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Purdue Agronomy Farm Corn Solar Radiation Intercepted (SRI) (841805)

Listed in Datasets

By Kevin P. Gallo

The objectives of this research are 1) the identification and measurement of corn crop canopy variables, 2) development of relationships between identified variables, and 3) development and testing of methods in corn crop yield models

Additional materials available

Version 1.0 - published on 28 Jul 2015 doi:10.4231/R7B27S7R - cite this Archived on 25 Oct 2016

Licensed under CC0 1.0 Universal

Description

Spectral Estimates of Intercepted Photosynthetically Active Radiation and Application to Corn Yield Models

Rationale

In recent years the world's food situation has emphasized the need for timely information on world-wide crop production. Relatively few countries, however, have reliable methods for gathering and reporting crop production information.

Remote sensing from aerospace platforms can provide information about crops and soils which could be useful to crop production forecasting systems. The feasibility of utilizing multispectral data from satellites to identify and measure crop area has been demonstrated; however, relatively little research has been conducted on developing the capability of using multispectral data to provide information about crop condition and yield. If this spectrally derived information can be combined effectively with crop models which depict limitations imposed on crop yields by weather and climate, then potentially much better information about crop yield and production can be gained.

Literature Review

Solar radiation is an important energy source for photosynthesis, the process which governs the growth and production of plants. Numerous studies have found positive correlation between photosynthesis (or C02 assimilation) and the amount and/or intensity of solar radiation incident on a crop canopy (2, 8, 9, 10, 15, 16, 18). More recent works (3, 11) have concentrated on the relationship between photosynthesis and the amount of incident solar radiation that is intercepted and thereby available for utilization by the plants (ISR). Both SR and ISR, however, have been shown to be positively correlated with increases in plant matter as well as final grain yield (6, 7, 12, 15, 19, 20, 22, 23).

The interaction of SR with a crop canopy is a function of the amount of vegetative matter present, geometric configuration of the canopy, and solar zenith and azimuth angles (Norman, 1980). The amount of crop canopy vegetation present in a given area is often described by the leaf area index (LAI) or the percent crop cover (PCC). Most model estimates of crop canopy ISR involve measurement of some, or all, of the above variables. Measurement of these variables, however, is tedious and time consuming. The direct measurement of light interception by a canopy has been found to be a more direct and simpler approach (Hesketh and Baker, 1967).

A more simple method of estimating the amount of light intercepted by a canopy may be through remote sensing of canopy reflectance, as canopy reflectance is a function of many of the same variables that govern ISR. Daughtry et al. (1982) estimated ISR

ISR = 1 - exp (-0.79 LAI)

where LAI is the measured leaf area index and 0.79 is the extinction coefficient used by Linvill et al. Seasonal ISR was regressed on spectral variables which were computed from reflectance measurements made over the corn crop canopy through the growing season. A coefficient of determination of 0.90 was ascertained for the quadratic relationship between ISR and the spectral variable Greenness Index (Malila and Gleason, 1977). In actuality only about 50% of the SR intercepted by a crop canopy causes a photosynthetic response. The portion of the SR spectral which activates photosynthesis has been defined as photosynthetically active radiation (PAR), with a waveband ranging from 0.4 to 0.7 um (Monteith, 1969). IPAR rather than ISR is therefore the preferred measure of canopy interception of radiation.

IPAR (or ISR) is only one of a number of factors which influence crop growth and final yield. Other factors essential to crop growth and production include temperature, moisture availability, plant nutrients, carbon dioxide and management practices. Due to its importance as an energy source for photosynthesis, and in turn crop growth and production, IPAR (or ISR) is a factor included in many models of crop growth and production (1, 4, 20, 21).

Objectives

The overall objectives of this research are 1) the identification and measurement of corn crop canopy variables which are related to crop growth and final yield, 2) development of relationships between variables identified in Objective 1 and remotely sensed canopy spectral reflectance, and 3) development and testing of methods for combining spectral and meteorological data in corn crop yield models which are capable of providing accurate estimates of crop conditions and yield potential, throughout the growing season.

REFERENCES

  1. Arkin, G. F., R. L. Vanderlip, and J. T. Ritchie. 1975. A dynamic sorghum growth model. Trans. Am. Soc. Agric. Eng. 19:622-626, 630.
  2. Baker, D. N. and R. B. Musgrave. 1964. Photosynthesis under field conditions. V. Further plant chamber studies of the effects of light corn (Zea mays L.) Crop Sci. 2:127-131. from field measurements of corn LAI using the following equation from Linvill et al. (1978):
  3. Brown, R. H. and R. E. Blasser. 1980. Leaf area index in pasture growth. Herbage Abstracts 38:1-8.
  4. Coelho, D. T. and R. F. Dale. 1980. An energy-crop growth variable and temperature function for predicting corn growth and development: planting to silking. Agron. J. 72:503-510.
  5. Daughtry, C. S. T., K. P. Gallo, and M. E. Bauer. 1982. Spectral estimates of solar radiation intercepted by corn canopies. LARS Tech. Report 030182. Lab. Applic. Remote Sensing, Purdue Univ., West Lafayette, IN.
  6. Duncan, W. G., D. L. Shaver, and W. A. Williams. 1973. Insolation and temperature effects on maize growth and yield. Crop Sci. 13:187-191.
  7. Gallagher, J. N. and P. V. Biscoe. 1978. Radiation absorption, growth and yield of cereals. J. Agric. Sci., Camb. 91:47-60.
  8. Hesketh, and D. Baker. 1967. Light and carbon assimilation by plant communities. Crop Sci. 7:285-293.
  9. Hesketh, J. D. and D. N. Moss. 1963. Variation in the response of photosynthesis to light. Crop Sci. 3:107-110.
  10. Hesketh, J. D. and R. B. Musgrave. 1962. Photosynthesis under field conditions. IV. Light studies with individual corn leaves. Crop Sci. 2:311-315.
  11. Kanemasu, E. T. and G. F. Arkin. 1974. Radiant energy and light environment of crops. Agr. Metr. 14:211-225.
  12. Linvill, D. E., R. F. Dale, and H. F. Hodges. 1978. Solar radiation weighting for weather and corn growth models. Agron. J. 70:257-263.
  13. Malila, W. A. and J. M. Gleason. 1977. Investigations of spectral separability of small grains, early season wheat detection and multicrop inventory planning. Tech. Report 122700-34-F. Environmental Research Institute of Michigan, Ann Arbor, MI.
  14. Monteith, J. L. 1969. Light interception and radiative exchange in crop stands. In physiological aspects of crop yield. J. D. Eastin, F. A. Haskins, C. Y. Sullivan, and C. H. M. Van Bavel (eds.). ASA, CSSA, Madison, WI.
  15. Moss, D. N. and R. B. Musgrave. 1971. Photosynthesis and crop production. p. 317-334 in Adv. in Agronomy (23) ASA. N. C. Brady ed. Academic Press, NY.
  16. Moss, D. N., R. B. Musgrave, and E. R. Lemon. 1961. Photosynthesis under field conditions. III. Crop Sci. 1:83-87.
  17. Norman, J. M. 1980. Interfacing leaf and canopy light interception models. p. 49-68. In J. D. Hesketh and J. W. Jones (ed.) Predicting photosynthesis for ecosystem models. Vol. 2. CRC Press. Boca Raton, FL.
  18. Saeki, T. 1960. Interrelationships between leaf amount, light distribution and total photosynthesis in a plant community. Bot. Mag. (Tokyo) 73:55-63.
  19. Shibles, R. M. and C. R. Weber. 1965. Leaf area, solar radiation interception and dry matter production by soybeans. Crop Sci. 5:575- 577.
  20. Steven, M. D. 1982. Optical remote sensing for predictions of crop yields. Proc. Soc. Photo-Optical Inst. Eng. (In Press).
  21. Strapper, M. and G. F. Arkin. 1979. CORNF: A dynamic growth and development model for maize (Zea mays L.). Program and model documentation No. 80-2. The Texas Agric. Exp. Stn., Blackland Resr. Center, Temple, Texas.
  22. Williams, W. A., R. S. Loomis and C. R. Lepley. 1965. Vegetative growth of corn and affected by population density. Crop Sci. 5:211- 215.
  23. Witt, M. D., R. L. Vanderlip, L. D. Bark. 1972. Effect of two width and orientation on light intercepted by grain sorghum. Trans. Kansas. Acad. Sci. 75(1) 29-40.

The test took place in 1984.

The supporting docs include a brief summary of used instruments, a wavelength table (Wavelength_ASCII.txt), reflectance note and reflectance tables (ReflectanceTable206.txt and ReflectanceTableMulti.txt), and file format description (ExperimentDataFormat3.txt). The format description file is in ASCII format in lines of 80 characters.

This research dataset is part the Field Research Data Library that consists of over 200,000 spectral observations of soils and vegetation that have been collected since 1972 till 1991 as part of the research focused on vegetation and soils at the Laboratory for Applications of Remote Sensing (LARS) located at the Purdue University.

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Notes

  • Location: Purdue Agronomy Farm, West Lafayette
  • County: Tippecanoe
  • Latitude/Longitude: 0402813N 0865927W
  • Illumination: Solar
  • Experiment Type: Crops - Corn
  • Landsat MSS Band Radiometer: Exotech 100 (Wavelength Range: 0.50-1.10 um): 841805.051.txt (327 KB)
  • Landsat TM Band Radiometer: Barnes 12-1000 (Wavelength Range: 0.45-2.35 um): 841805.091.txt (327 KB)
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