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 large field cotton experiment, to be completed in Oct. 2011, produced a large volume of multispectral airborne and ground-based remote sensing measurements. A new helicopter platform was developed; including a software user interface for operation of remote sensing cameras onboard the helicopter. Soil textural and water holding properties were mapped for the field using apparent electrical conductivity measurements. Data for evaluating crop evapotranspiration (ET) included wireless, micro-meteorological data at 24 locations, and data from two eddy-covariance stations, two scintillometers, and five ET gauges. Other field data included irrigation and fertilizer data, canopy heights, soil water contents, and cotton canopy cover, temperature, and leaf area index. Also collected were biomass samples for estimating cotton leaf weight, stem weight, and boll weight. The field data is used to validate our remote sensing approaches for estimating spatially distributed crop ET, and is used in calibrating and evaluating cotton crop growth models for use in irrigation water management. We developed an approach to evaluate crop simulation model performance using diverse Fortran compilers on multiple computer operating systems. A single desktop computer with five identical hard drives was designed to make comparisons between five operating systems while minimizing differences in hardware configuration. Compatibility and performance issues among compiler and operating system combinations were tested for the well-known Cropping System Model. However, the methodology is applicable for improving robustness and performance while facilitating the use of many simulation models in a wide range of computer environments. To provide critical data needed for modeling crop water use, stress and heat tolerance of cotton, micro-meteorological instruments were installed in a 1-ha cotton experiment at Maricopa. Multispectral image and radiometric data was collected to develop new ways to rapidly screen for desirable plant phenotypes. A methodology was designed to process digital images of lesquerella canopies for flower count. Key features of the image processing approach included an image transformation to the hue, saturation, and intensity color space and an approach to address uncertainty in the parameters used. Information from the digital image processing method can be used to determine optimum times for irrigation water management, desiccant application, crop harvest, and installation of bee boxes to maximize pollination. A nitrogen (N) fertilizer study with surface-irrigated cotton was conducted to compare reflectance-based N fertilizer management with standard soil test-based and N management. We also compared ground fertilizer applications of N against water-run (fertigation) applications of N. Collaborative ET research with ARS scientists at Jornada, New Mexico, continued. Field-based data were acquired for land surface temperatures and vegetation cover. This work is also collaborative with NASA who provides the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image data used in the research.
1. Imaging technology for breeding and management of lesquerella. Lesquerella seed oil may be used as a bio-renewable petroleum substitute in the production of many industrial products. However, before the crop can be produced commercially, informational tools are needed to guide breeding efforts and to assist with crop management. USDA-ARS research scientists at Maricopa, Arizona, developed a digital imaging approach to monitor the progression of flowering in lesquerella. The imaging approach provided flowering information more inexpensively, more practically, and with greater accuracy than previously reported approaches. Application will aid breeders in the selection of optimum varieties and will aid growers with irrigation management and harvest decisions. Results will benefit plant breeders, growers, and others aiming to develop lesquerella into a commercially viable oilseed crop for production of bio-renewable products.
Thorp, K.R., Dierig, D.A., French, A.N., Hunsaker, D.J. 2010. Analysis of hyperspectral reflectance data for monitoring growth and development of lesquerella. Industrial Crops and Products. 33(2):524-531.