Winter Wheat Drought Monitoring with Multi-temporal MODIS data and AquaCrop Model—A Case Study in Henan Province

In recent years, frequent droughts have caused serious harm to the sustainable development of agriculture and food security. Based on the wide range and real time data, remote sensing was widely employed in drought monitoring. Many researchers have proposed a series of drought monitoring methods based on MODIS data over the past few decades. The AquaCrop model was released by FAO, which is suitable for crop growth monitoring in arid area with its simple interface, less input parameters and good simulation effect. AquaCrop model was applied to the researches on climate change, irrigation strategy, planting management and so on. Many scholars have verified the efficiency of AquaCrop model in arid areas of China. Since AquaCrop model is driven by water, it is reasonable to use it to monitor and reflect the drought process. This study was based on Terra-MODIS data products and agro-meteorological data during the year of 2007-2008, 2008-2009, 2009-2010 and 2011-2012 of Winter Wheat growth periods in Henan Province. In this study, firstly, localizing the AquaCrop by the simulated and observed Canopy Cover (CC), Biomass (B), Yield (Y) and 0-50cm Soil Water Content (SWC). The values between observed and simulated have good consistency, with R2 = 0.9289 of CC, R2 =0.9418 of B, R2 = 0.8309 of SWC. Meanwhile, the simulation results of four growth stages of winter wheat show that the model has good applicability and stability in Henan Province. Secondly, calculating TVDI using MODIS data in the same time range. Finally, analyzing the correlation between the results of two models and observed soil water content in different depth of soil layers. Similarly, the simulation accuracy of two models are better with shallow soil moisture. MODIS TVDI has a highest correlation with 30cm soil water content (r= -0.344), while AquaCrop model with 10cm (r=0.819). The research can provide important reference for drought monitoring of winter wheat and it is helpful for effective agriculture drought monitoring and decision-making.