispectral UAS in improving irrigation water management.
• Identifying guidelines regarding how to use an UAS and process data collected in an algorithm suitable for improved irrigation water management.
• Audiences: irrigation districts, state engineer office, water conservation districts, farmers, consultants, students/faculty, federal agencies.

Links

José L. Chávez
Personal

Selected Publications

Subedi* A., J.L. Chávez, and A. Andales. ASCE-EWRI Standardized Penman-Monteith Evapotranspiration (ET) Equation Performance in Southeastern Colorado. Submitted on 10 Feb 2016. Accepted on June 30, 2016. Available on-line 24 Aug 2016. Agricultural Water Management, 179, 74-80.

Kullberg*, E.G., DeJonge, K.C., and J.L. Chávez. Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Submitted on 15 Feb 2016. Accepted on July 3, 2016. Agricultural Water Management, 179, 64-73.

DeJonge K., Mefford*, B.S., and J.L. Chávez. 2016. Assessing corn water stress using spectral reflectance, International Journal of Remote Sensing. Submitted on 02 Feb 2015, re-submitted on 03 Feb 2015, re-submitted on 11 Feb 2016. Accepted on 15 March 2016. Volume 37, Issue 10, 2016, pages 2294-2312.

Mcebisi Mkhwanazi*, José L. Chávez , and Allan A. Andales, 2015, SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part I: Development and validation, Remote Sensing, Submitted 17 May 2015, Revised 1 Oct., Accepted on 3 Nov. Published 10 Nov. 2015. Remote Sens. 2015, 7(11), 15046-15067.

bhinaya Subedi*, and José L. Chávez, 2015, Crop evapotranspiration (ET) estimation models: A review and discussion of the applicability and limitations of ET methods, Journal of Agricultural Science, V7, No 6, 2015,Submitted on 23 Feb 2015, Accepted 10 Apr 2015, On-line publication 15 May 2015.

Rambikur*, E., and J.L. Chávez, 2014, Assessing Inter-Sensor Variability and Sensible Heat Flux Derivation Accuracy for a Large Aperture Scintillometer,Sensors, 14(2), 2150-2170.

Taghvaeian*, S., J.L. Chávez, W.C. Bausch, K.C. DeJonge, and T.J. Trout, 2014, Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize,Irrigation Science, 32, 53-65.

Taghvaeian*, S., José Chávez, Mary Hattendorf, Mark Crookston, 2013, Optical and Thermal Remote Sensing of Turfgrass Quality, Water Stress, and Water Use under Different Soil and Irrigation Treatments,Remote Sensing, 5, 2327-2347.

Chávez, J.L., Gowda, P.H., Howell, T.A., Garcia, L.A., Copeland, K.S., and Neale, C.M.U., 2012, ET mapping with high resolution airborne remote sensing data in an advective semi-arid environment,Journal of Irrigation and Drainage Engineering, 138(5), 416-423.

Elhaddad, A., L.A. Garcia, and J.L. Chávez, 2011, Using a Surface Energy Balance Model to Calculate Spatially Distributed Actual ET, Irrigation and Drainage Engineering, 137(1), 17-26.