For the sake of the increasing demands of oil and gas essential, scientists and engineers have exerted great efforts in obtaining natural resources deposited in the deep water. Risers, as the channel between platform and wellhead, play a key role in oil and gas transportation. Subjected to coarse environmental conditions and uncertain loading patterns, risers would display complex dynamic behaviors which could result in severe fatigue damages. Recently, riser response is commonly measured using passive and durable Fiber Bragg Grating (FBG) strain sensors for industry safety, especially for Integrity Management (IM). However, for technique difficulties as well as economical consideration, it is impossible to execute distributed fatigue monitoring on the whole riser, which makes it essential to find out an optimized method utilizing the least number of sensors and reconstruct the global response of risers. This paper propose a method which combines H<sub>2</sub> norms and Kalman filter to give optimal state estimation of risers based on limited FBG-based dynamic strain information. The H<sub>2</sub> norms are used to give guideline for optimized sensor placement. Meanwhile, Kalman filter realize the global response reconstruction of risers as well as minimize the environmental disturbance. The accuracy and efficiency of the method has been verified by a numerical case. This study provides an FBG-based early warning technique for industry application of deepwater risers in future.