## 4 Discussion  
The development and application of a workflow to derive an empirical model of gross primary productivity from remote sensing imagery have been demonstrated at Donana National Park wetlands. The model has been upscaled for the estimation of GPP in the marsh area surrounding the EC tower. The generated monthly maps of GPP have the potential to provide detailed information on the patterns and dynamics of primary production. The model selection led to a linear regression model driven by the remote sensing based red-edge chlorophyll index CLr in combination with the rainfall data. This confirmed previous research outcomes on the sensitivity of the red-edge to canopy biomass, chlorophyll content and photosynthesis activity [76; 77; 78] and thus on the enhanced estimation of the vegetation biophysical characteristics [33; 79; 80]. In general, analyzing each VI proposed in the methodology, we observed that the greenness-sensitive VIs performed the best. Narrowband greenness VIs also showed good performances. The EVI and EVI-2 resulted to be more sensitive than NDVI, confirming that EVI can improve the estimation of productivity in areas with dense vegetation being less prone than NDVI to noise caused by soil and atmospheric effects [81; 82]. Water-sensitive VIs, such as the LSWI and the MNDWI, did not show a high correlation with in situ GPP. Previous researchers have shown that these indexes are more sensitive to drought [34; 83] than greenness-sensitive VIs and thus we suggest further investigation of their performances during dry phases and in ecosystems undergoing seasonal droughs. The integration of satellite-derived VIs with environmental variables in the model formulation increased the correlation with in situ GPP. Particularly, rainfall data increased the model performances confirming that the biomass production of the Donana marsh ecosystem is strongly dependent on precipitation [58]. As a result of the high exploitation of water resources in the proximities of the Donana National Park, the current flooding regime of the natural marsh is mainly determined by local precipitation [16]. The wetlands' size and depth change remarkably between years, driven mainly by the variability in the precipitation [48], indicating that the flood levels and associated primary productivity in the ecosystems also correlate with the precipitation regimen. The association has also been supported by other authors studying species distribution stating that precipitation is the main driver of primary productivity and associated support of habitats for waterbirds in the ecosystem [84]. The phenological cycle of the vegetation of the study area is also well reproduced, confirming the choice of our model predictors. [48; 58] report that the biomass and associated photosynthesis activity observed through the total intake of carbon dioxide or GPP is low during the start of the rain season (October-December) and the initial stage of the more intense precipitation and flood season (January-February). Then, the GPP content increases in the middle of the wet season when the water availability in the ecosystem reaches a maximum (February-March) [58] and continues to increase till the dry season when GPP starts to decrease. During the months with higher productivity, the vegetation contributed to 81% of the total GPP produced during the entire period. This pattern can also be observed in the generated monthly GPP maps. Given the lack of validation data outside the EC tower, we attempt to validate the derived products at the location of the EC tower. Although the empirical regression model was generally accurate, an overestimation of the monthly GPP was observed when upscaling the method. Smaller time windows were also considered to check the influence of the window size on the model results. The model performances did not improve significantly; however, the smaller time window allowed for a better discretization in time and the identification of GPP variation within the months.