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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #362165
Title: UAS-Based Variable Rate Irrigation- Is it possible?

Author
item CHAVEZ, JOSE - COLORADO STATE UNIVERSITY
item Zhang, Huihui
item RUDNICK, DARAN - UNIVERSITY OF NEBRASKA
item SCHNEEKLOTH, JOEL - COLORADO STATE UNIVERSITY

Submitted to: Colorado Water Magazine
Publication Type: Trade Journal
Publication Acceptance Date: 12/1/2018
Publication Date: 12/1/2018
Citation: Chavez, J.L., Zhang, H., Rudnick, D., Schneekloth, J. 2018. UAS-Based Variable Rate Irrigation- Is it possible?. Colorado Water Magazine. 35(6):24-27.

Interpretive Summary: N/A

Technical Abstract: Remote sensing (RS) techniques have been used to identify crops grown during different seasons and to estimate crop biophysical characteristics and water use. Images from satellites such as Landsat 5, 7, and 8 have been used extensively to map crop evapotranspiration rates (ET) using a suite of algorithms. However, Landsat satellites have a fixed revisit frequency (e.g., 16 days) and pixel spatial resolution of 30 m for the visible (VIS) and mid-infra-red (MIR ) bands while the thermal infra-red (TIR) band pixel size is 100-120 m. Furthermore, some RS of ET algorithms require that the TIR band be corrected for atmospheric effects. These characteristics limit the application of satellites to generate frequent and higher spatial resolution ET maps which are needed in soil-water balance methods to help manage irrigation effectively over heterogeneous fields. These fields’ irrigation hardware, if irrigated with a center pivot or a linear move, could potentially be equipped with a Variable Rate Irrigation (VRI) system capable of applying variable irrigation amounts per location in the field. VRI demands higher spatial and temporal resolution ETa maps to generate irrigation application prescription maps. In this context, Unmanned Aerial Systems (UASs) are amenable to VRI imagery and map demands. In the article, we presented an approach to combine soil water content sensors and UAS-based imagery to produce more accuracy VRI prescription maps.