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Net primary productivity (NPP) estimation is an important study field of global change and terrestrial ecosystem. Here, we present an integrated modeling and analysis framework for researching climate change using NPP as an ecosystem indicator. It's brings together remote sensing, GIS and an ecosystem simulation model without requiring users to have any knowledge of any other specific software. Satellite data driven architecture can play a positive role in understanding the climate change and carbon cycles from regional to global scales. The benefit of the framework is that it provides complete spatial coverage and high temporal resolution, versus plot samples from which it may be difficult to provide an overall assessment for large-scale study. Recent advances in service-oriented architectures are allowing us to share of model results by geospatial services and create distributed applications needed for the collaborative research. Multi-layer approaches allow the framework more flexible and easy to extend. New datasets and models can be integrated for use in the development of new applications. A preliminary system based on the framework was carried out for monitoring the dynamic changes of terrestrial ecosystem NPP in China. State-of-the art processing algorithms was developed based on light-use efficiency model (Carnegie-Ames-Stanford Approach (CASA) model). Fraction of photosynthetically active radiation absorbed by vegetation (FPAR) is a linear function of Normalized Difference Vegetation Index (NDVI) and Simple Ratio vegetation index (SR). We combined the method of NDVI and SR together to compute the FPAR of China. And then, the NPP time-series of Chinese terrestrial vegetation is built. The System is capable of querying, retrieving, and visualizing datasets with heterogeneous formats and the spatio-temporal analysis revealed the relationship between NPP and climatic change in China. These results supported national trends of NPP in relation to lag effects of- - climate.