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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #236933
Title: Irrigation Management

Author
item Evett, Steven - Steve
item Colaizzi, Paul
item Oshaughnessy, Susan
item Hunsaker, Douglas - Doug
item Evans, Robert

Submitted to: Encyclopedia of Remote Sensing
Publication Type: Book / Chapter
Publication Acceptance Date: 2/12/2009
Publication Date: 6/19/2012
Citation: Evett, S.R., Colaizzi, P.D., O'shaughnessy, S.A., Hunsaker, D.J., Evans, R.G. 2012. Irrigation Management. In: Njoku, E., editor. Encyclopedia of Remote Sensing. SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg. DOI: 10.1007/SpringerReference_327137 2012-06-19 17:54:39 UTC.

Interpretive Summary: Water resource constraints are becoming more severe in the U.S. at the same time that pressure to produce more crops for biofuels, food and fiber is increasing. Since irrigation is used on 20% of U.S. cultivated land to produce >60% of crops, while consuming about 60% of freshwater resources, it is critical that irrigation management be effective in producing more crop economic yield per unit of water applied. USDA-ARS scientists assessed the usefulness of remote sensing techniques, including near-surface remote sensing using sensors mounted on moving irrigation systems, in promoting this water use efficiency. They identified five key approaches, three of which use near-surface remote sensing, which employs sensors mounted on moving irrigation systems or masts in fields, and two of which use satellite or aircraft sensor platforms. The latter two methods have held promise for many years, but have not become useful due to problems of timeliness (satellite passes only every other week), lack of appropriate sensors for surface temperature, image resolution that is too coarse (can’t see what’s going on in a producer’s field), high cost, and data processing complexity. The approaches that use near-surface remote sensing avoid most of these problems, and two of them are already partially commercialized, allowing sensing of crop water stress and providing irrigation timing to producers. Obstacles to successful use of these approaches are identified so that research and development efforts may be focused on solving these problems.

Technical Abstract: Crop water use (evapotranspiration = ET) is often estimated as ET = Kc x ETo, where ETo is a reference ET calculated from weather data and Kc is a crop coefficient that is specific to a crop variety and stage of growth. This paradigm has become the dominant one in state and federal efforts to provide irrigation scheduling information to farmers in the USA as evidenced by networks of weather stations and corresponding water use prediction systems established by both federal agencies and the states. Given the current state of practice in irrigation management, the challenge for remote sensing is to provide irrigation management tools that allow producers to obtain yield, yield quality and water and nutrient use efficiencies, comparable to those obtained using current practice, profitably and sustainably, which means that such tools must be practical and cost effective. There are several approaches to irrigation management using remote sensing, including: 1 – Scheduling irrigation to replace ET estimated from a reference ET (ETo), calculated from local weather data, which is multiplied by a crop coefficient estimated with a crop coefficient function, Kc(NDVI), where NDVI is the normalized difference vegetative index (NDVI) or a similar index adjusted for reflectance from soil. The NDVI can be remotely sensed. 2 – Scheduling irrigations at a fixed amount whenever a trigger to irrigate is generated by the crop water stress index (CWSI), which is estimated using remotely sensed Ts and local weather data. 3 – Scheduling irrigations at a fixed amount when triggered by the time-temperature threshold index (TTTI) reaching a crop and region-specific value. The TTTI is calculated using Ts. 4 – Scheduling irrigation to replace ET estimated with the field surface energy balance (FSEB), which uses remotely sensed surface temperature, Ts, determined from thermal infrared data, and data on canopy cover and surface emissivity deduced from the near infrared (NIR) and visible bands. 5 – Sensing of crop characteristics in order to guide timing, placement and amount of fertilizer and water through irrigation (or fertigation) systems of various orders of precision. The characteristics, including crop cover fraction, nitrogen status of leaves, disease and pest damage, all of which vary spatially and temporally, are inferred from various remotely sensed vegetative indices (VI). This chapter provides an introduction to these approaches with emphasis on problems to be solved and numerous citations for further study.