Objectives   Develop procedures to quantitatively interpret remotely sensed data in terms of plant biophysical properties to allow better use of this technology for farm management and research. Particular emphasis is placed on water and fertilizer management issues important to arid Western agriculture.

Site 
A majority of our research is conducted at The University of Arizona's Maricopa Agricultural Center (MAC).

Platforms & Sensors  

Agricultural Irrigation Imaging System (AgIIS) and other ground-based platforms. Narrow and wide band radiometers covering visible, NIR, SWIR and thermal spectral regions. Hyperspectral field radiometers (Visible, NIR, SWIR). Thermal scanner.

Crop Water Stress Detection 

Infrared techniques and advanced crop water stress index (CWSI) products have been adapted for use with images obtained from aircraft and satellites. Shown here are maps indicating crop water use (left) and crop water stress (right). This type of analysis would enable farmers to map crop water needs and schedule irrigations. It also paves the way for estimating water losses to evaporation from very large areas.

Determining plant energy use

The relationship between vegetation indices and the fraction of absorbed photosynthetically active radiation (PAR) can track functional canopy development and provide an important linkage with existing crop models.

Images provide soil mapping tools 

Imagery of bare soil with a limited number of samples can be used to generate high resolution soil maps (“directed sampling”).

Spectral data detects nutrient stress

A new spectral index (canopy chlorophyll content index, CCCI) has recently been developed that shows a strong correlation with cotton and wheat nitrogen status.

Extending the value of images and models

Agricultural managers can benefit from daily information provided by crop models and the spatial resolution provided by imagery. Above, a color infrared image (left) was used in a computer model to generate a predicted yield map (right).

1. Determining surface component temperatures for use in 2-dimensional water stress indices. Example: Estimating dry bare soil temperature used in Water Deficit Index trapezoid.
2. Resolving apparent "inconsistencies" in RS products resulting from:
   • sensor deployment, filter response functions, calibration issues, and conversion to reflectance.
   • BRDF: viewing and illumination angles, variation in ratio of direct beam to diffuse, instrument field of view.
3. Improving techniques needed to integrate remotely sensed data with soil and crop models.
4. Finding sensors or combinations thereof sensitive to specific sources of management problems (i.e., P vs. N deficit, whitefly vs. mite infestation in cotton).
5. Lack of thermal infrared on current genre of commercial RS satellites & aircraft
6. Lack of shared spectral reference libraries for soils, leaves, and canopies under normal and stressed conditions. These libraries should include associated metadata.
7. No mechanism to determine equipment and software used by other ARS researchers and their experiences.

1. Test robustness of water deficit index trapezoid at appropriate field management scales.
2. Establish utility or lack thereof for short-wave infrared (1.1 to 2.5 um) in revealing crop water status.
3. Test methods to apply remotely sensed data to pre- harvest broccoli quality.
4. Incorporate remotely sensed estimates of fAPAR, nitrogen and crop water stress directly into existing crop models.
5. Further investigation of canopy chlorophyll content index and its potential use for N management.
6. Expand use of spectral crop coefficients in irrigation scheduling.

Current and Potential Research Cooperators:
Jim Schepers (ARS, Lincoln), Craig Daughtry (ARS, Beltsville), Jack Morgan (ARS, Ft. Collins). Precision farming expertise, remote sensing approaches for detecting crop condition, NPP, and yield estimation. James Everitt (ARS, Weslaco), Steve Maas (Texas Tech), Mickey McGuire (ARS, Shafter) - Airborne remote sensing systems. Walter Bausch (ARS, Fort Collins), Susan Moran (ARS, Tucson) Bill Kustas (ARS, Beltsville) - multispectral crop coefficients, large scale evapotranspiration, sensor calibration, image analysis. Dale Heerman (ARS, Fort Collins), Carl Camp (ARS, Florence), Don Wanjura (ARS, Lubbock), Terry Howell (ARS, Bushland), Pete Waller (Ag. & Biosystems Engineering Dept., Univ. Arizona, Tucson) - site specific irrigation management. David Jones (Univ. Nebraska, Lincoln) - Fuzzy logic,neural net analysis of spectral data; Laj Ahuja (ARS, Fort Collins), Robert Lascano (Texas Tech)., decision support systems, crop water consumption models with potential for RS input.