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Empirical Modelling for Retrieval of Foliar Traits in Cotton Crop using Spatial Data


Affiliations
1 Ecophysiology and RS-GIS Lab, Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara - 390 002, India
 

The present study conducted in cotton fields of Vadodara district, Gujarat, India during kharif season of 2009-10, aimed at assessing foliar traits, in particular crop leaf area index (LAI) and chlorophyll content (CC) from space-borne optical LANDSAT 5 TM and IRS LISS-IV satellite data. Field measurements of these foliar traits coinciding with the dates of the satellite data for cotton were used for validation of RSbased VI-LAI and VI-CC empirical models developed in the present study. These models developed for LAI estimation in cotton crop showed good correlation with R2 varying from 0.592 to 0.805, and CC between 0.585 and 0.746 with P at 0.01 level in both cases. It has been observed that the potential of NDVI-LAI and NDVI-CC empirical models was better compared to RVI-LAI and RVI-CC models. The VI-LAI and VI-CC models derived from LISS-IV data were better estimators of LAI compared to LANDSAT. A high R2 value between ground-measured foliar traits and those predicted using empirical models complemented the validation.

Keywords

Cotton Crop, Empirical Models, Foliar Trait, Spatial Data.
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  • Asner, G. P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing Environ., 1998, 64, 234- 253.

  • Houborg, R., Soegaard, H. and Boegh, E., Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sensing Environ., 2007, 106(1), 39-58.

  • Watson, D. J., Comparative physiological studies in the growth of field crops. I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann. Bot., 1947, 11, 41-76.

  • Bonan, G., Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing Environ., 1993, 43(3), 303-314.

  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S. and Briggs, J. M., Relationships between leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing Environ., 1999, 70, 52-68.

  • Buermann, W., Dong, J., Zeng, X., Myneni, R. B. and Dickinson, R. E., Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Climate, 2001, 14, 3536-3550.

  • Dash, J., Curran, P. J. and Foody, G. M., Remote sensing of terrestrial chlorophyll content. In Global Climatology and Ecodynamics: Anthropogenic Changes to Planet Earth, Springer, Berlin, Heidelberg, 2009, pp. 77-105.

  • Singh, D. B., Malhi, R. K. M. and Kiran, G. S., Assessing the impact of agronomic spacing conditions on biophysical and biochemical parameters along with yield and yield components in cotton. IJAAR, 2015, 6(1), 36-44.

  • Blackburn, G. A., Hyperspectral remote sensing of plant pigments. J. Exp. Bot., 2007, 58(4), 855-867.

  • Collins, W., Remote sensing of crop type and maturity. Photogramm. Eng. Remote Sensing, 1978, 44(1), 43-55.

  • Milton, N. M. and Mouat, D. A., Remote sensing of vegetation responses to natural and cultural environment conditions. Photogramm. Eng. Remote Sensing, 1989, 55, 1167-1173.

  • Curran, P. J., Dungan, J. L. and Gholz, H. L., Exploring the relationship between reflectance red edge and chlorophyll content in splash pine. Tree Physiol., 1990, 7, 33-48.

  • Filella, I., Serrano, L., Serra, J. and Penuelas, J., Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci., 1995, 35(5), 1400-1405.

  • Everitt, J. H., Richardson, A. J. and Gausman, H. W., Leaf reflectance- chlorophyll relations in buffelgrass. Photogramm. Eng. Remote Sensing, 1985, 51, 436-466.

  • Muñoz-Huerta, R., Guevara-Gonzalez, R., Contreras-Medina, L., Torres-Pacheco, I., Prado-Olivarez, J. and Ocampo-Velazquez, R., A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors, 2013, 13(8), 10823-10843.

  • Moran, J. A., Mitchell, A. K., Goodmanson, G. and Stockburger, K. A., Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiol., 2000, 20(16), 1113-1120.

  • Martinez, B., Cassiraga, E., Camacho, F. and Garcia-Haro, J., Geostatistics for mapping leaf area index over a cropland landscape: efficiency sampling assessment. Remote Sensing, 2010, 2, 2584-2606.

  • Pandya, M. R., Singh, R. P., Chaudhari, K. N., Bairagi, G. D., Sharma, R., Dadhwal, V. K. and Parihar, J. S., Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over Central India. IEEE Trans. Geosci. Remote Sensing, 2006, 44(7), 1858-1965.

  • Goel, N. S. and Strebel, D. E., Inversion of vegetation canopy reflectance models for estimating agronomic variables I: problem definition and initial results using the Suits' model. Remote Sensing Environ., 1983, 13(6), 487-507.

  • Goel, N. S., Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Rev., 1988, 4(1), 1-212.

  • Privette, J. L., Myneni, R. B., Tucker, C. J. and Emery, W. J., Invertibility of a 1-D discrete ordinates canopy reflectance model. Remote Sensing Environ., 1994, 48(1), 89-105.

  • Myneni, R. B., Nemani, R. R. and Running, S. W., Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Remote Sensing, 1997, 35(6), 1380-1393.

  • Jacquemoud, S., Bacour, H., Poilve, H. and Frangi, J. P., Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sensing Environ., 2000, 74, 417-481.

  • Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjon, A. and Aguera, J., Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing Environ., 2004, 90, 463-476.

  • Schaepman, M. E., Koetz, B., Schaepman-Strub, G. and Itten, K. I., Spectrodirectional remote sensing for the improved estimation of biophysical and chemical variables: two case studies. Int. J. Appl. Earth Obs. Geoinf., 2005, 6(3-4), 271-282.

  • Houborg, R. and Boegh, E., Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data. Remote Sensing Environ., 2008, 112(1), 186-202.

  • Yao, Y., Liu, Q., Liu, Q. and Li, X., LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Remote Sensing Environ., 2008, 112(1), 94-106.

  • Asrar, G., Fuchs, M., Kanemasu, E. T. and Hatfield, J. L., Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J., 1984, 76, 300-306.

  • Curran, P. J. and Williamson, H. D., Airborne MSS data to estimate GLAI. Int. J. Remote Sensing, 1987, 8, 57-74.

  • Chen, J. M. and Cihlar, J., Retrieving leaf area index of boreal conifer forests using landsat TM images. Remote Sensing Environ., 1996, 55, 153-162.

  • Franklin, S. E., Lavigne, M. B., Deuling, M. J., Wulder, M. A. and Hunt, E. R., Estimation of forest leaf area index using remote sensing and GIS data for modeling net primary production. Int. J. Remote Sensing, 1997, 18, 3459-3471.

  • Kuusk, A., Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model. Int. J. Remote Sensing, 1998, 19(15), 2893-2905.

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., Brown de Colstoun, E. and McMurtrey III, G. E., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74(2), 229-239.

  • le Maire, G., Francois, C. and Dufrene, E., Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing Environ., 2004, 89(1), 1-28.

  • Xavier, A. C. and Vettorazzi, C. A., Mapping leaf area index through spectral vegetation indices in a subtropical watershed. Int. J. Remote Sensing, 2004, 25(9), 1661-1672.

  • Wang, F., Huang, J, Tang, Y. and Wang, X., New vegetation index and its application in estimating leaf area index of rice. Rice Sci., 2007, 143, 195-203.

  • Patil, S. S., Patil, V. C., Patil, B. N. and Patil, P. L., Simple yield prediction models to estimate wheat production. In The Third National Conference on Agro-Informatics and Precision Agriculture 2012 (AIPA 2012), IIT Hyderabad, Hyderabad, 1-3 August 2012, pp. 162-166.

  • Welles, J. M. and Norman, J. M., Instrument for indirect measurement of canopy architecture. Agron. J., 1991, 83, 818-825.

  • Arnon, D. I., Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol., 1949, 24(1), 1-15.

  • Wu, C., Niu, Z., Tang, Q. and Huang, W., Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric. For. Meteorol., 2008, 148(8-9), 1230-1241.

  • Pax-Lenney, M. and Woodcock, C. E., Monitoring agricultural lands in Egypt with multitemporal landsat TM imagery: how many images are needed? Remote Sensing Environ., 1997, 59(3), 522- 529.

  • Heller, E., Rhemtulla, J. M., Lele, S., Kalacska, M., Badiger, S., Sengupta, R. and Ramankutty, N., Mapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of southern India using multi-temporal medium-resolution imagery: implications for assessing water use in agriculture. Photogramme. Eng. Remote Sensing, 2012, 78(8), 815-827.

  • Dong, J., Nai-Bin, W., Xiao-Huan, Y. and Ji-Hua, W., Study on the interaction between NDVI profile and the growing status of crops. Chin. Geogr. Sci., 2003, 13(1), 62-65.

  • Ji-Hua, M. and Bing-Fang, W., Study on the crop condition monitoring methods with remote sensing. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2008, XXXVII(B8), 945-950.

  • Meng, J., Du, X. and Wu, B., Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation.Int. J. Digit. Earth, 2013, 6(3), 203-218.

  • Mohan, S., Das, A., Maity, S., Mehta, R. L. and Haldar, D., ALOS RA: evaluation of polarimetric, interferometric and differential interferometric techniques for vegetation and land subsidence study. Final Project Report, ISRO, 2011, pp. 1-32.

  • Nayak, S. S., Thermal imagery and spectral reflectance based system to monitor crop condition. M Sc thesis, Texas Tech University, USA, 2005, pp. 1-107.

  • Ludeke, M., Janecek, A. and Kohlmaier, G. H., Modelling the seasonal CO2 uptake by land vegetation using the global vegetation index. Tellus B, 1991, 43(2), 188-196.

  • Gitelson, A. A., Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol., 2004, 161(2), 165-173.

  • Gonzalez-Dugo, M. and Mateos, L., Spectral vegetation indices for estimating cotton and sugarbeet evapotranspiration. Earth observation for vegetation monitoring and water management. In AIP Conference Proceedings, 2006, vol. 852, pp. 115-123.

  • Le Maire, G., Francois, C., Soudani, K., Davi, H., Dantec, V. L., Saugier, B. and Dufrene, E., Forest leaf area index determination: a multiyear satellite-independent method based on within-stand normalized difference vegetation index spatial variability . J. Geophys. Res., 2006, 111(G2), G02027.

  • Ferrara, R. M., Fiorentino, C., Martinelli, N., Garofalo, P. and Rana, G., Comparison of different ground-based NDVI measurement methodologies to evaluate crop biophysical properties. Ital. J. Agron., 2010, 5(2), 145-154.

  • Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V. and Bonfi, J. D., Assessment of leaf area index by the red-edge inflection point derived from VENμS bands. In Proceedings of the ESA Hyperspectral Workshop, Frascati, Italy, 2010, vol. 683, pp. 1-7.

  • Zhao, J., Lia, J., Liua, Q. and Yang, L., A preliminary study on mechanism of LAI inversion saturation. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2012, XXXIX(B1), 77-81.

  • Srinivas, P., Das, B. K., Saibaba, J. and Krishnan, R., Application of distance based vegetation index for agricultural crops discrimination. In XXth ISPRS Congress, Technical Commission VII, Istanbul, Turkey, 2004, pp. 1-6.

  • Chaurasia, S. et al., Development of regional wheat VI-LAI models using resourcesat-1 AWiFS data. J. Earth Syst. Sci., 2011, 120(6), 1113-1125.

  • Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite-1 Symposium (eds Freden, S. C., Mercanti, E. P. and Becker, M.), Technical Presentations, NASA SP-351, NASA, Washington, D.C., 1974, vol. 1, pp. 309- 317.

  • Pearson, R. L. and Miller, L. D., Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In Proceedings of the 8th International Symposium on Remote Sensing of the Environment II, Pawnee National Grasslands, Colorado, 1972, pp. 1355-1379.


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  • Empirical Modelling for Retrieval of Foliar Traits in Cotton Crop using Spatial Data

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Authors

Ramandeep Kaur M. Malhi
Ecophysiology and RS-GIS Lab, Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara - 390 002, India
G. Sandhya Kiran
Ecophysiology and RS-GIS Lab, Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara - 390 002, India

Abstract


The present study conducted in cotton fields of Vadodara district, Gujarat, India during kharif season of 2009-10, aimed at assessing foliar traits, in particular crop leaf area index (LAI) and chlorophyll content (CC) from space-borne optical LANDSAT 5 TM and IRS LISS-IV satellite data. Field measurements of these foliar traits coinciding with the dates of the satellite data for cotton were used for validation of RSbased VI-LAI and VI-CC empirical models developed in the present study. These models developed for LAI estimation in cotton crop showed good correlation with R2 varying from 0.592 to 0.805, and CC between 0.585 and 0.746 with P at 0.01 level in both cases. It has been observed that the potential of NDVI-LAI and NDVI-CC empirical models was better compared to RVI-LAI and RVI-CC models. The VI-LAI and VI-CC models derived from LISS-IV data were better estimators of LAI compared to LANDSAT. A high R2 value between ground-measured foliar traits and those predicted using empirical models complemented the validation.

Keywords


Cotton Crop, Empirical Models, Foliar Trait, Spatial Data.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi12%2F2089-2096