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Figure/Table detail
Agricultural Drought Monitoring in Arid Irrigated Areas Based on TVDI Combined with ICEEMDAN-ARIMA Model
WEIYuxin, LIQiao, TAOHongfei, LUChunlei, LUOXu, MAHEMUJIANGAihemaiti, JIANGYouwei
Smart Agriculture
, DOI:
10.12133/j.smartag.SA202502005
Fig.1
Santun river irrigation area in Xinjiang
Other figure/table from this article
Table 3
Linear fitting results of TVDI/VTCI and 0－10 cm measured soil water content
Table 2
Classification standard of Mann-Kendall trend test
Table 5
Evaluation of the Prediction accuracy of the ICEEMDAN-ARIMA combined model in the Santun river irrigation area of Xinjiang
Fig.7
Interannual variation rate and distribution proportion of TVDI in Santun river irrigation area， Xinjiang from 2005 to 2022
Fig.8
Trend type and area proportion of drought change in Santun river irrigation area， Xinjiang， from 2005 to 2022
Table 1
Drought classification criteria based on different drought indices
Fig.6
Interannual variation of TVDI and area proportion of different drought grades in Santun river irrigation area from 2005 to 2022
Fig.10
Analysis of spring， summer and autumn forecast results of ICEEMDAN-ARIMA combined model in Santun river irrigation area， Xinjiang in 2023
Fig.2
Data preprocessing and main research workflow of agricultural drought monitoring research in arid irrigation areas based on TVDI combined with ICEEMDAN-ARIMA model
Fig.3
Sampling points of measured soil moisture content in Santun river irrigation area
Fig.4
Multi-temporal TVDI dry and wet edge fitting in Santun river irrigation area， Xinjiang， from 2005 to 2022
Fig.9
TVDI data sampling points of the ICEEMDAN-ARIMA combined model in Santun river irrigation area， Xinjiang
Table 4
Component order of ICEEMDAN-ARIMA combination model at different sampling points
Fig.5
Spatial distribution of temperature vegetation drought index in Santun river irrigation area， Xinjiang， from 2005 to 2022
