Satellite-based Crop Monitoring is an important tool for decision making of irrigation, fertilization, crop protection, damage assessment and more. To allow crop monitoring worldwide, on a daily basis, data fusion of images taken by different satellites is required. So far, most researches on data fusion focus on retrospective analysis, while advanced crop monitoring capabilities mandate the use of data in real time mode. Therefore, our project goals were: (1) to build a data-fusion online system for any input satellite sensor, and (2) to test its utility for the mapping of crop parameters. The methods we used in this project were field measurements, imagery processing and data fusion architecture. We ground-measured crop parameters, such as vegetation fraction, crop height and leaf-area-index in processing tomato, cotton and vineyards, respectively, during the growing season of 2016 (processing tomato and cotton in Israel) and 2016-2017 (vineyard in Australia). We processed images from multiple satellite platforms with various spectral, spatial and temporal resolutions and utilized published equations to estimate the above-mentioned crop parameters. The systems’ accuracy was evaluated in two tests. First, we calculated the errors among the spectra from different sensors for an independent dataset and the results showed no significant difference between the sensors, and most of the errors were below 10%. Secondly, we compared the crop estimations to the crop measurements and the matrix of the errors accorded with the published accuracy. These results show that data fusion of different sensors can be achieved with acceptable accuracy, allowing the monitoring of crop plots over the entire globe, with near-real-time delivery.