Authors :
Onyibe, J. E.; Wahab, A. A.; Baba, D.; Durojaiye, L. O.; Muibi, K. H.
Volume/Issue :
Volume 10 - 2025, Issue 11 - November
Google Scholar :
https://tinyurl.com/3en9tzuy
Scribd :
https://tinyurl.com/2hrpz7uy
DOI :
https://doi.org/10.38124/ijisrt/25nov595
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Abstract :
Rice is a vital crop for global food security, and remote monitoring and mapping are crucial for detecting crop
stress and estimating cultivated areas and outputs. This study employed Normalized Difference Vegetation Index (NDVI)
and supervised image classification to assess crop health and map rice-growing areas in three Nigerian states (Kano, Kaduna
and Katsina). The research analyzed satellite imagery from Sentinel and Landsat missions during the 2022 growing season.
NDVI values revealed variations in vegetation health, ranging from 0.044 to 0.148 in May and peaking at 0.110 to 0.450 in
August. Supervised image classification identified an average rice cultivation area of 0.227 hectares. The actual rice yield
was measured at 1512.624 kg/ha, while the model predicted a higher output of 4218.21 kg/ha. The prediction model exhibited
an average root mean square error (RMSE) of 0.419, corresponding to an accuracy of 82%. This study highlights the
potential of remote sensing technologies in tracking rice crop performance and mapping cultivated areas. These tools provide
critical insights for data-driven decisions, enhancing rice sector development planning. By integrating remote sensing
technologies, this research underscores their value in minimizing human involvement in estimating rice area and
productivity indices. The study's findings have significant implications for rice production and food security in Nigeria and
globally. The use of remote sensing technologies can help optimize rice production, reduce losses, and improve food
availability and affordability. By leveraging these technologies, stakeholders can make informed decisions to enhance rice
sector development, ultimately contributing to global food security.
Keywords :
Rice, NDVI, Remote Sensing, Crop Monitoring and Food Security.
References :
- Adebayo, A. A., & Olusola, A. O. (2020). Unmanned Aerial Vehicle (UAV) for Small-Area Imagery in Nigeria. Journal of Geography and Regional Planning, 13(2), 1-9.
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- Esquerdo J, Zullo J, Antunes JFG (2011). Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil. International Journal of Remote Sensing 32: 3711–3727.
- Food and Agriculture Organization (FAO) of the United Nations. (2020). The State of Food Security and Nutrition in the World 2020. Transforming food systems for affordable healthy diets. Rome: FAO.
- Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture," Remote Sensing of Environment 90, 337- 352 [doi:10.1016/j.rse.2003.12.013].
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- Mkhabela MS, Bullock P, Raj S, Wang S, Yang Y (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151: 385–393
- Onyibe, J. E., Wahab, A. A., Dahiru B., Durojaiye, L. O., & Muibi, K. H. (2024). A Normalised Difference Vegetation Index Model for Maize Crop Performance Monitoring and Cropland Area Mapping in Sudan Ecological Zone of Nigeria. Asian Journal of Advanced Research and Reports, 18(6), 10–20. https://doi.org/10.9734/ajarr/2024/v18i6649
- Schut AGT, Stephens DJ, Stovold RGH, Adams M, Craig RL (2009). Improved wheat yield and production forecasting with a moisture stress index, AVHRR, and MODIS data. Crop & Pasture Science 60: 60–70
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- Wang, F.-M., J.-F. Huang, Y.-L. Tang, and X.-Z. Wang, "New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice," Rice Science 14, 195-203 (2007) [doi:10.1016/S1672- 6308(07)60027-4]
- Wang, X.; Mochizuki, K.; Yamaya, Y.; Tani, H.; Kobayashi, N.; Sonobe, R. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. J. Appl. Remote Sens. 2018,12, 026019.
- Zhang, M., Li, Z., & Chen, Q. (2020). Crop Stress Detection Using NDVI and Satellite Imagery: A Review. International Journal of Agricultural and Biological Engineering, 13(4), 77-86.
Rice is a vital crop for global food security, and remote monitoring and mapping are crucial for detecting crop
stress and estimating cultivated areas and outputs. This study employed Normalized Difference Vegetation Index (NDVI)
and supervised image classification to assess crop health and map rice-growing areas in three Nigerian states (Kano, Kaduna
and Katsina). The research analyzed satellite imagery from Sentinel and Landsat missions during the 2022 growing season.
NDVI values revealed variations in vegetation health, ranging from 0.044 to 0.148 in May and peaking at 0.110 to 0.450 in
August. Supervised image classification identified an average rice cultivation area of 0.227 hectares. The actual rice yield
was measured at 1512.624 kg/ha, while the model predicted a higher output of 4218.21 kg/ha. The prediction model exhibited
an average root mean square error (RMSE) of 0.419, corresponding to an accuracy of 82%. This study highlights the
potential of remote sensing technologies in tracking rice crop performance and mapping cultivated areas. These tools provide
critical insights for data-driven decisions, enhancing rice sector development planning. By integrating remote sensing
technologies, this research underscores their value in minimizing human involvement in estimating rice area and
productivity indices. The study's findings have significant implications for rice production and food security in Nigeria and
globally. The use of remote sensing technologies can help optimize rice production, reduce losses, and improve food
availability and affordability. By leveraging these technologies, stakeholders can make informed decisions to enhance rice
sector development, ultimately contributing to global food security.
Keywords :
Rice, NDVI, Remote Sensing, Crop Monitoring and Food Security.