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Article

Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands

by 1,2,3, 4,*, 5 and 1
1
School of Microelectronics, Tianjin University, Tianjin 300072, China
2
School of Physics and Electronic Information, Hulunbuir College, Hulunbuir 021008, China
3
Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin University, Tianjin 300072, China
4
Department of Electronics, Carleton University, Ottawa, ON K1S5B6, Canada
5
College of Physics and Electronic Information Engineer, Qinghai Nationalities University, Xining 810000, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(9), 1789; https://doi.org/10.3390/w11091789
Received: 22 July 2019 / Revised: 21 August 2019 / Accepted: 24 August 2019 / Published: 28 August 2019
(This article belongs to the Section Hydrology)
The latest research indicates that there are time-lag effects between the normalized difference vegetation index (NDVI) and the precipitation variation. It is well known that the time-lags are different from region to region, and there are time-lags for the NDVI itself correlated to the precipitation. In the arid and semi-arid grasslands, the annual NDVI has proved not only to be highly dependent on the precipitation of the concurrent year and previous years, but also the NDVI of previous years. This paper proposes a method using recurrent neural network (RNN) to capture both time-lags of the NDVI with respect to the NDVI itself, and of the NDVI with respect to precipitation. To quantitatively capture these time-lags, 16 years of the NDVI and precipitation data are used to construct the prediction model of the NDVI with respect to precipitation. This study focuses on the arid and semi-arid Hulunbuir grasslands dominated by perennials in northeast China. Using RNN, the time-lag effects are captured at a 1 year time-lag of precipitation and a 2 year time-lag of the NDVI. The successful capture of the time-lag effects provides significant value for the accurate prediction of vegetation variation for arid and semi-arid grasslands. View Full-Text
Keywords: time-lag effects; recurrent neural network; NDVI; precipitation time-lag effects; recurrent neural network; NDVI; precipitation
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MDPI and ACS Style

Wu, T.; Feng, F.; Lin, Q.; Bai, H. Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands. Water 2019, 11, 1789. https://doi.org/10.3390/w11091789

AMA Style

Wu T, Feng F, Lin Q, Bai H. Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands. Water. 2019; 11(9):1789. https://doi.org/10.3390/w11091789

Chicago/Turabian Style

Wu, Taosuo, Feng Feng, Qian Lin, and Hongmei Bai. 2019. "Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands" Water 11, no. 9: 1789. https://doi.org/10.3390/w11091789

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