## 4 Discussion  
Our results show that CFAPAR, a seasonal indictor of green area formation [38], has a strong linear correlation with the cropland NPP (\(r=0.60\), \(p<0.01\), \(n=513\)). This is consistent with literature studies [4; 32; 38; 59]. For example, [PERSON] _et al._[4] found that the correlation coefficient (\(r\)) between the measured herbaceous dry biomass in Senegal and the CFAPAR retrieved from the SPOT-VEGFATION was 0.58, and [PERSON], _et al._[38] found that CFAPAR had a stronger correlation with official yields under water-limited conditions than that of non-water-limited areas. The main reason is that FAPAR plays an important role in photosynthetic process [30; 31; 38]. Thus, CFAPAR, which has a much higher spatial resolution than that of crop yield statistics, could be used to demonstrate the influence of climate variables on cropland productivity at different spatial-temporal scales. However, a larger portion of the NPP variability was accounted for by the accuracy of CFAPAR. The contribution factors to CFAPAR variability may include, but are not limited to, (a) the uncertainties in MODIS FAPAR product caused by mixed pixels and retrieval algorithm [32; 42; 60]; (b) a general cropland mask  
Figure 12: The coefficient of variation (CV) about the temporal variation of annual CFAPAR during the period of 2000–2013. Figure 13: Linear relationship between the coefficient of variation (CV) of CFAPAR and that of rainfall. Samples located inside cropland mask were randomly selected using the Create Random Points tool of ArcGIS software. obtained from the MODIS land cover product [43]; (c) the uncertainties in the extracted phenological metrics [4]; and the modeling errors in converting census crop yield to NPP [37; 50], especially the former two factors. The results in this study also showed that cropland productivity had an increasing trend, especially in AB and SK (Figures 6 and 7). This increasing trend has been attributed to the contribution of technology improvement [12]. Furthermore, the influence of cropland variability on cropland productivity was negligible in this study as the crop area data from Statistics Canada ([[http://www.statcan.gc.ca/start-debut-eng.html](http://www.statcan.gc.ca/start-debut-eng.html)]([http://www.statcan.gc.ca/start-debut-eng.html](http://www.statcan.gc.ca/start-debut-eng.html))) showed that the variability of total cropland area on the Canadian Prairies was quite stable from 2000 to 2013. For the cropland productivity variation analysis, cropland productivity located in the semi-arid and arid zones showed strong variation (Figure 12). Our results are consistent with [PERSON], _et al._[12] who analyzed the variation of the surveyed yield from Statistics Canada over 1985-2012 at the CAR level. Further analyses about the relationships between CFAPAR and climate variables suggest that rainfall has played a key role in the interannual spatial-temporal variation of cropland productivity. Rainfall had a smaller effect on cropland productivity variation in MB than in AB and SK (Figures 9 and 11). A major reason is that annual cumulative rainfall in MB is usually ample (Figure 4a) and its variation is smaller (Figure 12) than that in the other two provinces. The analysis of the spatial relationships between cropland productivity and rainfall suggest that rainfall could account for about 50% of the cropland productivity variation. Thus, it can be concluded that higher cropland productivity variation in the Canadian Prairies is often accompanied by higher variability in rainfall. Cropland productivity was also observed to be negatively affected by temperature in some areas of the Canadian Prairies, which is consistent with results of other researchers, who have indicated that water stress is one of the major yield limiting factors in the Canadian Prairies [2; 3; 6; 8; 15]. This also further confirms why the CFAPAR in the Canadian Prairies in the years of 2001, 2003, 2006, 2009 and 2012 were below the corresponding trend line (Figures 7 and 8).