A review of data assimilation of crop growth simulation based on remote sensing information

It is of great strategic importance of agricultural sustainable development for obtaining in timely the agricultural information, such as crop growing and yield. Data assimilation of remote sensing based on crop growth simulation becomes an effective means of monitoring regional agriculture information, which provides significant advantages in terms of economic cost, effectiveness, precision and suitability on regional scales. Data assimilation based crop growth process models can be defined as the methodology of improving process and precision of simulation which integrates simulated forecast with multi-source observation in the framework of dynamic models. According to mathematic principles of integrating model forecast with observation, three approaches of assimilating remote sensing information into crop models are concluded in this review, such as forcing, calibration and updating. Some critical issues and trends in the operation system of remote sensing data assimilation based on crop models on regional or global scales has been discussed, such as remotely sensed observation, uncertainty in assimilating process, data assimilation schemes. It is the significant and valuable research work for data assimilation of crop growth simulation based on remote sensing information. This review is a great of reference and summary for improving agricultural monitoring operation based on data assimilation technology.