# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 5 Multi-Source Data and Data Assimilation
### Data Assimilation  
Data assimilation can improve the accuracy of inversed parameters (e.g., LAI and soil moisture) by assimilating observation information into dynamic models (here, a dynamic model is a discrete stochastic-dynamical system whose parameters may include the external forcings or the boundary conditions [243]) and minimizing the differences between observed and simulated values [243; 244; 245; 246]. The temporal resolutions of dynamic models are usually higher than those of remote sensing data, thus addressing the discontinuity of remotely sensed data. As for drought monitoring, data assimilation can improve the accuracy and temporal resolution of soil moisture inversion. Moreover, the root-zone soil moisture could be acquired through assimilating remote sensing data into hydrological models, which is critical for evaluating agricultural drought [247]. However, most remote sensing detection methods can only obtain surface soil moisture [248; 249]. When drought occurs, surface soil moisture is decoupled from root-zone soil moisture, and the vertical gradient of soil moisture cannot be directly retrieved [250; 251]. In recent years, it has become a notable trend to assimilate remote sensing soil moisture products and related meteorological factors into dynamic models [251; 252; 253; 254; 255; 256; 257; 258; 259]. The ensemble Kalman filter (EnKF) is currently the mainstream method for data assimilation [260; 261; 262; 263]. [PERSON] et al. [260] and [PERSON] et al. [261] demonstrated that the inversion accuracy and efficiency of soil moisture could be improved by assimilating microwave data into the land surface and radiative transfer models with EnKF. [PERSON] et al. [262] proved that the precision of simulated soil moisture of the Boreal Ecosystem Production Simulator (BEPS) in arid and semi-arid areas was higher after assimilating inversed soil moisture of remote sensing. [PERSON] and [PERSON] [263] constructed the Auto-Tuned Land Data Assimilation System (ATLAS) to analyze soil moisture anomalies and monitor drought by assimilating the rainfall data and soil moisture inversed with remote sensing. Apart from soil moisture, [PERSON] et al. [264] also used EnKF to assimilate other parameters (e.g., LAI, FVC, fraction of absorbed photosynthetically active radiation (FAPAR) and chlorophyll content, which are inversed mainly with optical remote sensing) into the AcquaCrop model and estimated the loss of wheat grain yield caused by drought. Root-zone soil moisture can be obtained from surface moisture conditions with the utilization of dynamic models, especially in the context of microwave remote sensing. It has been demonstrated that the assimilation of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) as well as the Scanning Multichannel Microwave Radiometer (SMMR) soil moisture to NASA's Catchment land surface model can achieve higher accuracies in both surface and root-zone soil moisture estimation [265; 266]. Subsequent studies have made use of more data sources, e.g., the Advanced SCATterometer (ASCAT, active microwave), the Soil Moisture and Ocean Salinity (SMOS, passive microwave), the Soil Moisture Active Passive (SMAP, activate/passive microwave) and GRACE (gravity), and further enhanced the retrieval of root-zone soil moisture [267; 268; 269; 270; 271; 272]. The correlation between surface and root-zone soil moisture is often nonlinear or non-significant [273]. For hydrological models with nonlinear expressions, it is inappropriate to assume that errors in EnKF follow a Gaussian distribution. Moreover, linear updating rules might violate the conservation of mass [274]. To solve this problem, [PERSON] et al. [275] noticed that the particle filter (PF) method could relax the Gaussian hypothesis while conforming to the conservation of mass. Additionally, PF does not rely on the cross-covariance between the surface and root-zone soil moisture. Therefore, PF is considered more suitable than EnKF for estimating soil moisture in the root zone [276].