Determination of Onset and End of Growing Season in Nigeria using the Modified Dynamic (Optimal) Threshold Method and Satellite data
DOI:
https://doi.org/10.62292/njtep.v1i1.2023.15Keywords:
Vegetation phenology, SOS & EOS, climate change, modified dynamic (optimal) threshold method, Koppen climate zonesAbstract
Understanding and accurately determining the onset and end of the growing season is essential for crop management, forecasting yields, and assessing the impacts of climate change, which are crucial for various sectors such as agriculture, ecology, and climate science. The study investigates the start and end of seasons using the optimal threshold method in different climate zones from 2001 to 2022. The climate zones include Tropical Rainforest (Af), Tropical Monsoon (Am), Tropical Wet (Aw), Hot Semi-Arid (BSh), Hot Arid (BWh), and Hot Summer Mediterranean climates. The relationship between climate parameters (temperature and precipitation) and phenology was examined using cross-correlation analysis. Furthermore, the research explores the annual distribution of precipitation and temperature, highlighting the variable nature of precipitation compared to temperature. Climate zone-specific analyses reveal trends in precipitation and temperature changes, indicating potential impacts on vegetation growth. The results show that the Af (BWh) climate zone indicated the longest (shortest) season length, and longer zones with a larger Length of Season (LOS) experienced delayed End of Season (EOS) and/or an early onset of the season. Moreover, Af climate seasons start earlier and finish later than those in the other climate zones. The findings have shown that season length (LOS) in Af, Aw, BSh, BWh, and Csb increased at 1.2, 0.4, 0.2, 0.9, and 0.5 days/yr, respectively. However, it is noteworthy that Am experiences a contraction at -0.6 days/yr. The study found that climatic fluctuation has an impact on vegetation phenology throughout all climate zones of Nigeria. Changes in agricultural growing seasons should be studied to maximize agricultural output.
References
Atzberger, C., Eilers, P.H.C. (2011) A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digital Earth, 4 (5):365–386. https://doi.org/10.1080/17538947.2010.505664
Atkinson, P.M., Jeganathan, C., Dash, J. and Atzberger, C. (2012). Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ, 123:400–417. https://doi.org/10.1016/j.rse.2012.04.001
Beck, P.S.A., Atzberger, C., Høgda, K.A. and Skidmore, A.K. (2006). Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI. Remote Sensing of Environment, 100(3):321-334. https://doi.org/10.1016/j.rse.2005.10.021
Cerlini, P.B., Saraceni, M., Orlandi, F., Silvestri, L. and Fornaciari, M. (2022). Phenological response to temperature variability and orography in Central Italy. Int J Biometeorol, 66:71–86. https://doi.org/10.1007/s00484-021-02190-1
Chen, X., & Zhang, Y. (2023). The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming. Frontiers in plant science, 14: 1248482. https://doi.org/10.3389/fpls.2023.1248482
Cui, X., Xu, G., He, X. and Luo, D. (2022). Influences of Seasonal Soil Moisture and Temperature on Vegetation Phenology in the Qilian Mountains. REMOTE SENS., 14: 3645. https://doi.org/10.3390/rs14153645
Curnel, Y., Oger, R. (2006). Agrophenology indicators from remote sensing: state of the art. In Proceedings of ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates, Stresa, Italy, 30 Nov–1 Dec 2006; pp. 31–38. https://www.isprs.org/proceedings/XXXVI/8-W48/31_XXXVI-8-W48.pdf
Duchemin, B.T., Goubier, J.M. and Courrier, G. (1999). Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sens. Environ., 67:68–82. https://doi.org/10.1016/S0034-4257(98)00067-4
Fischer, A. (1994) A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote. Sens. Environ., 48:220–230. https://doi.org/10.1016/0034-4257%2894%2990143-0
Fisher, J.I., Mustard, J.F. and Vadeboncoeur, M.A. (2006). Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sens. Environ. 100:265–279. https://harvardforest1.fas.harvard.edu/publications/pdfs/Fisher_RemoteSensEnviron_2007.pdf
Huang, X., Liu, J., Zhu, W., Atzberger, C. and Liu, Q. (2019). The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method. REMOTE. SENS., 11(23): 2725. https://doi.org/10.3390/rs11232725
Igbawua, T., Zhang, J., Chang, Q. and Yao, F. (2016). Vegetation dynamics in relation with climate over Nigeria from 1982 to 2011. Environmental Earth Sciences, 75:1-16. https://link.springer.com/article/10.1007/s12665-015-5106-z
Igbawua, T., Abiem, L.T., tsor, J.O., Adagba, G. and Egbe, S. (2023) Assessment of Start and End of Growing Seasons in different Ecological Zones of Nigeria Using Satellite Data, Nigerian Annals of Pure and Applied Sciences, 6(1):154-171. DOI:10.5281/zenodo.7338397
Jeong, S.J., Ho, C.H., Kim, J. and Levis, S. (2011). Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2_ CO2 climate. Climate Dynamics, 37: 821–833. https://link.springer.com/article/10.1007/s00382-010-0827-x
Jonsson, P., Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote. Sens., 40:1824–1832. doi: 10.1109/TGRS.2002.802519
Li, H., Li, F.Y., Guo, J. and Gao, X. (2023). An improved dynamic threshold method for determining the start of the vegetation greening season in remote sensing monitoring: The case of Inner Mongolia. Ecological Informatics, 78 (102378). https://doi.org/10.1016/j.ecoinf.2023.102378
Liu, L., Zhang, X. (2020) Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016. Sci Rep, 10:17952. https://doi.org/10.1038/s41598-020-74804-4
Lloyd, D. (1990). A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote. Sens., 11:12, 2269-2279, DOI: 10.1080/01431169008955174
Miao, L., Jiang, C., Xue, B., Liu, Q., He, B. and Nath, R. (2014). Vegetation dynamics and factor analysis in arid and semi-arid Inner Mongolia. Environmental Earth Sciences. 73:2343–2352. https://doi.org/10.1007/s12665-014-3582-1
Reed, B.C., Brown, J.F., Vanderzee, D., Loveland, T.R., Merchant, J.W. and Ohlen, D.O. (1994) Measuring phenological variability from satellite imagery. J. Veg. Sci., 5(5):703-714. https://doi.org/10.2307/3235884
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N. and Ohno, H. (2005) A crop phenology detection method using time-series MODIS data. Remote Sens. Environ., 96(3-4):366-374. https://doi.org/10.1016/j.rse.2005.03.008
Wang, S., Yang, B., Yang, Q., Lu, L., Wang, X. and Peng, Y. (2016). Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012. PLoS One. 11(6): e0157134. https://doi.org/10.1371/journal.pone.0157134
Wang, Y., Luo, Y. and Shafeeque, M. (2019). Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China. Science of The Total Environment. 693, https://doi.org/10.1016/j.scitotenv.2019.07.359
White, M.A., Thornton, P.E. and Running, S.W. (1997). A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem. Cycles, 11(2):217-234. doi: 10.1029/97GB00330
Workie, T. G. and Debella, H. J. (2018). Climate change and its effects on vegetation phenology across ecoregions of Ethiopia. Global Ecology and Conservation, 13:e00366. https://doi.org/10.1016/j.gecco.2017.e00366
Zhang, X., Friedl, M.A. Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A. (2003) Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 84:471–475. https://doi.org/10.1016/S0034-4257(02)00135-9