Application of 4DVAR and EnKF approaches for assimilating vegetation temperature condition index

The objective of this study was to combine the remotely sensed Vegetation Temperature Condition Index (VTCI) and CERES-Wheat model to get high accuracy of drought monitoring results by using two data assimilation approaches, the Four-dimensional Variational (4DVAR) and Ensemble Kalman Filter (EnKF). VTCI was retrieved from remote sensing data (AVHRR) for drought monitoring, and the surface soil moisture was simulated from the CERES-Wheat model using ground survey data and meteorological data in Guanzhong Plain of Shaanxi province. The simulated VTCI values in the study area were achieved by employing the established empirical linear model between retrieved VTCI and soil surface moisture. The assimilation was carried out in the eight sampling sites and the whole study area, respectively. After establishing the assimilation system, the retrieved VTCI values and the simulated ones of the eight sampling sites were used to test the two assimilation approaches. The results showed that the assimilated VTCI values of the sites were more accurate and closer to the real ones. The texture of the assimilated VTCI image of the whole study area was more smooth than that of the retrieved one, and the sudden changes between the adjacent pixels in the assimilated image were reduced compared to those of the retrieved VTCI image. Based on the prior knowledge of the spatial drought occurrence in the study area, the assimilated VTCI values could get a high accuracy of drought monitoring results. After comparing the distributions of the differences and root mean square errors between the two assimilated VTCI values and the retrieved ones in the study area, it can be concluded that the EnKF approach has stronger applicability and higher accuracy than those of the 4DVAR approach.