# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 4 Discussion  
The moderate performance of the NDVI index to directly predict GPP is a surprise, since it has been shown to perform well using satellite data [10, 13, 16]. However, in [1, 18] the direct NDVI-GPP correlation is moderate. A Landsat-7 study [17] shows good performance of NDVI, but also indicates that the NDVI exhibits limitations in GPP estimation at moderate to high vegetation densities. In [1], a study in maize using MODIS imagery shows that VIs (NDVI, SAVI, EVI, WDVI) are moderately linearly related to GPP, with NDVI being the worst. This study also shows that a good correlation between the VIs and the GPP can be obtained using a non-linear relationship including the PAR, as follows: VI \(\times\) VI \(\times\) PAR. Related research [20] uses VI \(\times\) PAR with moderated correlations for remote sensing images. In [10], another study in maize using Landsat-8 shows better results using the VPM. The EVI has been amply used to estimate GPP with good correlation [10, 14, 17, 18, 21] as well as the EVI2 [28], coinciding with our good results here with EVI2. A moderate correlation of EVI with GPP has been shown in [1], and good-to-moderate performance was shown in [18]. We obtain a good correlation between SAVI and GPP, and similar to the results indicated in [1]. We also obtain a good correlation between MSAVI and MSAVI2 to GPP, with MSAVI being the best. The MSAVI indexes have not been reported in the literature as showing a good correlation with GPP, so they should be included in further studies. The simple DVI and WDVI showed good correlation with GPP on the 11 spectral samples and, surprisingly, were a bit better than the best index NIRv for estimating the accumulated GPP. The novel NIRv has been shown to have a very good correlation with GPP in [19, 28], and was the best VI at the 11 temporal samples taken. It is interesting to note that the NIRv is the product of the scene NIR and the NDVI, and that this simple correction of the NDVI overcomes the limitations of the NDVI, agreeing with our results. Contrary to the results indicated in [17, 18, 20], the Clgreen, the Clred-edge, the RENDVI, the MCARI, the MTCI, and the WDRVlgreen show no correlation at all with GPP. As indicated in [1, 2, 10, 12, 16, 18, 20, 21, 22], empirically-based VI models using additional information to the VIs provide, in general, better correlations between VIs and GPP than VIs alone, and LUE-based models perform even better than empirically-based VIs. Our results are good compared with the general results using satellite imagery and simple vegetation indexes. However, they can be improved using more complex models that can obtain R\({}^{2}\) scores well above 0.9 to better predict the daily GPP during a crop's lifecycle.  
These results are an important first step to being able to routinely monitor carbon uptake on carrot crops on regions that are highly covered by clouds. Other crops which are highly cultivated in the area, such as potatoes, could also be studied to develop GPP-VIs linear models, as we accomplished here, to monitor carbon uptake using high-resolution, cloud-free multispectral imagery acquired with UAVs.