Drought Forecasting with Vegetation Temperature Condition Index

The drought forecasting models is developed using the time series of the quantitative drought monitoring results of vegetation temperature condition index(VTCI) in the Guanzhong Plain of Northwest China.The autoregressive integrated moving average(ARIMA) is used to simulate the VTCI series of each pixel and forecast their changes in the future.A new way of modeling the spatio-temporal series is presented by extending of the forecasting models of some pixels to the whole area.The AR(1) models are suitable for all VTCI series of the 36 pixels.Therefore,the AR(1) models are applied to each pixel of the whole area,and the forecast is done with 1-2 lead-times.Comparing the monitoring and forecasting results,the forecasting accuracies of the AR(1) models are better,and the accuracies of the 1 lead-time are less than those of the 2 lead-times.The VTCI series of pixels in the whole study area are fitted by the selected best models.Comparing the fitting data with the historical data,the results show that VTCI series are better fitted by AR(1) models.Most of the simulating errors are small.All these results demonstrate that AR(1) models are suitable for drought forecasting using the VTCI series.