Abstract
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.
Highlights
The interannual variability of vegetation can be well-described by the remotely sensed normalized difference vegetation index (NDVI), which is derived from satellite optical-infrared remote sensing [1,2,3,4,5,6].In arid and semi-arid regions, the annual NDVI is highly sensitive to the interannual variability of precipitation [7,8,9,10]
It was found that recurrent neural network (RNN) has the best performance among the four comparative models in predicting the NDVI with respect to precipitation in terms of mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) in the four sub-regions
The results indicate that the RNN model has better predicting accuracy (MAE) and stronger robustness (RMSE and MAPE) in the NDVI predictions than
Summary
In arid and semi-arid regions, the annual NDVI is highly sensitive to the interannual variability of precipitation [7,8,9,10]. The annual NDVI has proved to be highly dependent on the precipitation of the concurrent year and the previous year [7,8,11]. The dependence of vegetation variation on precipitation is referred to as “time-lag effects” [8,12]. These effects have been observed in different arid and semi-arid regions [12]. In order to understand time-lag effects, the time-lags have to be investigated both qualitatively and quantitatively. It is desirable to have quantitatively investigated time-lags for the further understanding of time-lag effects
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