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Use of Remotely-Sensed Data in Cotton Growth Model  

Ko, Jong-Han (Texas A&M University Texas Agricultural Experiment Station)
Maas, Stephan J. (Texas Tech University Plant and Soil Sciences)
Publication Information
KOREAN JOURNAL OF CROP SCIENCE / v.52, no.4, 2007 , pp. 393-402 More about this Journal
Abstract
Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.
Keywords
crop model; LAI; PVI; remote sensing; within season calibration;
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1 Arkin, G. F., C. L. Wiegand, and H. Huddleston. 1977. The future role of a crop model in large area yield estimation. In Proceedings of the Crop Modeling Workshop, PP. 87-116. USDA-NOAA-EDIS-CEAS, Columbia, MO
2 Baez-Gonzalez, A. D., P. Chen, M. Tiscareno-Lopez, and R. Srinivasan. 2002. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop Sci. 42 : 1943-1949   DOI   ScienceOn
3 Constable, G. C. and A. B. Hearn. 1981. Irrigation of crops in a subhumid environment. VI. Effect of irrigation and nitrogen fertilizer on growth, yield, and quality of cotton. Irrigation Science 3 : 17-28
4 Guo, W. 2005. Spatial and temporal variability in cotton yield in relation to soil apparent electrical conductivity, topography, and remote sensing imagery. Ph. D. diss. Texas Tech Univ., Lubbock
5 Howell, T. A., K. R. Davis, R. L. McCormick, H. Yamada, V. T. Walhood, and D. W. Meek. 1984. Water use efficiency in narrow row cotton. Irr. Sci. 5 : 195-214
6 Jones, C. A. and J. R. Kiniry. 1986. CERES-MAIZE: A simulation model of maize growth and development. Texas A&M University Press. College Station, TX
7 Maas, S. J. 1998. Estimating cotton ground cover from remotely sensed scene reflectance. Agron. J. 90 : 384-388   DOI   ScienceOn
8 Maas, S. J. 1992. GRAMI: a crop growth model that can use remotely sensed information. USDA, ARS-91, 77p
9 Reddy, V. R., B. Acock, D. N. Baker, and M. Acock. 1989. Seasonal leaf area - leaf weight relationships in the cotton canopy. Agron. J. 81 : 1-4   DOI
10 Richardson, A. J. and C. L. Wiegand. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43 : 1541-1552
11 Sanders, V. O., M. P. Bange, and S. P. Milroy. 1997. Reproductive allocation of cotton in response to plant environmental factors. Annuals of Botany. 80 : 75-81   DOI   ScienceOn
12 Wanjura, D. F. and J. R. Supak. 1985. Temperature methods for monitoring cotton development. Beltwide Cotton Conferences. pp. 369-372
13 Ritchie, J. T. and S. Otter. 1985. Description and performance of CERES-Wheat: A User-oriented wheat yield model. P. 159-175. In ARS Wheat Yield Project. ARS-38. National Technology Information Service, Springfield, VA
14 Monteith, J. L. and M. H. Unsworth. 1990. Principles of environmental physics, second edition. Edward Arnold. New York. 291p
15 Maas, S. J. 1993a. Parameterized model of gramineous crop growth: I. Leaf area and dry mass simulation. Agron. J. 85 : 348-353   DOI   ScienceOn
16 Maas, S. J. 2000. Linear mixture modeling approach for estimating cotton canopy ground cover using satellite multi-spectral imagery. Remote sensing. Environ. 73 : 304-308
17 Barns, M. B. P. J. Pinter Jr., B. A. Kimball, G. W. Wall, R. L. LaMorte, D. J. Husaker, F. Adamsen, S. Leavitt, T. Thompson, and J. Mathius. 1997. Modification of CERES-Wheat to accept leaf area index as an input variable. The 1997 ASAE Annual International Meeting Sponsored by ASAE, Minneapolis, Minnesota, August 10-14. ASABE, St. Joseph, MI
18 Chiarello, N. R. and S. L. Gulmon. 1991. Stress effects on plant reproduction. In: Mooney H. A., Winner W. E., Pell E. J., Chu E, Eds. Response of plants to multiple stresses. New York: Academic Press, 161-168
19 Ko, J., S. J. Maas, R. J. Lascano, and D. Wanjura. 2005. Modification of the GRAMI model for cotton. Agron. J. 97: 1374-1379   DOI   ScienceOn
20 Maas, S. J. 1993c. Within-season calibration of modeled wheat growth using remote sensing and field sampling. Agron. J. 85 : 669-672   DOI   ScienceOn
21 Moran, M. S, S. J. Maas, and P. J. Pinter, Jr. 1995. Combining remote sensing and modeling for estimating surface evaporation and biomass production. Remote Sensing Reviews 12 : 335-353   DOI
22 Maas, S. J. and G. F. Arkin. 1978. User's guide to SORGF: A dynamic grain sorghum growth model with feedback capacity. Research Center Program and Model Documentation No. 78-1, Texas Agricultural Experiment Station. College Station, TX
23 Conte, S. D. and D. de Boor. 1965. Elementary numerical analysis: An algorithmic approach. McGraw-Hill, New York
24 Kimball, B. A. and J. R. Mauney. 1993. Response of cotton to varying $CO_{2}$, irrigation, and nitrogen: yield and growth. Agron. J. 85 : 700-706
25 Rosenthal, W. D., R. L. Vanderlip, B. S. Jackson, and G. F. Arkin. 1989. SORKAM: A grain sorghum crop model. Texas Agric. Exp. Stn. Miscellaneous Publication MP-1669
26 Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. 1992. Numerical recipes in Fortran: The art of scientific computing, second edition. Cambridge Univ. Press, New York
27 Rhoads, F. M. and M. E. Bloodworth. 1964. Area measurement of cotton leaves by dry-weight method. Agon. J. 56 : 520-522
28 Bronson, K. F., J. W. Keeling, J. D. Booker, T. T. Chua, T. A. Wheeler, R. K. Boman, and R. J. Lascano. 2003. Influence of landscape position, soil series, and phosphorus fertilizer on cotton lint yield. Agron. J. 95 : 947-957
29 Rajapakse, S. S. 2005. Automated radiometric normalization techniques for multi-temporal Landsat-TM and ETM+ imagery. Dissertation, Texas Tech University
30 Wilkerson, G. G., J. W. Jones, K. J. Boot, and J. W. Mishoe. 1985. SOYGRO V5.0: Soybean crop growth and yield model. Technical Documentation, Univ. Florida, Gainesville, FL
31 Maas, S. J. 1993b. Parameterized model of gramineous crop growth: II. Within-season simulation calibration. Agron. J. 85 : 354-358   DOI
32 Wanjura, D. F., D. R. Upchurch, and S. J. Maas. 2004. Spectral reflectance estimates of cotton biomass and yield. Beltwide Cotton Conference, pp. 838-851
33 Maas, S. J. and P. C. Doraiswamy. 1996. Integration of satellite data and model simulation in a GIS for monitoring regional evaporation and biomass production. Proceedings of 3rd International Conference on Integrating GIS and Environmental Modeling, Santa Fe, NM, Jan. 21-26, 2006, CD-ROM. The National Center for Geographic Information and Analysis, Santa Barbara, CA
34 Orgaz, F., L. Mateos, and E. Fereres. 1992. Season length and cultivar determine the optimum evapotranspiration deficit in cotton. Agron. J. 84 : 700-706   DOI
35 Li, H., R. J. Lascano, E. M. Barnes, J. Booker, L. T. Wilson, K. F. Bronson, and E. Segarra. 2001. Multispectral reflectance of cotton related to plant growth, soil water and texture, and site elevation. Agron. J. 93 : 1327-1337   DOI   ScienceOn
36 Jackson, B. S., G. F. Arkin, and A. B. Hearn. 1990. COTTAM: a cotton plant simulation model for an IBM PC microcomputer. College Station, Texas, The Texas Agricultural Experiment Station, The Texas A&M University System: 241p
37 Charles-Edwards, D. A., D. Doley, and G. M. Rimmington. 1986. Modeling plant and development. Academic Press, Orlando, FL
38 Jackson, B. S., G. F. Arkin, and A. B. Hearn. 1988. The cotton simulation model 'COTTAM': fruiting model calibration and testing. Trans. of the ASAE. 31(3) : 846-854   DOI
39 Ko, J., S. J. Maas, S. Mauget, G. Piccinni, and D. Wanjura. 2006. Modeling water-stressed cotton growth using within-season remote sensing data. Agron. J. 98 : 1600-1609   DOI   ScienceOn