Remote sensing time series is the collection or acquisition of remote sensing data in a fixed equally spaced time period over a particular area or for the whole world. Near daily high spatial resolution data is very much needed for remote sensing applications such as agriculture monitoring, phenology change detection, environmental monitoring and so on. Remote sensing applications can produce better and accurate results if they are provided with dense and accurate time series of data. The current remote sensing satellite architecture is still not capable of providing near daily or daily high spatial resolution images to fulfill the needs of the above mentioned remote sensing applications. Limitations in sensors, high development, operational costs of satellites and presence of clouds blocking the area of observation are some of the reasons that makes near daily or daily high spatial resolution optical remote sensing data highly challenging to achieve. With developments in the optical sensor systems and well planned remote sensing satellite constellations, this condition can be improved but it comes at a cost. Even then the issue will not be completely resolved and thus the growing need for high temporal and high spatial resolution data cannot be fulfilled entirely. Because the data collection process relies on satellites which are physical system, these can fail unpredictably due to various reasons and cause a complete loss of observation for a given period of time making a gap in the time series. Moreover, to observe the long term trend in phenology change due to rapidly changing environmental conditions, the remote sensing data from the present is not just sufficient, the data from the past is also important. A better alternative solution for this issue can be the generation of remote sensing time series by fusing data from multiple remote sensing satellite which has different spatial and temporal resolutions. This approach will be effective and efficient. In this method a high temporal low spatial resolution image from a satellite such as Sentinel-2 can be fused with a low temporal and high spatial resolution image from a satellite such as the Sentinel-3 to generate a synthetic high temporal high spatial resolution data. Remote sensing time series generation by data fusion methods can be applied to the satellite images captured currently as well as the images captured by the satellites in the past. This will provide the much needed high temporal and high spatial resolution images for remote sensing applications. This approach with its simplistic nature is cost effective and provides the researchers the means to generate the data needed for their application on their own from the limited source of data available to them. An efficient data fusion approach in combination with a well planned satellite constellation can offer a solution which will ensure near daily time series of remote sensing data with out any gap. The aim of this research work is to develop an efficient data fusion approaches to achieve dense remote sensing time series.