Cotton yield estimation using several vegetation indices

Year 2024, Volume: 8 Issue: 1, 139 - 151, 19.01.2024

Bakhtiyar Babashli , Aytaj Badalova , Ramis Shukurov Agil Ahmadov

https://doi.org/10.31127/tuje.1346353

Abstract

Accurate yield estimation before harvest is important for farmers and researchers to optimize field management and increase productivity. The purpose of this study is to develop efficient cotton plant productivity using field studies and satellite imagery. Nitrogen (N) fertilizer is an important nutrient in plant development, and when suboptimal amounts are applied, it can cause yield reductions. Different vegetation indices were employed to analyze the dynamics and yield of cotton plants, with a primary focus on the Red, Near-Infrared (NIR), and Red Edge bands derived from satellite imagery. The objective was to assess the nitrogen content in the plants. The present study involved a comparative analysis of various vegetation indicators in relation to cotton plant production. The productivity of the cotton plant was assessed by employing the indices that exhibited the most influence. The analysis revealed that the MCARI index exhibited the worst weaknesses, while the CLRE index demonstrated the main performance. The productivity of each index was computed, and it was observed that the CLRE index exhibited the closest proximity to the average productivity of 34.48 cents per hectare (cent/ha). Similar results have been observed in other indices. The MCARI index exhibits a distinct value of 32.08 in comparison to the others indices. The results of this study illustrate the potential of satellite imaging in monitoring cotton yield, hence offering valuable theoretical and technological assistance for estimating cotton production in agricultural areas.

Keywords

Cotton, Yield, Vegetation indices, Biomass, Satellite images

References

• Giller, K. E., Delaune, T., Silva, J. V., Descheemaeker, K., van de Ven, G., Schut, A. G., ... & van Ittersum, M. K. (2021). The future of farming: Who will produce our food?. Food Security, 13(5), 1073-1099. https://doi.org/10.1007/s12571-021-01184-6
• Kim, H. J., & Triplett, B. A. (2001). Cotton fiber growth in planta and in vitro. Models for plant cell elongation and cell wall biogenesis. Plant physiology, 127(4), 1361-1366. https://doi.org/10.1104/pp.010724
• Ashraf, K. H., & Hanif, M. (2010). Screening of cotton germplasm against cotton leaf curl virus. Pakistan Journal of Botany, 42(5), 3327-3342.
• Ridley, W., & Devadoss, S. (2023). Competition and trade policy in the world cotton market: Implications for US cotton exports. American Journal of Agricultural Economics, 105, 1365-1387. https://doi.org/10.1111/ajae.12370
• Aytaç, S., Başbağ, S., Arslanoğlu, F., Ekinci, R., & Ayan, A. K. (2020). Lif bitkileri üretiminde mevcut durum ve gelecek. Türkiye Ziraat Mühendisliği IX. Teknik Kongresi Bildiriler Kitabı-1, 463-491.
• He, L., & Mostovoy, G. (2019). Cotton yield estimate using Sentinel-2 data and an ecosystem model over the southern US. Remote Sensing, 11(17), 2000. https://doi.org/10.3390/rs11172000
• Tariq, A., Siddiqui, S., Sharifi, A., & Shah, S. H. I. A. (2022). Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan. Arabian Journal of Geosciences, 15(11), 1045. https://doi.org/10.1007/s12517-022-10238-8
• Corwin, D. L., Lesch, S. M., Shouse, P. J., Soppe, R., & Ayars, J. E. (2003). Identifying soil properties that influence cotton yield using soil sampling directed by apparent soil electrical conductivity. Agronomy Journal, 95(2), 352-364. https://doi.org/10.2134/agronj2003.3520
• Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132. https://doi.org/10.1016/j.isprsjprs.2017.01.001
• Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7(1), 36. https://doi.org/10.3390/pr7010036
• Zhang, D., & Zhou, G. (2016). Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors, 16(8), 1308. https://doi.org/10.3390/s16081308
• Ahmad, T., Sud, U. C., Rai, A., & Sahoo, P. M. (2020). An Alternative Sampling Methodology for Estimation of Cotton Yield using Double Sampling Approach. Journal of the Indian Society of Agricultural Statistics, 74(3), 217–226.
• Shi, G., Du, X., Du, M., Li, Q., Tian, X., Ren, Y., ... & Wang, H. (2022). Cotton Yield Estimation Using the Remotely Sensed Cotton Boll Index from UAV Images. Drones, 6(9), 254. https://doi.org/10.3390/drones6090254
• Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., & Tong, Q. (2023). Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science, 13, 1048479. https://doi.org/10.3389/fpls.2022.1048479
• Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65. https://doi.org/10.1016/j.compag.2015.11.018
• Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K., & Huang, W. (2019). Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture, 165, 104943. https://doi.org/10.1016/j.compag.2019.104943
• Hou, P., Liu, Y., Liu, W., Liu, G., Xie, R., Wang, K., ... & Li, S. (2020). How to increase maize production without extra nitrogen input. Resources, Conservation and Recycling, 160, 104913. https://doi.org/10.1016/j.resconrec.2020.104913
• Ekinci, M., Atamanalp, M., Turan, M., Alak, G., Kul, R., Kitir, N., & Yildirim, E. (2019). Integrated use of nitrogen fertilizer and fish manure: Effects on the growth and chemical composition of spinach. Communications in Soil Science and Plant Analysis, 50(13), 1580-1590. https://doi.org/10.1080/00103624.2019.1631324
• Guo, Z., Luo, C., Dong, Y., Dong, K., Zhu, J., & Ma, L. (2021). Effect of nitrogen regulation on the epidemic characteristics of intercropping faba bean rust disease primarily depends on the canopy microclimate and nitrogen nutrition. Field Crops Research, 274, 108339. https://doi.org/10.1016/j.fcr.2021.108339
• Dhivya, R., Amalabalu, P., Pushpa, R., & Kavithamani, D. (2014). Variability, heritability and genetic advance in upland cotton (Gossypium hirsutum L.). African Journal of Plant Science, 8(1), 1-5. https://doi.org/10.5897/AJPS2013.1099
• Onoda, Y., Wright, I. J., Evans, J. R., Hikosaka, K., Kitajima, K., Niinemets, Ü., ... & Westoby, M. (2017). Physiological and structural tradeoffs underlying the leaf economics spectrum. New Phytologist, 214(4), 1447-1463. https://doi.org/10.1111/nph.14496
• Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., & Garnier, J. (2014). 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters, 9(10), 105011. https://doi.org/10.1088/1748-9326/9/10/105011
• Singh, R. J., & Ahlawat, I. P. S. (2012). Dry matter, nitrogen, phosphorous, and potassium partitioning, accumulation, and use efficiency in transgenic cotton-based cropping systems. Communications in Soil Science and Plant Analysis, 43(20), 2633-2650. https://doi.org/10.1080/00103624.2012.716125
• Alganci, U., Ozdogan, M., Sertel, E., & Ormeci, C. (2014). Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs. Field Crops Research, 157, 8-19. https://doi.org/10.1016/j.fcr.2013.12.006
• Liu, Q. S., Li, X. Y., Liu, G. H., Huang, C., & Guo, Y. S. (2016). Cotton area and yield estimation at Zhanhua County of China using HJ-1 EVI time series. In ITM Web of Conferences, 7, 09001. https://doi.org/10.1051/itmconf/20160709001
• Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., ... & Chen, S. (2022). Prediction of field-scale wheat yield using machine learning method and multi-spectral UAV data. Remote Sensing, 14(6), 1474. https://doi.org/10.3390/rs14061474
• Leroux, L., Castets, M., Baron, C., Escorihuela, M. J., Bégué, A., & Seen, D. L. (2019). Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy, 108, 11-26. https://doi.org/10.1016/j.eja.2019.04.007
• Elders, A., Carroll, M. L., Neigh, C. S., D'Agostino, A. L., Ksoll, C., Wooten, M. R., & Brown, M. E. (2022). Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2. Remote Sensing Applications: Society and Environment, 27, 100820. https://doi.org/10.1016/j.rsase.2022.100820
• Azərbaycan Respublikası Beyləqan Rayon İcra Hakimiyyəti (2023). Coğrafi mövqeyi. http://www.beyleqan-ih.gov.az/az/page/13.html
• State Statistical Committee of the Republic of Azerbaijan (2022). Main economic indicators of agricultural enterprises and private owner farms.
• Simarmata, N., Nadzir, Z. A., & Agustina, L. K. (2022). Application of Spot6/7 Satellite Imagery for Rice Field Mapping Based on Transformative Vegetation Indices. Jurnal Geografi, 14(1), 69. https://doi.org/10.24114/jg.v14i1.29036
• Ismatova, K. H. R., Badalova, A. N., Ismailov, A. I., Aliyev, Z. H., & Talibova, S. S. (2019). Features of the Use of Aerospace Methods in Soil Science. Journal of Medical Care Research and Review, 2(5), 149-154.
• De Wit, A. J. W., & Clevers, J. G. P. W. (2004). Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 25(20), 4091-4112. https://doi.org/10.1080/01431160310001619580
• Beriaux, E., Jago, A., Lucau-Danila, C., Planchon, V., & Defourny, P. (2021). Sentinel-1 time series for crop identification in the framework of the future CAP monitoring. Remote Sensing, 13(14), 2785. https://doi.org/10.3390/rs13142785
• Fu, H., Zhao, H., Song, R., Yang, Y., Li, Z., & Zhang, S. (2022). Cotton aphid infestation monitoring using Sentinel-2 MSI imagery coupled with derivative of ratio spectroscopy and random forest algorithm. Frontiers in Plant Science, 13, 1029529. https://doi.org/10.3389/fpls.2022.1029529
• Hümbətov, H. S., X. Q. Xəlilov, X. Q. (2012). Pambiq lifinin texnologiyasi.
• Zheng, B., Myint, S. W., Thenkabail, P. S., & Aggarwal, R. M. (2015). A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation, 34, 103-112. https://doi.org/10.1016/j.jag.2014.07.002
• Barriere, V., & Claverie, M. (2022). Multimodal crop type classification fusing multi-spectral satellite time series with farmers crop rotations and local crop distribution. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.2208.10838
• Fridgen, J. L., & Varco, J. J. (2004). Dependency of cotton leaf nitrogen, chlorophyll, and reflectance on nitrogen and potassium availability. Agronomy Journal, 96(1), 63-69. https://doi.org/10.2134/agronj2004.6300
• Ravandi, S. H., & Valizadeh, M. (2011). Properties of fibers and fabrics that contribute to human comfort. Improving Comfort in Clothing, 61-78. Woodhead Publishing. https://doi.org/10.1533/9780857090645.1.61
• Roznik, M., Boyd, M., & Porth, L. (2022). Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks. Remote Sensing Applications: Society and Environment, 25, 100693. https://doi.org/10.1016/j.rsase.2022.100693
• Ramoelo, A., Cho, M. A., Mathieu, R., Madonsela, S., Van De Kerchove, R., Kaszta, Z., & Wolff, E. (2015). Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation, 43, 43-54. https://doi.org/10.1016/j.jag.2014.12.010
• Pettorelli, N. (2013). The normalized difference vegetation index. Oxford University Press, USA.
• Leo, S., Migliorati, M. D. A., Nguyen, T. H., & Grace, P. R. (2023). Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates. Agricultural Systems, 205, 103559. https://doi.org/10.1016/j.agsy.2022.103559
• Li, Z., Menefee, D., Yang, X., Cui, S., & Rajan, N. (2022). Simulating productivity of dryland cotton using APSIM, climate scenario analysis, and remote sensing. Agricultural and Forest Meteorology, 325, 109148. https://doi.org/10.1016/j.agrformet.2022.109148
• Mumtaz, F., Tao, Y., de Leeuw, G., Zhao, L., Fan, C., Elnashar, A., ... & Wang, D. (2020). Modeling spatio-temporal land transformation and its associated impacts on land surface temperature (LST). Remote Sensing, 12(18), 2987. https://doi.org/10.3390/rs12182987
• NASA (2000). Normalized Difference Vegetation Index (NDVI). https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php
• Dalezios, N. R., Domenikiotis, C., Loukas, A., Tzortzios, S. T., & Kalaitzidis, C. (2001). Cotton yield estimation based on NOAA/AVHRR produced NDVI. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(3), 247-251. https://doi.org/10.1016/S1464-1909(00)00247-1
• Vincini, M., Frazzi, E., D’alessio, P., & Stafford, J. V. (2007). Comparison of narrow-band and broad-band vegetation indexes for canopy chlorophyll density estimation in sugar beet. Precision agriculture, 7, 189-196. https://doi.org/10.3920/978-90-8686-603-8
• Daughtry, C. S., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote sensing of Environment, 74(2), 229-239. https://doi.org/10.1016/S0034-4257(00)00113-9
• Elfanah, A. M., Darwish, M. A., Selim, A. I., Shabana, M. M., Elmoselhy, O. M., Khedr, R. A., ... & Abdelhamid, M. T. (2023). Spectral reflectance indices’ performance to identify seawater salinity tolerance in bread wheat genotypes using genotype by yield* trait biplot approach. Agronomy, 13(2), 353. https://doi.org/10.3390/agronomy13020353
• Barnes, E. M., Clarke, T. R., Richards, S. E., Colaizzi, P. D., Haberland, J., Kostrzewski, M., ... & Moran, M. S. (2000, July). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the fifth international conference on precision agriculture, Bloomington, MN, USA, 1619, 6.
• Zhang, K., Ge, X., Shen, P., Li, W., Liu, X., Cao, Q., ... & Tian, Y. (2019). Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages. Remote sensing, 11(4), 387. https://doi.org/10.3390/rs11040387
• Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical research letters, 32(8), 1-4. https://doi.org/10.1029/2005GL022688
• Kaya, Y., & Polat, N. (2021). Sulu ve kuru tarım alanlarında buğday verim tahmininde bitki örtüsü indekslerinin kullanımı. Türk Tarım ve Doğa Bilimleri Dergisi, 8(3), 736-746. https://doi.org/10.30910/turkjans.864231
• Sasaki, N., & Chansangiam, P. (2020). Modified Jacobi-gradient iterative method for generalized Sylvester matrix equation. Symmetry, 12(11), 1831. https://doi.org/10.3390/sym12111831
• Azərbaycan Respublikasının Dövlət Statistika Komitəsi (2023). Tarla bitkilərinin əkin sahəsi, məhsul yığımı və məhsuldarlığı.
• Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2007). Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy, 26(3), 335-344. https://doi.org/10.1016/j.eja.2006.12.001
• Sawan, Z. M. (2018). Climatic variables: Evaporation, sunshine, relative humidity, soil and air temperature and its adverse effects on cotton production. Information processing in agriculture, 5(1), 134-148. https://doi.org/10.1016/j.inpa.2017.09.006
• Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47-55. https://doi.org/10.1038/nature14956
• Li, Z., Ding, L., & Xu, D. (2022). Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. Science of The Total Environment, 815, 152880. https://doi.org/10.1016/j.scitotenv.2021.152880
• Iqbal, A., Qiang, D., Zhun, W., Xiangru, W., Huiping, G., Hengheng, Z., ... & Meizhen, S. (2020). Growth and nitrogen metabolism are associated with nitrogen-use efficiency in cotton genotypes. Plant Physiology and Biochemistry, 149, 61-74. https://doi.org/10.1016/j.plaphy.2020.02.002