Possibilities of Using Landsat 8 OLI-Based NDVI for Assessing Citrus Crop Yield in Syria
DOI:
https://doi.org/10.52575/2712-7443-2026-50-1-95-104Keywords:
satellite-based land monitoring, citrus crops, yield forecasting, remote sensing, LandsatAbstract
Citrus crops are the mainstay of agricultural production in a large part of the Mediterranean. The unstable meteorological conditions predetermine a strong variation in the yield of these crops from year to year, which affects the economic situation of the countries. In this regard, their monitoring and yield forecasting are of great importance. In many cases, these activities are not carried out at all or are estimated by survey and statistical methods. The aim of the research was to analyze the possibilities of using multi-year NDVI (Normalized Difference Vegetation Index) time series calculated from Landsat 8 OLI satellite data to predict the yield of citrus crops. For Latakia region with plantations of various citrus crops, NDVI values were calculated for all images available in the archives for the period from 2013 to 2023. After that, regression analysis of different predictors calculated from NDVI with statistical values of yield was performed. It has been found that the best predictor of yield for all citrus cultivars is the annual average NDVI value, using which regression models with R2 ranging from 0.5 to 0.6 are possible. Less qualitative models with R2 varying between 0.36 and 0.45 may be built using the average NDVI values obtained in winter, prior to the vegetation season, as a predictor, thus opening up the possibility to obtain predictive estimates of yield even before the harvesting phase.
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Panek E., Gozdowski D. 2021. Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries. Agronomy, 11(2): 340. https://doi.org/10.3390/agronomy11020340
Rai S., Nandre J., Kanawade B.R.A. 2022. A Comparative Analysis of Crop Yield Prediction using Regression. International Conference on Intelligent Technologies (CONIT), 1–4. https://doi.org/10.1109/CONIT55038.2022.9847783.
Rehman S.U., Abbasi K., Qayyum A. 2020. Comparative Analysis of Citrus Fruits for Nutraceutical Properties. Food Science and Technology, 40(1): 153–157. https://doi.org/10.1590/fst.07519.
Rouse J.W., Haas R.H., Scheel J.A., Deering D.W. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Sym-posium, 1: 48–62.
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van der Velde M., van Diepen C.A., Baruth B. 2019. The European Crop Monitoring and Yield Forecasting System: Celebrating 25 years of JRC MARS Bulletins. Agricultural Systems, 168: 56–57.
Wang S., Xie W., Yan X. 2022. Effects of Future Climate Change on Citrus Quality and Yield in China. Sustainability, 14(15): 9366. https://doi.org/10.3390/su14159366.
Wu B., Meng J., Li Q., Yan N., Du X., Zhang M. 2014. Remote Sensing-Based Global Crop Monitoring: Experiences with China's CropWatch System. International Journal of Digital Earth, 7(2): 113–137. https://doi.org/10.1080/17538947.2013.821185.
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