Super-resolution microscopy typically achieves high 2D spatial resolution but the detection of depth is always difficult. At the same time the temporal resolution remains low and obstructs most biological and chemical researches. In this thesis, I firstly introduced the depth detection method via phase modulation with a 4f system. In the Fourier domain, a phase mask encodes the depth information with a specific phase pattern, double helix phase mask. The final point spread functions deviates from the standard Gaussian point spread functions and the depth information can be measured with high precision by fitting the corresponding double helix point spread functions. Based on the 4f system, I modified the instrument and propose a novel technique Super Temporal-Resolved Microscopy (STReM) to improve the temporal resolution of 2D super-resolution microscopy. The fundamental basis for STReM is the utilization of a double helix phase mask which is rotated at fast speeds to encode temporal information in Fourier domain. The signal can be analyzed either by single emitter fitting or a l_1norm constrained optimization process, which is based on dynamic properties of emitter movement. STReM has been verified using both simulated and experimental 2D data for adsorption/desorption and 2D transport. The temporal resolution has been improved roughly 20 times when comparing traditional methods to that of the novel method of STReM presented in this thesis.