Examining the Impacts of Climate Variability on Agricultural Phenology: A Comprehensive Approach Integrating Geoinformatics, Satellite Agrometeorology, and Artificial Intelligence
Keywords:
Climate variability, crop phenology, geoinformatics, satellite agrometeorology, AI techniques, adaptation strategies, vulnerable areas, weather variables, crop growth and developmentAbstract
The study on the impacts of climate variability on agricultural phenologydelves into the exploration of climate variability's influence on agricultural phenology through the synergistic utilization of geoinformatics, satellite agrometeorology, and AI techniques. Geoinformatics serves the purpose of identifying vulnerable locations, while satellite agrometeorology furnishes indispensable weather data crucial for crop production. By employing AI techniques to analyze extensive datasets, valuable patterns in crop phenology can be discerned, leading to significant insights into crop reactions to climate change. The integration of these methodologies enables researchers to develop a comprehensive comprehension of how climate variability impacts crop phenology, thereby facilitating the formulation of adaptation plans by policymakers and farmers. Ultimately, this research contributes to the promotion of sustainable farming practices and the enhancement of food security amidst the challenges posed by climate change.
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L. He et al., “Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China,” Agric. For. Meteorol., vol. 200, pp. 135–143, 2015, doi: 10.1016/j.agrformet.2014.09.011.
R. Jiang et al., “Modelling adaptation strategies to reduce adverse impacts of climate change on maize cropping system in Northeast China,” Sci. Rep., vol. 11, no. 1, 2021, doi: 10.1038/s41598-020-79988-3.
AfDB, “Climate Smart Agriculture in the African Context. African Development Bank Group: Abidjan, Ivory Coast.” 2015. Accessed: May 02, 2023. [Online]. Available: https://cgspace.cgiar.org/handle/10568/68944
H. Kazemi and H. Akinci, “A land use suitability model for rainfed farming by Multi-criteria Decision-making Analysis (MCDA) and Geographic Information System (GIS),” Ecol. Eng., vol. 116, pp. 1–6, 2018, doi: 10.1016/j.ecoleng.2018.02.021.
A. El Yaacoubi, G. Malagi, A. Oukabli, M. Hafidi, and J. M. Legave, “Global warming impact on floral phenology of fruit trees species in Mediterranean region,” Sci. Hortic. (Amsterdam)., vol. 180, pp. 243–253, 2014, doi: 10.1016/j.scienta.2014.10.041.
F. M. Chmielewski, A. Müller, and E. Bruns, “Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961-2000,” Agric. For. Meteorol., vol. 121, no. 1–2, pp. 69–78, 2004, doi: 10.1016/S0168-1923(03)00161-8.
Y. Liu, Q. Chen, Q. Ge, J. Dai, and Y. Dou, “Effects of climate change and agronomic practice on changes in wheat phenology,” Clim. Change, vol. 150, no. 3–4, pp. 273–287, Oct. 2018, doi: 10.1007/s10584-018-2264-5.
Q. Zhang, J. Wang, X. Peng, P. Gong, and P. Shi, “Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data,” Int. J. Remote Sens., vol. 23, no. 15, pp. 3057–3078, 2002, doi: 10.1080/01431160110104728.
B. Cohen, “Urban growth in developing countries: A review of current trends and a caution regarding existing forecasts,” World Dev., vol. 32, no. 1, pp. 23–51, 2004, doi: 10.1016/j.worlddev.2003.04.008.
Z. Zhao, A. Sharifi, X. Dong, L. Shen, and B. J. He, “Spatial variability and temporal heterogeneity of surface urban heat island patterns and the suitability of local climate zones for land surface temperature characterization,” Remote Sens., vol. 13, no. 21, 2021, doi: 10.3390/rs13214338.
H. A. H. Al-Najjar et al., “Land cover classification from fused DSM and UAV images using convolutional neural networks,” Remote Sens., vol. 11, no. 12, p. 1461, 2019, doi: 10.3390/rs11121461.
W. Li, “Mapping urban impervious surfaces by using spectral mixture analysis and spectral indices,” Remote Sens., vol. 12, no. 1, 2020, doi: 10.3390/RS12010094.
A. Tariq and H. Shu, “CA-Markov chain analysis of seasonal land surface temperature and land use landcover change using optical multi-temporal satellite data of Faisalabad, Pakistan,” Remote Sens., vol. 12, no. 20, pp. 1–23, 2020, doi: 10.3390/rs12203402.
S. A. Zahoor et al., “Improving water use efficiency in agronomic crop production,” in Agronomic Crops: Volume 2: Management Practices, Springer Singapore, 2019, pp. 13–29. doi: 10.1007/978-981-32-9783-8_2.
S. Torresan, A. Critto, M. Dalla Valle, N. Harvey, and A. Marcomini, “Assessing coastal vulnerability to climate change: Comparing segmentation at global and regional scales,” in Sustainability Science, Apr. 2008, vol. 3, no. 1, pp. 45–65. doi: 10.1007/s11625-008-0045-1.
G. Faour and A. Fayad, “GIS-Based approach to the assessment of coastal vulnerability to sea level rise: Case study on the eastern Mediterranean soil mapping Lebanon view project multitemporal remote sensing based on an FVC reference period using Sentinel-2 for Mmnitoring Eichho,” J. Surv. Mapp., vol. 1, no. 3, pp. 41–48, 2013.
R. S. Mahendra, P. C. Mohanty, H. Bisoyi, T. S. Kumar, and S. Nayak, “Assessment and management of coastal multi-hazard vulnerability along the Cuddalore-Villupuram, east coast of India using geospatial techniques,” Ocean Coast. Manag., vol. 54, no. 4, pp. 302–311, 2011, doi: 10.1016/j.ocecoaman.2010.12.008.
K. Kantamaneni et al., “A systematic review of coastal vulnerability assessment studies along Andhra Pradesh, India: A critical evaluation of data gathering, risk levels and mitigation strategies,” Water (Switzerland), vol. 11, no. 2, 2019, doi: 10.3390/w11020393.
Q. Weng, D. Lu, and J. Schubring, “Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies,” Remote Sens. Environ., vol. 89, no. 4, pp. 467–483, 2004, doi: 10.1016/j.rse.2003.11.005.
S. Hussain et al., “Using space–time scan statistic for studying the effects of COVID-19 in Punjab, Pakistan: a guideline for policy measures in regional agriculture,” Environmental Science and Pollution Research. Springer Science and Business Media Deutschland GmbH, Mar. 01, 2021. doi: 10.1007/s11356-021-17433-2.
M. F. Baqa et al., “Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan,” Land, vol. 10, no. 7, 2021, doi: 10.3390/land10070700.
S. Pal and S. Ziaul, “Detection of land use and land cover change and land surface temperature in English Bazar urban centre,” Egypt. J. Remote Sens. Sp. Sci., vol. 20, no. 1, pp. 125–145, 2017, doi: 10.1016/j.ejrs.2016.11.003.
A. Mathew, S. Khandelwal, and N. Kaul, “Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: Study of Chandigarh city, India,” Sustain. Cities Soc., vol. 26, pp. 264–277, 2016, doi: 10.1016/j.scs.2016.06.018.
V. N. Mishra, P. K. Rai, P. Kumar, and R. Prasad, “Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images,” Forum Geogr., vol. XV, no. 1, pp. 45–53, 2016, doi: 10.5775/fg.2016.137.i.
S. K. Singh, P. B. Laari, S. Mustak, P. K. Srivastava, and S. Szabó, “Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India,” Geocarto Int., vol. 33, no. 11, pp. 1202–1222, Nov. 2018, doi: 10.1080/10106049.2017.1343390.
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