## 1 Introduction  
Regional to global scale crop monitoring and yield forecasting are important for agricultural management and food security [1; 2]. Satellite remote sensing enables assessment of agricultural crop growth and yield across large territories [3; 4; 5; 6; 7]. Various Biomass Proxies (hereafter BPs) have been developed from remote sensing imagery to be used in empirical regressive models that monitor agricultural crop growth and estimate crop yield [8; 9; 10]. NDVI, fAPAR, LAI (Leaf Area Index), GAI (Green Area Index) and EVI (Enhanced Vegetation Index) are examples of BPs that have been derived from remote sensing and that are used in vegetation monitoring and crop yield forecasting [5; 7; 11; 12; 13; 14; 15; 16]. Though relationships between BPs and yield have been established, few studies relate Dry Matter Productivity (DMP) to crop yield [17; 18]. Vegetation productivity can be defined in several ways. Gross primary productivity (GPP) is the rate at which plants capture and store atmospheric carbon dioxide (CO\({}_{2}\)) to generate oxygen and energy as biomass [19]. Net primary productivity (NPP) is the difference between GPP and the energy lost during plant autotrophic respiration. NPP is thus the rate of atmospheric carbon uptake through the process of photosynthesis and represents the daily accumulation of standing biomass. DMP is analogous to NPP, but expressed in different units (kgDM/ha/day instead of gC/m\({}^{2}\)/day), for agro-statistical purposes [20]. The efficiency of the conversion between carbon and dry matter is on average 0.45 gC/gDM [21]. Three types of models are used to estimate the photosynthetic carbon uptake and understand its spatio-temporal variability [22; 23; 24]. The first group consists of empirical models often with a limited applicability outside the area where they have been calibrated. The second group of models is based on major biophysical and biochemical processes of photosynthesis and respiration measured under laboratory conditions [25]. These models have a high computational demand. The third group of models are parametric models driven by remote-sensing-derived variables and weather data, calibrated with flux monitoring. Constant parameters are used to link measurements to biophysical processes rendering these models particularly suitable for the local scale. While such assumptions may be difficult to hold at global scale, parametric models may offer a balance between simplicity and process description [23]. Production efficiency models, such as Monteith parametric models, have been developed to monitor the primary production of vegetation [26; 27]. [PERSON] suggested that crop growth under non-stressed conditions linearly correlates with their Radiation Use Efficiency (RUE) times the amount of Absorbed Photosynthetically Active Radiation (APAR) [28; 29]. According to a review of experimental studies [30], RUE values for C\({}_{3}\) species range from 1.32-3.50 gDM/MJ of intercepted PAR and for C\({}_{4}\) species from 1.48-4.32 gDM/MJ of intercepted PAR during different crop development stages. The seasonal variability of photosynthetic activity, however, depends on environmental constraints [22]. For example, RUE is negatively related to water stress and positively related to temperature for annual crops [22]. Water stress has been estimated as a function of soil moisture [31; 32], water deficits [33] or satellite-derived land surface water index [34]. Production efficiency models have been widely used to estimate regional or global carbon balances in crops, grasslands and forests due to the simplicity of the RUE concept and the availability of remotely sensed data [17; 31; 32; 35; 36; 37; 38; 39; 40; 41]. However, their performance has been shown to vary in describing the carbon budget [42].