## 4 Discussion
In July, the SPOT NDVI (NDVI\({}_{\text{S}}\)) overestimated ground NDVI in 2009 and underestimated in 2010, particularly for Sounding Creek (Figure 5), which is made clearer by comparing the regression with the 1:1 line (Figure 9). This greater scatter in the SPOT NDVI than in the MODIS NDVI would make simple correction for the offset much more difficult and suggests SPOT would be less reliable than MODIS for monitoring variation in rangeland yield. In our study, the utility of the SPOT platform for monitoring year-to-year variability was limited by the inadequate time series capability resulting from restricted temporal coverage along with periodic cloud contamination. ATIC (the local SPOT data distributor) did not distribute SPOT images with greater than 10 percent cloud cover within the image; consequently, cloud-freeSPOT imagery was unavailable for Sounding Creek in both June and August 2010 (Figure 5), making extensive, direct comparisons with MODIS and ground NDVI difficult. This reduced availability of SPOT data, combined with its high cost, precluded a fully equivalent sensor comparison. Some studies [24] have suggested that SPOT is superior to MODIS for rangeland monitoring due to its higher spatial resolution. SPOT satellite imagery has a spatial resolution of 30 m, which resulted in decreased temporal resolution relative to MODIS, as the smaller field of view requires more orbits of the Earth to cover the same position [42]. In comparison to the daily coverage by the MODIS platforms, the 26-day temporal resolution of SPOT appeared to be inadequate to ensure repeat coverage of an area with data quality comparable to MODIS. While MODIS sensors have lower spatial resolution, the daily coverage allows creation of eight-day composites (employing maximum value compositing and bidirectional reflectance distribution function correction [37]), so errors due to clouds and other effects are reduced. The SPOT sensor can be directed off-nadir to increase the temporal coverage [24]; however, this introduces error due to altered sensor angle [42]. Based on the results of our study, we conclude that the MODIS sensor may actually be more effective than SPOT for mixed prairie yield monitoring and management, particularly when continuous time series are required over large areas. A number of factors can introduce variability into the NDVI-biomass relationship. For example, scatter in the NDVI-green biomass relationship can be a result of different canopy architecture, which has an effect on NDVI [43], and this may lead to seasonal progression in the NDVI-biomass relationship (Figure S1). In May, the variation between NDVI and biomass is partly due to vegetation carryover from the previous year masking new growth. At the beginning of the growing season there had been little or no new green growth, with the initial growth likely to be hidden by the last season's biomass (the carryover error). The greater variability in August is likely due to senescence, which causes a decline in chlorophyll content (which would affect NDVI); this gradually changing greenness was not fully considered by our harvesting method, which distinguished between green and brown vegetation but did not consider the varying degrees of green associated with changing pigment content at different growth and senescence stages. The effects of dead biomass on reducing the NDVI correlations with biomass are likely to be strongest at the beginning of the season (due to the \"carryover\" of dead standing biomass from the previous year) and towards the end of the season, when vegetation senescence has begun [44; 45]. Our seasonal results could also have been influenced by the different species composition in the two townships, with Pinhorn having greater amounts of drought-resistant C\({}_{4}\) graminoids than the C\({}_{3}\) species dominant in Sounding Creek [46]. Together, these effects undoubtedly contributed to scatter in the NDVI-green biomass relationship when all seasons and sites are combined (\(R^{2}\) = 0.85, Figure 6D).