A new approach for estimating and delineating within-field crop water stress zones with satellite imagery. Issue 16 (18th August 2021)

• Record Type: Journal Article
• Authors: Smith, Ryan; Oyler, Lindi; Campbell, Colin; Woolley, Elisa A.; Hopkins, Bryan G.; Kerry, Ruth; Hansen, Neil C.
• Is Part Of: International journal of remote sensing. Volume 42:Issue 16(2021)
• Page Start: 6003
• Page End: 6022
• Publication Date: 2021-08-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1931536
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: As water scarcity and demand for food both grow, viable solutions to efficiently irrigate crops are needed. The recent advent of variable rate irrigation systems provides technology for growers to water their fields accounting for the spatially heterogeneous demand of their crops for water. Some zones within a field may require less or more water based on varying solar radiation, soil type, or surface or subsurface water flow paths. Since these processes are heterogeneous and complex, they are challenging to model or measure with the spatial resolution necessary to implement successful variable rate irrigation. In this paper, we present a new approach that uses high-resolution (20 to 100 m) satellite imagery obtained from Sentinel-2 and Landsat 8 to estimate zones of crop water stress. Our approach integrates two independent measures of crop water availability – Normalized Difference Water Index, and Evapotranspiration modelled using remotely sensed land surface temperature – with k-means clustering to delineate zones in an automated fashion. Our study area in southern Idaho has been set up experimentally to grow crops that are water-stressed and not water-stressed, thus providing a rigorous design to test and validate our method. The results of our method agree well with our independent suite of crop water stress measures, such as yield, variable irrigation rates, and matric potential. This method can be used to develop data-driven zones for variable rate irrigation, which has the potential to improve the efficiency of water use without sacrificing yield.