Abstract
Projection of future changes in agricultural water demand and supply through numerical model simulations can be one of the reference materials used for long-term rural planning under climate change. With a distributed hydrological model incorporating an agricultural sub-model, time-series rice growth and its yield can be simulated in correspondence with time-series soil moisture simulated by a hydrological model. Whereas such an agro-hydrological model is useful for assessing the climate change impact on agriculture in that it can simulate the potential rice production and the water-related damages on it against the given rainfall and meteorological condition, one of the challenges in simulating a realistic situation by a model is how to give a planting date in the model in regions where it is not fixed to a certain calendar date and differs in different fields and in different years. In this study, we examined an algorithm to estimate planting date in paddy fields using satellite data. The data we used are the 16-day composite Enhanced Vegetation Index (EVI) product by the moderate resolution imaging spectroradiometer (MODIS), which provides global coverage with 250-m spatial resolution. The time series of the EVI values at each pixel was analyzed first to detect heading dates as the local maxima in each year. Considering the number of days for growing rice, the existence of the second and the third rice cropping was judged from the time intervals of the local maxima. The planting dates were then estimated from the determined heading dates and local minima of the EVI time-series EVI. The estimated planting dates were validated against national statistical data in Japan and also with field-surveyed data in Cambodia. They showed reasonable estimations in the areal statistics, but there remained gaps in the comparison at individual fields.





Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Biemans H, Speelman LH, Ludwig F, Moors EJ, Wiltshire AJ, Kumar P, Gerten D, Kabat P (2013) Future water resources for food production in five South Asian river basins and potential for adaptation—a modeling study. Sci Total Environ 468–469(Supplement):S117–S131
Didan K (2015a) MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006, Data set, NASA EOSDIS LP DAAC. 2018, https://doi.org/10.5067/modis/mod13q1.006. https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1_v006. Accessed 27 Aug 2015
Didan K (2015b) MYD13Q1 MODIS/Aqua vegetation indices 16-Day L3 Global 250 m SIN Grid V006, Data set, NASA EOSDIS LP DAAC. 2018, https://doi.org/10.5067/modis/myd13q1.006. https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/myd13q1_v006. Accessed 27 Aug 2015
Gao X, Huete AR, Ni W, Miura T (2000) Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ 74(3):609–620
Hirooka Y, Homma K, Kodo T, Shiraiwa T, Soben K, Chann M, Tsujimoto K, Tamagawa K, Koike T (2016) Evaluation of cultivation environment and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia. Field Crops Res 199:150–155
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213
Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) A crop phenology detection method using time-series MODIS data. Remote Sens Environ 96(3–4):366–374
Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N, Yokozawa M (2006) Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ 100(1):1–16
So Im M, Tsujimoto K, Aida K, Tamagawa K, Ohta T, Koike T, Nukui T, Sobue S, Homma K (2014) Water and food security under climate change in Cambodia. Trans Jpn Soc Aeronaut Space Sci Aerosp Technol Jpn 12(ists29):Tn_31–Tn_39
Statistics Bureau, Ministry of Internal Affairs and Communications of Japan, Portal Site of Official Statistics of Japan (e-Stat). https://www.e-stat.go.jp/en. Accessed 27 Aug 2018
Stouffer RJ, Eyring V, Meehl GA, Bony S, Senior C, Stevens B, Taylor KE (2017) CMIP5 scientific gaps and recommendations for CMIP6”. Bull Am Meteorol Soc 98(1):95–105
Witte JPM, Bartholomeus RP, van Bodegom PM, Cirkel DG, van Ek R, Fujita Y, Janssen GMCM, Spek TJ, Runhaar H (2015) A probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale. Landsc Ecol 30(5):835–854
Acknowledgements
Parts of this research were supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers JP16K06503, JP17H04484, and JP17H01496. We thank the anonymous reviewers for their insightful comments which helped us greatly in improving the manuscript from its earlier version.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tsujimoto, K., Ohta, T., Hirooka, Y. et al. Estimation of planting date in paddy fields by time-series MODIS data for basin-scale rice production modeling. Paddy Water Environ 17, 83–90 (2019). https://doi.org/10.1007/s10333-019-00700-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10333-019-00700-x