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

Abstract

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.

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

“…Most studies have pointed out that it is insufficient to monitor drought conditions with a single index. T s and vegetation information have been combined for assessing droughts [1,2,4,14,15]. Goward et al (1985), Hope et al (1986), and Nemani et al (1989) found that the slope of the T s /NDVI curve could provide some soil moisture information [16][17][18].…”

Mathematics Model on Syariah Share Pricing in ISSI

“…ARIMA juga cukup baik dalam memodelkan kekeringan pertanian di daerah Guanzhong Cina. Pemodelan tersebut berdasarkan pada data time series yang diperoleh dari hasil pengamatan Vegetation Temperature Condition Index (VTCI) (Tian et al, 2016). Penerapan lain metode ini adalah pada data keberlanjutan dana providet karyawan di Malaysia (Hassan & Othman, 2018).…”

Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

“…To evaluate the influence of wildfire on post-fire vegetation recovery, we estimated an EVI time series for a scenario where the 2011 wildfire did not occur. Various time-series models are applied in remote-sensing vegetation indices such as the linear model autoregressive integrated moving average (ARIMA), or variations developed for dealing with non-stationary data, such as the seasonal ARIMA (SARIMA) [43]. EVI is a nonlinear, nonstationary, and seasonal time series, and the ANN is preferable to other nonlinear time series such as bilinear models, threshold autoregressive models, and regression trees [44].…”