# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 1 Introduction  
A full description of the carbon cycle requires detailed information on spatiotemporal patterns of surface-atmosphere carbon fluxes. One of the main carbon fluxes characterizing terrestrial ecosystems and biodiversity is the gross primary production (GPP), i.e., the amount of carbon absorbed by vegetation to perform photosynthesis, since it establishes the main carbon and energy inputs to ecosystems for producing food, wood, and fiber [1]. However, approximately half of the GPP is inspired by plants to provide the energy that supports their growth and maintenance. Net primary production (NPP) is the net carbon gain by vegetation and equals the difference between GPP and plant autotrophic respiration [2]. NPP is recognized as the most relevant variable to characterize the state of ecosystems since it represents the fundamental source of energy for all organisms in an ecosystem and is a driver of the most essential of ecosystem services [2]. The role that Earth observation (EO) data play in quantifying carbon fluxes evolved substantially in the last two decades with the beginning of the operational Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product from the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) program [3]. Sincethen, long time series of EO-based products from different models have been provided at different spatial and temporal scales. This is the case of the already mentioned MODIS products, MOD17, at different temporal and spatial resolutions [4], the daily Soil Moisture Active Passive (SMAP) Level-4 carbon (L4_C) product at 9 km [5] from NASA, or Gross Dry Matter Productivity (GDMP) product at 1 km [6] from the Copernicus Global Land Service. All of them are based on the production efficiency model (PEM) concept proposed by [PERSON] [7]. Most studies on the response of vegetation dynamics to climate change in Spain have focused until now on the NDVI time series (TS) [8] due to its well-recognized lineal relationship with photosynthesis as well as with GPP and NPP across diverse ecosystems [9]. Although the robustness of vegetation indices (VIs) along with the availability of long TS have led to a vast number of global carbon-climate studies [9; 10], VIs only have a moderate capacity to predict ecosystem structural and functional attributes. It is expected that GPP and NPP provide more information regarding the vegetation status thanks to the PAR and the light-use efficiency variables, which have shown a more rapid response than \(f_{\text{APAR}}\) to different environmental factors related to energy balance, water availability, and nutrient levels [11]. However, the carbon flux models mentioned above do not offer completely satisfactory results in Spain; some because of how they implement the water stress (MOD17 and L4_C), and others because they do not implement it (GDMP). Particularly, it was demonstrated that a different characterization of water stress, such as the one used in the current study, leads to more accurate results in the region [12] (see a summary of the methodology in Section 2.3) and, therefore, provides a better quality input for the temporal analysis. Moreover, a higher temporal variability may offer the possibility of detecting more accurately the presence of cloudy skies that leads to a carbon decrease related to the irradiation reduction and not to the vegetation type. Hence, inappropriate daily GPP and NPP characterization may result in inaccurate global GPP and NPP values at annual scales. The development of effective methodologies for the analysis of TS is one of the most important challenging issues for the remote sensing community due to the dynamic nature of terrestrial ecosystems [13; 14]. Many studies have tried to identify the best performing method among the different approaches proposed in the literature (including statistical, time-frequency-based, etc.).