Monitoring the Cotton Growth Conditions Based on LAI from Remote Sensing and Expert Knowledge

Abstract:

To manage the cotton field effectively, the quantitative classification method for cotton growth conditions would be researched based on LAI from remote sensing inversion in the field scale. The experiment was carried out in 2006-2007 in Xinjiang, China, and the eighteen cotton fields were selected as the standard observation station, the 255 quadrats data about LAI were obtained, and three vegetation indexes, including NDVI, PVI, and EVI, were used to estimate LAI by the correlativity between remote sensing and field measurement. The ideal LAI were proposed by the cotton cultivation experts when the cotton filed would realize the high yield, LAI from 2.8 to 3.2 was applicable at the first flowering stage in the research region. All of R2 on LAI estimation from three vegetation indexes got through the extremely significance test but the two models from PVI and EVI had the more excellent estimation ability when LAI was more than 3.0, and the overall estimation level of EVI is highest, RMSE and REPE is 0.67 and 0.12 respectively, and so LAI was retrieved by EVI in the paper. Through LAI inversed by the remote sensing data, the cotton growth conditions was quantitatively translated into three types of small population, classic population, and big population with LAI expert knowledge. The method classifying the cotton growth conditions was demonstrated in the research region in 2006 and 2007. The research results would contribute to the precision management on the cotton filed.

• Periodical: Advanced Materials Research (Volumes 317-319)
• Pages: 1050-1057
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.317-319.1050
• Online since: August 2011
• Authors: Jun Hua Bai, Jing Li, Shao Kun Li
• Keywords: Cotton, Population Growth, Quantitative Classification, Remote Sensing

References

[1] Basso, B., Ritchie, J. T., Piece, F. J., Braga, R. P., Jones, J. W., 2001. Spatial validation of crop models for precision agriculture. Agricultural Systems, 68, 97-112.

DOI: 10.1016/s0308-521x(00)00063-9

[2] Bréda N.J.J., 2008. Leaf Area Index. Encyclopedia of Ecology, 2148-2154.

DOI: 10.1016/b978-008045405-4.00849-1

[3] Boegh, E., Soegaarda, H., Brogeb, N., Hasagerc, C. B., Jensenc ,N. O., Scheldeb, K., Thomsen, A., 2002. Airborne multi-spectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81, 179–193.

DOI: 10.1016/s0034-4257(01)00342-x

[4] Cheng, G. W., Yu, Y., (1999). Study on the light compensation point of cotton Leaf and the most large leaf area index of cotton field. Journal of Xinjiang Agricultural University, 22(1), 9-14. (in Chinese with English abstract)

[5] Cohen, W. B., Maiersperger, T. K., Gower, S. T., Turner, D. P., 2003. An improved strategy for regression of biophysical variables and LANDSAT-ETM+ data. Remote Sensing of Environment, 84, 561–571.

DOI: 10.1016/s0034-4257(02)00173-6

[6] Darvishzadeh R., Skidmore A., Schlerf M., Atzberger C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment, 112(5), 2008, 2592-2604.

DOI: 10.1016/j.rse.2007.12.003

[7] Doraiswamy P. C., Moulin S., Cook P. W., Stern A., 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing, 69(6), 665–674.

DOI: 10.14358/pers.69.6.665

[8] Garrigues, S., Allard, D., Baret, F., Weiss, M., 2006. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Environment, 105, 286–298.

DOI: 10.1016/j.rse.2006.07.013

[9] González-Sanpedro, M.C., Toan, T. L., Moreno, J., Kergoat, L., Rubio, E., 2008. Seasonal variations of leaf area index of agricultural fields retrieved from LANDSAT data. Remote Sensing of Environment, 112, 810-824.

DOI: 10.1016/j.rse.2007.06.018

[10] Hansen, P. M., Schjoerring, J. K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, 542–553.

DOI: 10.1016/s0034-4257(03)00131-7

[11] Hoogenboom G., 2000. Contribution of agro-meteorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology, 103, 137–157.

DOI: 10.1016/s0168-1923(00)00108-8

[12] Houborg R., Anderson M., Daughtry C., 2009. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sensing of Environment, 113, 259–274.

DOI: 10.1016/j.rse.2008.09.014

[13] Jones, D., Barnes, E. M., 2000. Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management. Agricultural Systems, 65, 137-158.

DOI: 10.1016/s0308-521x(00)00026-3

[14] Lawless, C., Semenov, M. A., Jamieson P. D., 2005. A wheat canopy model linking leaf area and phenology. Europ. J. Agriculture, (22), 19–32.

DOI: 10.1016/j.eja.2003.11.004

[15] Li, M. C., Zhang, W. F., Ma, F. Y., Lv X., Jiang, G. Y., Wang, K. R., 1999. Research on photosynthetic physiological basis of cotton super-high-yield in Xinjiang. Journal of Xinjiang Agricultural University, 22(4), 276-282. (in Chinese with English abstract)

[16] Luo, H. H., Zheng, W. F., Zhao, R. H., Han, C. L., 2006. Effects of planting densities on canopy apparent photosynthesis, canopy structure and yield of cotton drip-irrigated under the mulch in Xinjiang. Chinese Journal of Eco-Agriculture, 4(4), 113-114. (in Chinese with English abstract)

[17] Lv, X., Zhang, W., Cao, L. P., 2005. Effect of different density on cotton canopy structure, photosynthesis and yield formation in high-yield cotton of Xinjiang. Acta Agriculture Boreali-occidentalis Sinica, 14(1), 142-148. (in Chinese with English abstract)

[18] Ma, F. Y., Li, J. H., Li, M. S., Yang, J. R., 1999. Study on increased yield mechanism under mulch drip irrigation and main coordinated Techniques. Journal of Xinjiang Agricultural University, 22(1), 63-68. (in Chinese with English abstract)

[19] Maire G. L., Marsden C., Verhoef W., Ponzoni F. J., Seen D. L., Bégué A., Stape J. L., Nouvellon Y, 2011. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations. Remote Sensing of Environment, 115, 586–599.

DOI: 10.1016/j.rse.2010.10.004

[20] Moran M.S., Inoue Y., Barnes E.M., 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319-346.

DOI: 10.1016/s0034-4257(97)00045-x

[21] Myneni, R. B., 2003. Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests. Remote Sensing of Environment, 85, 410–423.

DOI: 10.1016/s0034-4257(03)00017-8

[22] Nagler, P. L., Glenn, E. P., Thompson, T. L., Huete A., 2004. Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agricultural and Forest Meteorology, 125, 1–17.

DOI: 10.1016/j.agrformet.2004.03.008

[23] Richard, E. P., Thomas, A. K., Lowell, J. Z., Daniel, S. M., 1998. A qualitative simulation model for cotton growth and development. Computers and Electronics in Agriculture, 20, 165–183.

[24] Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., Briggs, J. M., 1999. Relationships between leaf Area index and LANDSAT-TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70, 52–68.

DOI: 10.1016/s0034-4257(99)00057-7

[25] Yu, Y., Chen, G. W., Lin, H., Wang, B., 2001. Research on the change of leaf area index in north Xinjiang. Cotton Science, 13(5), 300-303. (in Chinese with English abstract)

[26] Zhang, W. F., Wang, Z. L., Yu, S. L., Li, S. K., Cao, L. P., Wang D. W., 2002. Effect of nitrogen on canopy Transactions of the photosynthesis and yield formation in high-yielding cotton of Xinjiang. Acta Agronomica Sinica, 28(6), 789-796. (in Chinese with English abstract)

[27] Zhou, Y. G., Zhang, J. S., Zhu, D. Y., 1997. Study on community figuration and nutrient physiology in high-yield plot of cotton. .Journal of Xinjiang Agricultural University, 20(1), 12-14. (in Chinese with English abstract)

[28] Zhang, W. F., Gou, L., Du, L., Li, M. C., 2000. Cotton colony (1800kg·hm-2) of north Xinjiang. Cotton Science, 12(1), 27-31. (in Chinese with English abstract)

[29] Zheng, Z., Ma, F. Y., Li, L. H., Wu, Z. Y., Wang F., 2002. The diagnostic index and optimized regulating route of high-yield population quality on mulch drip irrigation cotton. Cotton Science, 14(5), 300-304. (in Chinese with English abstract)

[30] Zheng, Z., Ma, F. Y., Mu, Z. X., Li, J. H., 2000. Study of coupling effects and water-fertilizer model on mulched cotton by drip irrigation. Cotton Science, 12(4), 198-201. (in Chinese with English abstract)

[31] Zhou, Y. Q., Zhang, J. S., Chen, B., 1999. Study on population combination of high yield of cotton. Journal of Xinjiang Agricultural University, 22(4), 276-282. (in Chinese with English abstract)