1. Grass-like Crop Canopies
EECS-823 Phenomenology Presentation

2. Physical Characteristics of Poaceae
Grass as a term encompasses several species
Wheat, barley, millet, rice, oat, corn, bamboo
Various uses of the plants
Head often used for consumption (wheat, rice, corn)
Stem and leaves used for both consumption and structure (hay, bamboo)
Roots used for agricultural control (ryegrass,)
Ornamental purposes (lawns)
Industrial products (paper, ethanol, glue and binding agents)
Growth cycle results in development dependent structure
grain/cereal, leaf/steam,

3. Vegetation Scattering
The backscatter from any type of vegetation is dependent on the biomass and water content of the plant.
Water distribution varies along with plant structure, and in grasses the tillers, stalk, and head will vary in content and response across species.
Due to the profile of the plants, the backscatter coefficient is greatly dependent on the look angle and orientation of the setup used.
Total backscatter includes direct reflection from the ground and plant, along with plant->ground and ground- >plant scatter among other paths.

4. Canopy Modeling
Assuming a high plant density, a random distribution and orientation of plants, and a low angle of incidence, grasses can be modeled as layers, or horizontal dielectric slabs
Breaking into structural components, the head, stalks, and leaves would each have a refractive index of Where the volume coefficient has height dependence.
The total attenuation at any layer then is dependent on the sum of the component indices.
Doing so however ignores horizontal interactions, and does not extend well to more extreme incidence angles.
More explicit modeling of the plant can be done that resolves issues, but introduces complexity
Representing stalks and heads as dielectric cylinders, and the ground and leaves as discrete dielectric slabs significantly increases complexity for analysis and simulation
In cases of tall discrete grasses, the density condition is frequently met.

5. e_b represents the dielectric of the binding solution
Dielectric Model
Water distribution in the plant is non-discrete for the most part, and can be modeled as a continuous dielectric.
The dielectric has a free water component and a bound water component.
Free water includes contaminates such as various sugars and brines for the grass resulting in an increased conduction term
Bound water represents water bonded in the plant and is usually dwarfed by the free water term.
e_b represents the dielectric of the binding solution

6. Stalk Response
Stalks typically have less moisture content than leaves, sometimes less than half.
At nadir and assuming healthy grass, backscatter from the stalk is minimal compared to off-shooting leaves.
Due to stalk orientation, extinction and scattering are polarization dependent, with both increasing as vertical polarization increases.

7. Head Response Leaf Response
The head is similar to that of the stalk in that it is usually an approximate vertical cylinder, but it usually differs sufficiently to produce noticeable radar response.
Head moisture content can exceed 90% in some grasses at certain stages of development.
Head thickness can also be several times more than that of the stalk, resulting in increased nadir response.
Leaf Response
In grasses such as wheat, leaves produce the majority of backscatter at angles close to nadir.
The surface area density of green leaves is usually represented as the leaf area index, which serves as a proxy for total water content and correlates with backscatter coefficient.
Optical

8. Ground Reflection
For grass-like crops, soil water content can range from completely dry to fully saturated, such as in the case of immediately following rain, or early development rice.
For higher moisture content levels, the ground will dominate the response as a surface scatterer at nadir.
In general, increasing frequency, moving off of nadir, and using compacted soil with lower ground moisture content will decrease the ground backscatter relative to the canopy backscatter.

9. Group Effects
The geometries of how crops are planted in groups also affects the radar response.
As many crops are planted in rows, the scattering becomes dependent on the polarization with respect to the row orientation.
Increasing density of plants also affects the water holding capacity of the topsoil.
1 m row spacing
2.5 m plant height
Increase frequency, row dependence decreases

10. Extra Considerations
Most of the previous information assumes healthy grass in normal harvest conditions.
The presence of dew, residual rainwater, and snow can all increase backscatter significantly.
Excess wind and rain can knock over plants, changing polarization.
Excess sunlight or soil nutrients can dry out plants.
Some grasses over-extend in early growth due to low sunlight or nutrients, causing them to fall over.

11. Image Sources Referenced Material
First plant structure image: Wheat development image: Row response dependence plots: Radar Look Direction and Row Crops Batlivala P, Ulaby F Photogrammetric Engineering and Remote Sensing Vol 42, No. 2, Feb 1976 Soil Moisture Content Density Dependence image: Dielectric Model, Stalk Effect plots, and all other plots: Microwave Radar and Radiometric Remote Sensing, Ulaby and Long Photos of individual plants taken by me.
Referenced Material
Microwave Radar and Radiometric Remote Sensing F.T. Ulaby, D.G. Long Artech House (2014)
Radar response from vegetation with nodal structure. J. Blanchard, B & E. Oneill, P. (1984)
The effect of vegetation type, microrelief, and incidence angle on radar backscatter. Owe, M & E. Oneill, P & Jackson, T.J. & Schmugge, T. (Jul 1985). NTRS
Radar Response of Vegetation: An Overview F.T. Ulaby, M. Craig Dobson University of Michigan Accessed at:
Active Microwave Measurement of Soil Water Content F.T. Ulaby, J. Cihlar, R.K. Moore Remote Sensing of Environment Vol 3, (1974)
Microwave Dielectric Properties of Plant Materials F.T. Ulaby, R.P. Jedlicka IEEE Transactions on Geoscience and Remote Sensing, Vol GE-22, No. 4, July 1984
Radar Look Direction and Row Crops P.P. Batlivala, F.T. Ulaby F Photogrammetric Engineering and Remote Sensing Vol 42, No. 2, Feb 1976
Radar Remote Sensing of Spatial Crop Variability E. Zillmann, H. Lilienthal, T. Schrage, E. Schnug (2005)
Microwave Radar Response to Canopy Moisture, Leaf-Area Index, and Dry Weight of Wheat, Corn, and Sorghum T.W. Brakke, E.T. Kanemasu, J.L. Steiner, F.T. Ulaby, E. Wilson Remote Sensing of Environment Vol (1981)