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ARS Home » Pacific West Area » Tucson, Arizona » SWRC » Research » Publications at this Location » Publication #169408
Title: CROP WATER STRESS DETECTION USING REMOTE SENSING 1652

Author
item Moran, Mary
item ZARCO-TEJADA, P. - CIFA, SPAIN
item Clarke, Thomas

Submitted to: Encyclopedia of Water Science
Publication Type: Book / Chapter
Publication Acceptance Date: 12/10/2004
Publication Date: 10/1/2005
Citation: Moran, M.S., Zarco-Tejada, P., Clarke, T.R. 2005. Crop water stress detection using remote sensing. Encyclopedia of Water Sci., Dr. Jay H. Lehr (ed.), John Wiley and Sons Pub., 12 p.

Interpretive Summary: Over the past thirty years, progress has been made on the use of remotely sensed data from satellite-based cameras for retrieval of information useful for irrigation scheduling and management. This chapter offers a review of image-based approaches used to measure crop water status (e.g., crop water loss, crop metabolism and photosynthesis) and plant manifestations of chronic crop water stress (e.g., leaf growth and leaf loss). Based on this review, concrete suggestions were made for satellite sensor development, improved image availability, timely image delivery, and reduced image cost. This review provides a wide audience with the fundamentals and state-of-the-art of remote sensing for crop water stress detection.

Technical Abstract: Remote sensing in the optical spectrum has potential to detect plant manifestations of both transient and chronic crop water stress. Crop water status and evaporation rate have been directly related to thermal indices, such as crop water stress index (CWSI), water deficit index (WDI) and thermal kinetic window (TKW) index. Measurements of surface reflectance in narrow visible and near-infrared wavelengths have potential for monitoring crop photosynthesis and fluorescence. Surface reflectances in wide bands of the visible, near-IR and short-wave IR spectrum have been used to detect plant manifestations of chronic crop water stress, such as phenologic adaptations and leaf expansion and loss. To apply these approaches with satellite-based sensors for farm-scale irrigation scheduling and crop management, there are still needs for improved sensor design, timely image delivery, and reduced image cost