Abstract
This paper presents a study of vegetation phenology, which is used to monitor the state of rice fields. A well-known parameter, Normalized Difference Vegetation Index (NDVI) was created and based on the phenology of satellite images using Moderate Resolution Imaging Spectroradiometer (MODIS). Another parameter, Excessive Green Index (ExG), is also utilized to obtain the phenology from the field camera using visible range images. This study compares two phenological graphs. The results found a significant correlation between two phenology graphs on four observed rice fields. It should be noted that the field data was used as a validation data because it is a type of near field acquisition (high resolution, without atmospheric interference). In comparison to the field data, the results showed that the phenology obtained from MODIS was also used to analyze and estimate the different states of a rice field (e.g. seeding, heading and harvesting). Given the MODIS data in a wide area, we can monitor the status of several rice fields. It can be useful in terms of agricultural management (e.g. planning, demands, supplies and an environmental purpose, the campaign to reduce rice stubble burning).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Industrial Estate Authority of Thailand, 3 December 1995. http://www.dit.go.th/region/SA%20KAEO/Content?id=1712. Accessed 5 Jan 2016
Toshihiro, S., Masayuki, Y., et al.: A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96(3), 366–374 (2005). sciencedirect
Bethany, A.B., Robert, W.J., et al.: A curve fitting procedure to derive inter-annual phonologies from time series of noisy satellite NDVI data. Remote Sens. Environ. 106(2), 137–145 (2007). Science Direct
Brian, D.W., Stephen, L.E., Jude, H.K.: Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ. 108(3), 290–310 (2007)
Gistda (2016). http://www.gistda.or.th/main/th/node/948. Accessed 5 Jan 2016
USGS Earth Resources and Observation Science. https://dx.doi.org/10.3133/fs20143052. Accessed 5 Jan 2016
Narut, S., Panu, S., Preesan, R.: Rice growing stage monitoring in small-scale region using ExG vegetation index. In: 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Ratchasima, Thailand. IEEE (2014)
Huete, A, Justice, C., van Leeuwen, W.: MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document, Version 3. University of Arizona, Tucson, University of Virginia Department of Environmental Sciences, Charlottesville (1999)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Srichupieam, W., Soontranon, N., Chavanavesskul, S. (2020). Comparison of Rice Phenology from MODIS and Ground Image Data in Sakaeo Province, Thailand. In: Monprapussorn, S., Lin, Z., Sitthi, A., Wetchayont, P. (eds) Geoinformatics for Sustainable Development in Asian Cities. ICGGS 2018. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-030-33900-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-33900-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33899-2
Online ISBN: 978-3-030-33900-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)