Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR  
[PERSON]  
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and productivity on the Canadian Prairies [3; 8; 10; 14; 15; 16; 17]. Based on long-term climate datasets, [PERSON] _et al._[5; 11] analyzed the long-term variation of agro-climatic indices which are important for agricultural planning and management. They observed significant trends in agro-climatic indices (e.g., earlier start and delayed end of growing season) in Canada. Of all the agro-climatic factors, water-related factors or drought indices have played the most important role in variability in crop growth and productivity in the Canadian Prairies [2; 3; 8; 18; 19]. For instance, [PERSON] _et al._[19] found that drought indices, especially water demand (evapotranspiration, ET) and the water balance index, can explain between 27% and 74% of the variation of wheat yield (\(p<0.05\)) in the Canadian Prairies over the period 2003-2006. In addition, efforts to explore crop yield variation under future climate scenarios have been made using crop models and climate models [16; 20; 21]. Although these studies have greatly improved our understanding of the impact of climate change on crop growth, most of them were conducted based on data from sparse meteorological stations and annual surveyed crop yield. The results were unable to demonstrate the spatial heterogeneity associated with the relationship between climate indices and cropland productivity [22].  
Vegetation indices and biophysical variables derived from remote sensing data, such as the Normalized Difference Vegetation index (NDVI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), show a strong capability to monitor spatial-temporal variation of crop growth status and assess cropland productivity at the regional or global scale [4; 12; 13; 23]. Over the past decades, a few long-term earth observation datasets have been made available, such as the TM/ETM+/OLI from the Landsat series satellites, the Advanced Very High Resolution Radiometer (AVHRR) from the NOAA satellites, the Moderate Resolution Imaging Spectroradiometer (MODIS) from the Terra/Aqua satellites, and the VEGFATION from the SPOT satellites [24; 25]. These long-term datasets are useful in exploring how cropland productivity responds to climate variation (e.g., rainfall and temperature) and management practices [23; 26]. For instance, [PERSON] _et al._[4] found that crop biomass showed a strong positive correlation with growing season length (GSL) and a negative correlation with delays at the start of season (SOS) in Sahel. Based on time series NDVI, [PERSON] and [PERSON] [26] suggested that the trend of crop yield in Senegal was not only affected by rainfall but was also significantly influenced by changes in land cover and land use. [PERSON] _et al._[22] observed a declining trend in the relative growth rate of grain production in water-limited tropic countries (e.g., India) using the annual sum of the 12 monthly values of maximum NDVI. These studies indicated that long-term satellite data with their good spatial coverage could be useful in investigating the relationship between crop growth and climate variability. To date, in Canada, most studies have focused on estimating crop yield using remote sensing technology [12; 13; 27; 28; 29], while few studies have paid attention to the crop yield variation analysis using time series of remote sensing observations.  
The main objectives of this study were: (1) to analyze the spatial and temporal variation of cropland productivity on the Canadian Prairies from 2000 to 2013 using satellite data; and (2) to explore the effects of growing season climate variables including temperature, precipitation, and radiation on the cropland productivity derived from satellite data.