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1

Northwestern Ag Research Center, Montana State University, Kalispell, MT 59901, USA

2

Department of Plant and Soil Science, Texas Tech University, 3810 4th Street, Lubbock, TX 79415, USA

3

Monsanto Company, 700 Chesterfield Pkwy W, Chesterfield, MO 63017, USA

4

Texas A&M AgriLife Research and Extension Center, 823 W US 70, Plainview, TX 79072, USA



Current Address: Dupont Pioneer, 2302 SE 9th Street Hermiston, OR 97839, USA





*

Author to whom correspondence should be addressed.



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. View Full-Text

Keywords: cotton; water stress; irrigation; remote sensing; soil brightness; ground cover; temperature; fusion technique cotton; water stress; irrigation; remote sensing; soil brightness; ground cover; temperature; fusion technique





Autor: Jessica A. Torrion 1,* , Stephan J. Maas 2, Wenxuan Guo 3, James P. Bordovsky 4 and Andy M. Cranmer 4,†

Fuente: http://mdpi.com/



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