Solar-Induced chlorophyll Fluorescence (SIF) is a functional proxy of Gross Primary Production (GPP), and it is crucial to monitor the global or regional vegetation productivity and climate change. However, there is no available original dataset with global continuous coverage at high spatial resolution. Although there are some reconstructed datasets, but they are not specific enough to a region of interest. This disadvantage thus will limit their application for research related to SIF in such a region of interest. In order to explore the method to reconstruct SIF dataset in a region of interest, we built models on MODIS reflectance data and original OCO-2 SIF, combining machine learning, remote sensing technology and ecological principles. The reconstructed SIF dataset was built based on the spatial-temporal features with contiguous spatial coverage and higher spatial resolution. Based on the validation performance, this framework is capable for providing efficient and specific SIF data for a region of interest, and it can support the research related to SIF in this area.