Name
Abstract | ||
|---|---|---|
Regarded as a near real time drought monitoring method, the VTCI index based on remote sensing data is applied to the drought forecasting in the Guanzhong Plain. ARIMA models are used in the VTCI series, and forecast its changes in the future. A new way of modeling for the spatio-temporal series is used in the VTCI series. The time series of 36 pixels are studied firstly for their fitting models. Then the ARIMA model fitting for the whole area is determined. The AR(1) model are chosen to be the best model used in each pixel of the whole area, and the forecast is done with 1-2 steps. The results show that forecasting accuracy is better, 1 step is better than 2 steps. The historical VTCI data are simulated by AR(1) models. Comparing the simulating data with the historical data, the results show that the simulating accuracy is better. Most of the simulating errors are small. All results demonstrate that AR(1) model developed for VTCI series can be used for the drought forecasting in the Guanzhong Plain. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.mcm.2009.10.031 | Mathematical and Computer Modelling |
Keywords | Field | DocType |
historical vtci data,time series,drought forecasting,whole area,guanzhong plain,spatio-temporal series,vtci index,arima model fitting,vtci series,simulating errors,arima model,forecasting errors,vegetation temperature condition index,indexation,near real time | Autoregressive model,Remote sensing,Autoregressive integrated moving average,Pixel,Mathematics | Journal |
Volume | Issue | ISSN |
51 | 11-12 | Mathematical and Computer Modelling |
Citations | PageRank | References |
12 | 1.02 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Han | 1 | 12 | 1.02 |
| Peng Xin Wang | 2 | 14 | 6.53 |
| Shu Yu Zhang | 3 | 12 | 1.02 |
| De Hai Zhu | 4 | 102 | 23.81 |