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:
This is the final report for the project 5347-13660-006-00D, terminated in February 2012 and replaced with project 5347-13660-007-00D. See the FY2012 Annual report of the new project for additional information. Progress was made on all three objectives and their subobjectives, all of which were under the ARS Water Availability and Watershed Management NP 211 action plan. Under Objective 1.1, we made available new crop evapotranspiration (ET) and crop coefficient (Kc) information for use in arid irrigated regions. This included information for wheat, cotton, and the biodiesel oilseed crop, camelina. Objective 2.1 expanded the Kc information by providing real-time, spatially-distributed crop coefficients obtained with remote sensing. Field experiments validated the remote sensing Kc and confirmed their ability to achieve significant water use savings in irrigated crop production. Although crop growth changes and spatial variations can be monitored with remote sensing, utilizing this information to improve irrigation management can be challenging to growers. This problem, addressed in Objective 3.1, resulted in a new decision support model for managing and assessing the spatial and temporal inputs of crop and soil as obtained using remote sensing observations. Irrigation studies with cotton were conducted in farm-scale fields to further develop the irrigation decision model as a real-time management tool. Significant progress was made in the development and verification of remote sensing and surface energy balance modeling of ET in Objective 2.2. The farm-scale application of ET estimation was improved through development of a new remote sensing platform; including a software user interface for operation of remote sensing cameras onboard a helicopter. The methodology was extended, making decision support more robust by improved handling of problems encountered due to cloudy skies, infrequent images, and coarse resolution. Under Objective 3.2, a procedure was developed to statistically select the best field locations for placing fixed ground-based sensors to collect daily vegetation index data. Under Objective 3.3, semi-automated programs were incorporated into the remote sensing image processing routines to correct for atmospheric distortions due to dust and water vapor in the visible and infrared bands. These programs are now being used as part of the NASA-funded project to estimate ET over crops and rangeland in the U.S. Southwest. Under Objective 3.4, methods were developed to assimilate leaf area index (LAI) estimated by remote sensing to improve CERES-Wheat crop growth model simulations of ET and biomass. This initial work led to a new NASA grant to 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. A methodology was also developed to process remotely-acquired digital images of lesquerella canopies for determining flower count.
Thorp, K.R., White, J.W., Porter, C.H., Hoogenboom, G., Nearing, G.S., French, A.N. 2012. Methodology to evaluate the performance of simulation models for alternative compiler and operating system configurations. Computers and Electronics in Agriculture. 81(1):62-71.