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 second of two camelina experiments was completed in FY2008. Data from the experiments are being used to develop remote sensing approaches for monitoring crop ET, and to determine the water use and irrigation scheduling for camelina grown in the region. Two remote sensing methods were evaluated in FY2008. One method estimated ET from crop coefficients that were determined from a remote sensing vegetation index called the normalized vegetation index (NDVI). The other approach used aerial remote sensing imagery of NDVI and canopy temperature to calculate ET by a surface energy balance model. We have been developing several techniques during FY2008 for obtaining spatial NDVI information in a large-field setting in preparation for our FY2009 cotton experiment. One method collects NDVI data by active vegetation index sensors mounted on a tractor. During the past two years, we collected active sensor NDVI data that helps optimize the NDVI crop coefficient model. The main advantage of the active sensor is that it is insensitive to sun angle. This will allow NDVI measurements over the entire field during normal tractor operations without loss of data quality. A prototype 3-band radiometer was built this year as part of a wireless mesh network system. We plan to install a number of these radiometers to provide continuous NDVI data at fixed locations in the large field. Final development and testing of the unpiloted aerial vehicle will be performed by University of Arizona personnel. An improved autopilot package was installed, which will enable collection of frequent spatial NDVI data. The helicopter-based imaging system was tested over the camelina growing season with new on-board computer and monitor. The operating software was modified to improve reliability and ease of use. This system is now ready to provide remote sensing imagery during the large-field experiment, which will be used to periodically map ET for the entire field using surface energy balance modeling. During FY2008, the CERES-Wheat plant growth model was evaluated for local conditions using experimental data from our previous wheat experiments in Maricopa. The model was evaluated for its ability to simulate plant growth responses and soil moisture conditions under varying irrigation, plant population, and nitrogen levels. Techniques were also explored for implementing the model as a real-time predictor of crop water use for irrigation scheduling. Spectral reflectance characteristics of lesquerella canopies were evaluated during a collaborative 2008 field experiment in Maricopa. The lesquerella reflectance data are being developed to use remote sensing for identifying key growth stages and to optimize harvest schedules. During FY2008, team scientists participated in a multi-agency remote sensing field campaign in Bushland, TX, including the planning and coordinating of field data collection. A prime objective is to validate and improve ET methodologies using remote sensing. All of the above activities address issues within National Program 211, Water Availability and Watershed Management, Problem Area 2, Irrigation Water Management.
1. Two-source energy balance ET approach validated for irrigated wheat and camelina. Estimating and monitoring the differences in ET within fields and farms is becoming increasingly important for managing irrigation under scarce water supplies. Studies were conducted by the ARS scientists in the Water Management and Conservation Research Unit in Maricopa, AZ where daily ET was modeled using a two-source energy balance approach, in which the model inputs for crop cover and plant temperature were obtained from airborne remote sensing images for wheat and from ground-based weather and remote sensing data for camelina. For both crops, the modeled ET agreed well with independent measures of ET from soil moisture measurements. These positive experimental results will help current researchers develop operational techniques that can potentially assist growers in their efforts to find more effective ways to schedule irrigation events. The accomplishment addresses National Program 211, Water Availability and Watershed Management, Problem Area 2, Irrigation Water Management.
2. Evaluation of DRAINMOD-NII for conditions in Iowa. DRAINMOD-NII is a new nitrogen cycling model that simulates the dynamics of nitrogen movement through agriculture systems using the hydrology simulation results from the DRAINMOD water management model. ARS scientists in Maricopa, AZ performed the first-ever evaluation of DRAINMOD-NII for conditions in the Midwestern, United States. Using ten years of experimental data from an agricultural system in central Iowa, the model was evaluated for its ability to simulate the effect of variable nitrogen fertilizer application rates on nitrate losses through subsurface drainage systems. The evaluated model will serve as a useful tool for studying the effectiveness of alternative management practices, such as drainage water management, to reduce losses of nitrate to surface water bodies in the Midwestern, United States. The accomplishment addresses National Program 211, Water Availability and Watershed Management, Problem Area 3, Drainage Water Management Systems.
5. Significant Activities that Support Special Target Populations
El-Shikha, D.M., Barnes, E.M., Clarke, T.R., Hunsaker, D.J., Haberland, J.A., Pinter Jr, P.J., Waller, P.M., Thompson, T.L. 2008. Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI). Transactions of the ASABE. 51(1):73-82.