## 4 Discussion
### The Use of Remotely Sensed VIs to Predict R\({}_{s}\)  
Very few significant correlations were found between VIs and R\({}_{\rm s}\), and most of the correlations we did find between R\({}_{\rm s}\) and the six different VIs were relatively weak. Seasonally, it may be difficult to correlate R\({}_{\rm s}\) with greenness VIs due to soil temperature or moisture limitation which may limit the influence of substrate supply from photosynthesis to R\({}_{\rm s}\)[32]. NDLI was negatively correlated with R\({}_{\rm s}\) for September and August. The negative correlation may be related to the fact that litter with higher lignin content is more difficult to break down by heterotrophic organisms, lowering R\({}_{\rm s}\)[33]. The reason we may have found correlations with the R\({}_{\rm s}\) rates for September and August may simply be because that time of the year has high R\({}_{\rm s}\), as seen in Figure 1.  
Remotely sensed VIs, in the past, have been more closely correlated with gross primary production (GPP) than with respiration components [34]. However, [PERSON] et al. [35] found significant correlations between EVI, GPP, LAI, and NDVI and R\({}_{\rm s}\) in subtropical forests. [PERSON] et al. [36] found strong growing season correlations between R\({}_{\rm s}\) and NDVI (R\({}^{2}\) = 0.82) and between R\({}_{\rm s}\) and nighttime surface temperature (R\({}^{2}\) = 0.80) in Canadian black spruce. [PERSON] et al. looked at estimating daytime and nighttime carbon flux using an integrated NDVI (iNDVI). Linear relationships between the iNDVI and daytime carbon flux were strong, with R\({}^{2}\) values of 0.72 to 0.92 for the three years observed. The iNDVI even correlated better with their nighttime carbon flux (R\({}^{2}\) = 0.34) than NDVI correlated with our values of R\({}_{\rm s}\) (R\({}^{2}\) = 0.07) in our study [37]. It is important to note that NDVI tends to saturate at high vegetation densities and can be sensitive to background reflectance [38]. Our sites, therefore, could have been too highly vegetated for the use of NDVI, or there was little change in GPP across our fine-scale changes in vegetation types. The above-cited studies are generally at a much broader scale than our study. VIs can be estimators of changes in biophysical parameters such as green leaf area index (GLAI), and canopy chlorophyll content (Chlcanopy). GLAI and Chlcanopy, in turn, can explain most of the variation in Rs, much like in the study by [PERSON] et al. [15]. [PERSON] et al. saw the potential to investigate how VIs may directly relate to Rs due to the strong relationship between VIs and biophysical parameters. They found strong correlations between daily mean Rs and EVI, as well as daily mean Rs and CIred edge. The study was looking at maize and winter wheat fields and the indices were calculated from hyperspectral reflectance data taken by a portable spectroradiometer [15]. The differences in land use types between our study and the one mentioned above as well as the difference in capturing the hyperspectral data, may explain the differences we saw in the ability of VIs to estimate Rs. In fact, the study mentioned that sites with evergreen species might not exhibit as drastic a seasonal change in vegetation greenness as crop fields which may make the estimation of Rs by VIs more difficult. In our study, it is important to consider the difference between the spatial scale of the data collected on site versus the spatial scale of the remotely sensed indices. In turn, this could have explained the few weak relationships we found between the two. Perhaps, our vegetation types were at too fine of a scale compared to the satellite data. A study by [PERSON] et al. explored the use of NDVI as an estimator of biomass and nutritional value of forage plants in Alaska. They wanted to explore the differences in NDVI's ability as an estimator when it was collected in two different spatial scales, from moderate resolution satellite data (250 \(\times\) 250 m) and from a handheld spectrometer (20 cm diameter area).