Estimation of the vegetal cover fraction in corn from information obtained with remote sensing
DOI:
https://doi.org/10.24850/j-tyca-14-05-08Keywords:
Vegetation cover fraction (FCV), vegetation indices (IV), Landsat 8, radiometer, CanopeoAbstract
The Fractional vegetation cover (FVC) is a biophysical variable related to biomass, leaf area index and crop coefficient, among others. Currently, with the wide availability of satellite images, it is possible to estimate FVC extensively using vegetation indices (VI). However, it is important to examine the relationship between VCF measured in the field and that estimated with satellite imagery to determine its reliability. The objective of this study was to examine the feasibility of estimating FVC using different VIs (NDVI, SR, SAVI and MSAVI), calculated using radiometric information and Landsat 8 imagery, and to determine the differences that exist when estimating FCV with both sources of information. The radiometric information was collected in six corn plots located in the municipality of Texcoco, State of Mexico. The results showed a good fit of the VI calculated with field information when the FVC was less than 60 %. The correlation between the FVC measured in the field and the indices estimated with satellite imagery had R2 values greater than 0.78, being slightly higher in the case of the NDVIL (R2 = 0.89), a value that suggests an acceptable degree of adjustment. It is concluded that it is feasible to estimate the FCV in a maize crop using spectral images from Landsat 8. The best fit between the field VI and the VI calculated with Landsat 8 data, for the conditions of this study, corresponded to the NDVI.
References
Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L., & Royo, C. (2000). Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92(1), 83-91. DOI: https://doi.org/10.2134/agronj2000.92183x
Bocco, M., Ovando, G., Sayago, S., & Willington, E. (2013). Simple models to estimate soybean and corn percent ground cover with vegetation indices from modis. Revista de Teledetección, 39(39), 83-91.
Campos, I., Neale, C. M. U., López, M. L., Balbontín, C., & Calera, A. (2014). Analyzing the effect of shadow on the relationship between ground cover and vegetation indices by using spectral mixture and radiative transfer models. Journal of Applied Remote Sensing, 8(083562), 1-21. DOI: 10.1117/1.JRS.8.083562
Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62(9), 1025-1035.
Chen, X., Guo, Z., Chen, J., Yang, W., Yao, Y., Zhang, C., Cui, X., & Cao, X. (2019). Replacing the Red Band with the Red-SWIR Band (0.74ρred + 0.26ρswir) can reduce the sensitivity of vegetation indices to soil background. Remote Sensing, 11(7), 851. DOI: https://doi.org/10.3390/rs11070851
Cuesta, A., Montoro, A., Jochum, A. M., López, P., & Calera, A. (2005). Metodología operativa para la obtención del coeficiente de cultivo desde imágenes satelitales. ITEA, 101, 212-224.
De-la-Casa, A. C., Ovando, G. G., Ravelo, A. C., Abril, E. G., & Bergamaschi, H. (2014). Estimating maize ground cover using spectral data from Aqua-MODIS in Córdoba, Argentina. International Journal of Remote Sensing, 35(4), 1295-1308. DOI: 10.1080/01431161.2013.876119
De-la-Casa, A., Ovando, G., Bressanini, L., Martínez, J., Díaz, G., & Miranda, C. (2018). Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 531-547. DOI: https://doi.org/10.1016/j.isprsjprs.2018.10.018
Gilabert, M., González-Piqueras, J., & García-Haro, J. (1997). Acerca de los índices de vegetación. Revista de Teledetección, 8. Recuperado de https://www.researchgate.net/publication/39195330_Acerca_de_los_indices_de_vegetacion/link/00b7d5187635eb5a1a000000/download
Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(295-309). Recuperado de https://doi.org/10.1016/0034-4257(88)90106-X
Jiang, Z., Huete, A. R., Chen, J., Chen, Y., Li, J., Yan, G., & Zhang, X. (2006). Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3), 366-378. DOI: https://doi.org/10.1016/j.rse.2006.01.003
Jin, X., Li, Z., Yang, G., Yang, H., Feng, H., Xu, X., Wang, J., Li, X., & Luo, J. (2017). Winter wheat yield estimation base on multi-source medium resolution optican and radar imaging data and AquaCrop model using the particle swarm optimization algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 24-37. DOI: https://doi.org/10.1016/j.isprsjprs.2017.02.001
Johnson, L. F., & Trout. T. J. (2012). Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California´s San Joaquin Valley. Remote Sensing, 4(2), 439-455. DOI: https://doi.org/10.3390/rs4020439
Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4), 663-666. DOI: https://doi.org/10.2307/1936256
Marcial, M. J., Ojeda, W., González, A., & Jiménez, S. (2017). Estimación de la cobertura vegetal usando imágenes RGB obtenidas desde un dron. III Congreso Nacional de riego y drenaje COMEII 2017, COMEI-17048.
Patrignani, A., & Ochsner, T. E. (2015). Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal, 107(6), 2312-2320. DOI: https://doi.org/10.2134/agronj15.0150
Paz, F. (2018). Estimación de la cobertura aérea de la vegetación herbácea usando sensores remotos. Terra Latinoamericana, 36(3), 239-259. DOI: https://doi.org/10.28940/terra.v36i3.399
Paz, F., Romero, M. E., Palacios, E., Bolaños, M., Valdez, J. R., & Aldrete, A. (2014). Alcances y limitaciones de los índices espectrales de la vegetación: marco teórico. Terra Latinoamericana, 32(3), 177-194. Recuperado de https://www.terralatinoamericana.org.mx/index.php/terra/article/view/22/20
Paz, F., Romero, M., Palacios, E., Bolaños, M., Valdez, J., & Aldrete, A. (2015). Alcances y limitaciones de los índices espectrales de la vegetación: análisis de índices de banda ancha. Terra Latinoamericana, 33, 27-49. Recuperado de http://www.scielo.org.mx/pdf/tl/v33n1/2395-8030-tl-33-01-00027.pdf
Pinty, B., & Verstraete, M. M. (1992). GEMI: A non-linear index to monitor global vegetation from satellites. Vegetation, 101, 15-20. DOI: https://doi.org/10.1007/BF00031911
Purevdorj, T., Tateish, R., Ishiyama, T., & Honda, Y. (1998). Relationships between percent vegetation cover and vegetation indices. International Journal Remote Sensing, 19(18), 3519-3535. DOI: https://doi.org/10.1080/014311698213795
Qi, J., Chehbouni, A., Huete, A., Kerr, Y., & Sorooshian, S. (1994). A Modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119-126. DOI: https://doi.org/10.1016/0034-4257(94)90134-1
Ren, H., Zhou, G., & Zhang, F. (2018). Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. Remote Sensing of Environment, 209, 439-445. DOI: https://doi.org/10.1016/j.rse.2018.02.068
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107. DOI: 10.1016/0034-4257(95)00186-7
Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC, Type III, Final report, Greenbelt, MD. (pp. 1-390). Recuperado de https://ntrs.nasa.gov/api/citations/19750020419/downloads/19750020419.pdf
Schlemmer, M., Gitelson, A., Schepers, J., Ferguson, R., Peng, Y., Shanahan, J., & Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25, 47-54. DOI: https://doi.org/10.1016/j.jag.2013.04.003
Song, W., Mu, X., Ruan, G., Gao, Z., Li, L., & Yan, G. (2017). Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation, 58, 168-176. DOI: https://doi.org/10.1016/j.jag.2017.01.015
Venancio, L. P., Mantovani, E. C., Amaral, C. H., Neale, C. M. U., Gon çalves, I. Z., Filgueiras, R., & Campos, I. (2019). Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management, 225 (105779). DOI: http://doi.org/10.1016/j.agwat.2019.105779
Zhang, Y., Smith, A. M., & Hill, M. J. (2011). Estimating fractional cover of grassland components from two satellite remote sensing sensors. Proceedings of 34th International Symposium on Remote Sensing of Environment (pp. 10-15). Sydney, Australia. Recuperado de https://www.isprs.org/proceedings/2011/isrse-34/211104015Final00252.pdf
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