Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain

文献类型： 外文期刊

第一作者： Tian, Miao

作者： Tian, Miao;Wang, Pengxin;Khan, Jahangir

作者机构：

关键词： drought forecasting;vegetation temperature condition index;ARIMA;Guanzhong Plain

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2016 年 8 卷 9 期

页码：

收录情况： SCI

摘要： This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval) of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models' development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1) models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1) models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.

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