Author
Listed:
- Fa Zhao
(School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Guijun Yang
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China)
- Hao Yang
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Huiling Long
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Weimeng Xu
(College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China)
- Yaohui Zhu
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Yang Meng
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Shaoyu Han
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
- Miao Liu
(Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)
Abstract
Accurate determination of crop phenology is key to field management and decision making. The existing research on phenology based on remote sensing data is mainly phenology monitoring, which cannot realize the prediction of phenology. In this paper, we propose a method to predict the maturity date (MD) of winter wheat based on a combination of phenology monitoring method and accumulated temperature. The method is divided into three steps. First, 2-band Enhanced Vegetation Index (EVI2) time series data were generated using the moderate-resolution imaging spectroradiometer (MODIS) reflectance data at 8-day intervals; then, the time series were reconstructed using polynomial fitting and the heading date (HD) of winter wheat was extracted using the maximum method. Secondly, the average cumulative temperature required for winter wheat to go from HD to MD was calculated based on historical phenological data and meteorological data. Finally, the timing of winter wheat HD and the current year’s Meteorological Data were combined to predict winter wheat MD. The method was used to predict the MD of winter wheat in Hebei in 2018 and was validated with data from the phenology station and the Modis Land Cover Dynamics (MCD12Q2) product. The results showed that the coefficient of determination (R 2 ) for predicting MD using this method was 0.48 and 0.74, the root mean square error (RMSE) was 7.03 and 4.91 days, and Bias was 4.93 and −3.59 days, respectively. In summary, the method is capable of predicting winter wheat MD at the regional scale.
Suggested Citation
Fa Zhao & Guijun Yang & Hao Yang & Huiling Long & Weimeng Xu & Yaohui Zhu & Yang Meng & Shaoyu Han & Miao Liu, 2022.
"A Method for Prediction of Winter Wheat Maturity Date Based on MODIS Time Series and Accumulated Temperature,"
Agriculture, MDPI, vol. 12(7), pages 1-14, June.
Handle:
RePEc:gam:jagris:v:12:y:2022:i:7:p:945-:d:852070
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Cited by:
- Meftah Salem M. Alfatni & Siti Khairunniza-Bejo & Mohammad Hamiruce B. Marhaban & Osama M. Ben Saaed & Aouache Mustapha & Abdul Rashid Mohamed Shariff, 2022.
"Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis,"
Agriculture, MDPI, vol. 12(9), pages 1-28, September.
- István Kristó & Marianna Vályi-Nagy & Attila Rácz & Katalin Irmes & Lajos Szentpéteri & Márton Jolánkai & Gergő Péter Kovács & Mária Ágnes Fodor & Apolka Ujj & Klára Veresné Valentinyi & Melinda Tar, 2023.
"Effects of Nutrient Supply and Seed Size on Germination Parameters and Yield in the Next Crop Year of Winter Wheat ( Triticum aestivum L.),"
Agriculture, MDPI, vol. 13(2), pages 1-17, February.
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