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Evaluation of wheat canopy cover using NDVI in large areas of Iran | ||
| Iran Agricultural Research | ||
| مقاله 1، دوره 41، شماره 1، 2022، صفحه 1-8 اصل مقاله (1.24 M) | ||
| نوع مقاله: Note | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2022.41206.1449 | ||
| نویسندگان | ||
| M. H Jorenoosh1؛ S Boromand Nasab* 2؛ S Taghvaeian* 3؛ M Pakparvar* 1؛ A Sherbafi* 4 | ||
| 1Fars Agricultural and Natural Resources Research and Education Center, Shiraz, I. R. Iran | ||
| 2Department of Irrigation and Drainage, Shahid Chamran University of Ahvaz, Ahwaz, I. R. Iran | ||
| 3Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK, USA | ||
| 4Azad University of Arsanjan, Arsanjan, I. R. Iran | ||
| چکیده | ||
| To studies a suggested method for estimating fractional green canopy cover (FGCC or CC) from normalized difference vegetation index (NDVI) in Iran, a research project was carried out on four wheat farms in the large-scale region, in Fars and Khuzestan provinces during four growing seasons from 2015 to 2019. Two different image classification methods were used to provide the CCs of farms. In both methods, R2s were greater than 0.95 and NRMSEs were less than 0.15. A simple regression equation was constructed between CC and NDVI of 64 Landsat 8-Oli images with high accuracy from 2015 to 2018. The R2s of CC-NDVI equations were 0.84, 0.85, 0.88, 0.80 and 0.76 in Dezful, Omidieh, Darab, Zarghan and all sites together. The simple regression equation was used to simulate CC in 2018-2019 for validating the equation and had a good agreement with the measurements, especially in using the general equation of all sites. The p-values of the four equations were less than 0.03. As a suggested method, the CC-NDVI equation can be used to predict CC from NDVI as a low-cost and less time-consuming method in the study area to use in crop growth studies, crop growth models and crop growth management. | ||
| کلیدواژهها | ||
| Canopy cover؛ Crop growth؛ Landsat 8-OLI؛ NDVI | ||
| مراجع | ||
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Abdolalizadeh, Z., Ghorbani, A., Mostafazadeh, R., & Moameri, M. (2020). Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran. Arabian Journal of Geosciences, 13, 245-258. Ahmadi, K., Gholizadeh, H., Ebadzadeh, H., Hoseinpur, R., Abdeshah, H., Kazemian, A., & Rafiee, M. (2017). Iran agriculture statistics. Ministry of agriculture, IT Center. Retrieved from: http://amar.maj.ir (in Persian). Allen, R. G., & Pereira, L. S. (2009). Estimating crop coefficients from fraction of ground cover and height. Irrigation Science, 28(1), 17-34. Barnes, E. M., Clarke, T. R., Richard, S. E., Colaizzi, P. D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., & Lascano, R. J. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. The Fifth International Conference on Precision Agriculture Bloomington, MN, USA, 2000. Vol. 1619 Calera, A., Martínez, C., & Melia, J. (2001). A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. International Journal of Remote Sensing, 22(17), 3357-3362. Casa, A., Ovandoa, G., Bressaninib, L., Martínezc, J., Díaza, G., Mirandad, C. (2018). Soybean crop cover age 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. Chianucci, F., Lucibelli, A., & DellAbate, M. T. (2018). Estimation of ground CC in agricultural crops using downward-looking photography. Biosystems Engineering, 169, 209-216. Er-Raki, S., Chehbouni, A., Guemouria, N., Duchemin, B., Ezzahar, J., & Hadria, R. (2007). Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agricultural Water Management, 87(1), 41-54. Goodwin, A. W., Lindsey, L. E., Harrison, S. K., Paul, P. A. (2018). Estimating wheat yield with normalized difference vegetation index and fractional green canopy cover. Crop, Forage & Turfgrass Management, 4(1), 1-6. Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19, 1533-1543. Huete, A. R, Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, G. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Environment, 83, 195-213. Johnson, L. F., & Trout, T. J. (2012). Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin Valley. Remote Sensing, 4, 439-455. Larson, H. (2007). Linear regressions for CC estimation in Acacia woodlands using Landsat-TM, -MSS and SPOT HRV XS data. International Journal of Remote Sensing, 14, 2129-2136. López-Urrea, R., Montoro, A., Gonzalez-Piqueras, J., Lopez-Fuster, P., & Fereres, E. (2009). Water use of spring wheat to raise water productivity. Agricultural Water Management, 96(9), 1305-1310. Masek, J. G., Vermot, E. F., Saleous, N., Wolfe, R., Hall, F. G., Huemmrich, F., Gao, F., Kutler, J., & Lim, T. K. (2012). LEDAPS Landsat calibration, reflectance, atmospheric correction preprocessing code. Model product. Retrieved from: http://daac.ornl.gov from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA http://dx. doi.org/10.3334/ORNLDAAC/1080. Patrignani, A., & Ochsner, T. E. (2015). Canopeo: A powerful new tool for measuring fractional green CC. Agronomy Journal, 107, 2312-2320. Prabhakara, K., Hivelyb, W. D., McCartyc, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation,39, 88-102. Steduto, P., Hsiao, T. C., Raes, D., & Fereres, E. (2009). AquaCrop-the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101,426-437. Taghvaeian, S., Chávez, J. L., Hattendorf, M. J., & Crookston, M. A. (2013). Optical and thermal remote sensing of turfgrass quality, water stress, and water use under different soil and irrigation treatments. Remote Sensing, 5, 2327-2347. Tenreiro, T., R., García-Vilab, M., G´omeza, J. A., Jim´enez-Bernia, J. A., Fereresa, E. (2021). Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Computer and Electronics in Agriculture, 182, 106038. Trout, T. J., Johnson, L. F., & Gartung, J. (2008). Remote sensing of canopy cover in horticultural crops. Horticultural Science, 43(2), 333-337. White, M. A., Asner, R. G., Nemani, P. R., Rivette, J. L., & Running, S. W. (2000). Measuring fractional cover and leaf area index in arid ecosystem: Digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment, 74(l), 45-57. Zhang, X., Liano, C., Li, J., & Sun, Q. (2013). Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1satellite hyperspectral data. International Journal of Applied Earth Observation and Geology, 21, 506-512. | ||
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