A three-dimensional index for characterizing crop water stress

Jessica A. Torrion, Stephan J. Maas, Wenxuan Guo, James P. Bordovsky, Andy M. Cranmer

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict water stress interpretation by thermal remote sensing. For partial GC, the combination of plant canopy temperature and surrounding soil temperature in an image pixel is expressed as surface temperature (Ts). Soil brightness (SB) for an image scene varies with surface soil moisture. This study evaluates SB, GC and Ts-Ta and determines a fusion approach to assess crop water stress. The study was conducted (2007 and 2008) on a commercial scale, center pivot irrigated research site in the Texas High Plains. High-resolution aircraft-based imagery (red, near-infrared and thermal) was acquired on clear days. The GC and SB were derived using the Perpendicular Vegetation Index approach. The Ts-Ta was derived using an array of ground Ts sensors, thermal imagery and weather station air temperature. The Ts-Ta, GC and SB were fused using the hue, saturation, intensity method, respectively. Results showed that this method can be used to assess water stress in reference to the differential irrigation plots and corresponding yield without the use of additional energy balance calculation for water stress in partial GC conditions.

Original languageEnglish
Pages (from-to)4025-4042
Number of pages18
JournalRemote Sensing
Volume6
Issue number5
DOIs
StatePublished - May 2014

Keywords

  • Cotton
  • Fusion technique
  • Ground cover
  • Irrigation
  • Remote sensing
  • Soil brightness
  • Temperature
  • Water stress

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    Torrion, J. A., Maas, S. J., Guo, W., Bordovsky, J. P., & Cranmer, A. M. (2014). A three-dimensional index for characterizing crop water stress. Remote Sensing, 6(5), 4025-4042. https://doi.org/10.3390/rs6054025