# Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach
## 6 Conclusions  
The annual GPP has been estimated at 1-km spatial resolution from the MODIS standard VI products (NDVI and EVI) and the semi-empirical model, Equation (4), with \(C_{1}\) and \(C_{2}\) obtained, as a function of the vegetation type, as the coefficients of the linear regression between the reference annual GPP--calculated using Equation (2) with daily GPP data [4]--and the PAR-weighted average VI (Equation (7)).  
The main advantage of the semi-empirical model to estimate annual GPP is that it does not require actual meteorological data, thus solving a problem that is often faced, especially in retrospective studies. The model exclusively relies on a vegetation index product commonly available--the MOD13A2 in this case--and on a representative PAR (calculated for a time period sufficiently long although that might not be the same as the period when GPP is estimated). The calibration/validation process, however, does require a reference GPP product and, obviously, the higher the quality of the reference GPP product, the better the performance of the semi-empirical model.  
The performance of the model has been assessed in its ability to reproduce inter-annual variations of GPP in close agreement with those shown by the reference data. A total of eight essays have been performed using all the possible combination among the model driven by: NDVI or EVI, with or without vegetation map, and using as reference a previous GPP product generated by the authors [4] or the MODIS GPP product. The different statistics (Table 6) have shown a good agreement between the estimates and the reference data sets, with correlation coefficient between 0.84 and 0.91 and relative RMSE between 20% and 30% in all the cases, enhancing the robustness of the method and its applicability to other regions and potentially to other sensors with similar o lower spatial and temporal resolutions. However, special attention must be given to its applicability in semiarid areas, where the semi-empirical model overestimates the annual GPP, and in very dense forest areas, where the model slightly underestimates GPP. In the latter case, uncertainties are lower when using EVI instead of NDVI. Besides improving vegetation monitoring over high biomass conditions, EVI offers some other advantage: its performance does not depend on a vegetation map and hence this map is not a crucial requirement to apply the methodology.  
With the above limitations, it is concluded that the proposed semi-empirical model provides an excellent compromise between simplicity and accuracy for the calculation of long time series of annual GPP.  
This work has been partially supported by ESCENARIOS (MINECO, CGL2016-75239-R), RESET CLIMATE (MINECO, CGL2012-35831) and LSA SAF (EUMETSAT) projects. Special thanks are due to [PERSON] for their valuable suggestions. The MOD13A2 and MOD17A3 products were retrieved from the online Reverb, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (reverb.echo.nasa.gov). The authors would like to thank the anonymous reviewers for their valuable comments. All authors contributed equally to the development of this research.  
The authors declare no conflict of interest.