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United States Department of Agriculture

Agricultural Research Service


Location: Water Management and Conservation Research

2010 Annual Report

1a.Objectives (from AD-416)
(1) Determine crop coefficients, adjustment algorithms, and transfer capabilities for improving crop water use estimation of common and alternative crops in arid Southwestern U.S. climates; (2) Develop and verify remote sensing methods and techniques for predicting near real-time evapotranspiration and plant water stress at spatial scales relevant for single fields to watersheds; (3) Develop high resolution remote sensing decision support tools for managing spatially and temporally variable water and nutrient applications to crops.

1b.Approach (from AD-416)
Remote sensing experiments will be conducted in small plots and on production-size fields at The University of Arizona, Maricopa Agricultural Center (MAC). Remote sensing data, including reflectance and emittance, will be collected at regular intervals during the crop growing seasons with aircraft-mounted sensors, as well as with ground-based, handheld instruments. Meteorological and air quality parameters will be obtained from micrometeorological stations installed within the fields. Assessment of air quality at experimental sites will be made in conjunction with remote sensing data collection times. Reference evapotranspiration (ET) data will be provided by local weather stations of the Arizona Meteorological (AZMET) Network. Field data collection will include measurements of soil water contents, soil physical and chemical properties, crop ET, and irrigation water applications. Plant growth observations will include crop growth stage, percent canopy cover, biomass, and crop yield.

3.Progress Report
The wheat plot experiment, completed in June, FY2009, and the cotton large field experiment, completed in Oct., FY2010, produced a large volume of multispectral airborne and ground-based remote sensing data, agronomic measurements, and soil and water data. During FY2010, 32 days of passive hand-held radiometry, 11 days of active hand-held radiometry, three days of machine-mounted active radiometry, and 12 days of airborne remote sensing image data sets obtained in the wheat and cotton studies were reduced and tabulated. Heat and water vapor flux data collected from two eddy-covariance stations at the cotton site were compiled, tabulated, and prepared for integration with soil-derived evapotranspiration (ET) data. Several hundred individual irrigation and fertilizer field records and related hydraulic data and several thousand hand measurements of canopy heights, plant stands, grain and cotton yields, lint percentages, and other agronomic field measurements were entered and electronically stored on spreadsheets. Thousands of raw soil water content readings were organized to spreadsheets, and thousands of soil samples were analyzed for texture and hydraulic properties and electronically summarized.

During FY2010, we also conducted a small-plot camelina experiment. This experiment will add to our two previous field experiments with camelina aimed to develop remote sensing tools, irrigation scheduling, and nutrient requirements for this potential biodiesel oilseed crop in the southwestern US. The FY10 camelina effort contributes new information for assessing spatial crop evapotranspiration estimates based on remote sensing, for providing preliminary guidelines for camelina nitrogen management, and for developing and calibrating a camelina crop growth model.

In FY2010, we deployed our remote sensing irrigation monitoring and management system on a large barley field in central Arizona. The effort, in cooperation with a local farmer and a farm consultant, was intended to test and evaluate our system on a commercial-size field, as well as to help generate interest in applying our remote sensing approaches by the farming community. The barley study also provided our University of Arizona collaborator with an opportunity to test new algorithms for the irrigation scheduling component of our system with small grains. This trial effort will be of benefit in conducting our large field wheat experiment planed for FY2011 in Maricopa.

Methods were developed to assimilate leaf area index (LAI) estimated by remote sensing to improve CERES-Wheat crop growth model simulations of evapotranspiration (ET) and biomass. This work led to a $412,436 NASA grant that will develop procedures to forecast regional crop yield using data assimilation by merging soil moisture and LAI estimates from satellite remote sensing into CSM-CROPSIM-CERES-Wheat and other agricultural systems models

1. Using Remote Sensing for Water Use Efficiency. Estimation of evaporation of water from plants or ET plants with satellite and airborne remote sensing has great promise for increased crop water use efficiency in irrigated lands because spatial water use patterns can be readily distinguished. Remote sensing with thermal infrared in particular can be especially helpful for detecting crop water stress. However, implementing remote sensing image data into farm-scale routines has been very difficult because of cloudy skies, infrequent images, or coarse resolution. Analysis from field data collected at Maricopa by ARS Scientists show that these problems can be reduced by combining spatially oriented remote sensing data with time oriented land surface temperature data from fixed ground-based sites. This research shows that this combined data approach can reasonably forecast crop ET up to 10 days without additional remote sensing data. This information will be valuable for scientists and engineers involved in developing irrigation decision support tools.

Review Publications
Thorp, K.R., Youssef, M.A., Jaynes, D.B., Malone, R.W., Ma, L. 2009. DRAINMOD-N II: Evaluated for an agricultural system in Iowa and compared to RZWQM-DSSAT. Transactions of the ASABE. 52(5):1557-1573.

Thorp, K.R., Hunsaker, D.J., French, A.N., White, J.W., Clarke, T.R., Pinter Jr, P.J. 2010. Evaluation of the CSM-CROPSIM-CERES-Wheat Model as a Tool for Crop Water Management. Transactions of the ASABE. 53(1):87-102.

Thorp, K.R., Hunsaker, D.J., French, A.N. 2010. Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model. Transactions of the ASABE. 53(1):251-262.

French, Andrew N. and Inamdar, Anand(2010) Land cover characterization for hydrological modelling using thermal infrared emissivities, International Journal of Remote Sensing, 31: 14, 3867 — 3883

Last Modified: 4/16/2014
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