## 1 Introduction  
Terrestrial gross carbon uptake, expressed as gross primary productivity (GPP), and its response to climatic changes play a key role in projections of future carbon cycles and climate [1, 2]. Increasing attention has been concentrated on accurately and continuously quantifying and modeling GPP over large regions and long timescales [3, 4, 5]. Observations of vegetation primary productivity in situ, and empirical [6] or process-based [1, 7] models have been successfully used to estimate the global distribution of GPP. These measurements, however, rarely provide high-quality data and contain errors originating from the uncertainties of field work, which impede a comprehensive understanding of the global terrestrial carbon cycle [2, 7]. Remotely sensing carbon uptake provides a unique opportunity for extending the spatial coverage of carbon fluxes [2; 8; 9; 10; 11]. The quantification of GPP variation by remote-sensing techniques is generally based on a model of radiation-use efficiency (RUE) [12; 13]. This model mainly considers the absorbed photosynthetically active radiation (APAR) and the actual photochemical efficiency [12; 13]. APAR has been extensively analyzed and is usually derived from vegetation indices of greenness such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) [10; 14; 15; 16; 17]. The remote sensing of RUE, however, has been less extensively analyzed, perhaps because it is influenced by many factors [10; 18; 19; 20]. RUE is commonly remotely estimated using the photochemical reflectance index (PRI), which is a proxy of ecophysiological parameters linked to the competition between energy dissipation and photochemical conversion [11; 18; 19; 20; 21; 22; 23]. Reflectance at 531 nm rapidly decreases in response to the dissipation of excess energy by xanthophyll de-epoxidation due to an increase in zeaxanthin concentration and to chloroplast shrinkage following an increase in thylakoid \(\Delta\)pH, which is insensitive to short-term changes at 570 nm [18; 19]. PRI is thus defined as (R531 \(-\) R570)/(R531 \(+\) R570), where R is the reflectance and the numbers are central wavelengths of narrow bands in nanometers. The remote sensing of photosynthetic performance and plant stress has advanced considerably over the last two decades [9; 11; 20; 24], with tower- [25], aircraft- [26; 27] and satellite- [9; 28; 29; 30; 31] based PRI at both short (hours to days) and long (days to seasons) terms since [PERSON], [PERSON] [18] and [PERSON] et al. [19] proposed this reflectance index. The short-term change in PRI associated with the xanthophyll cycle, as proposed by [PERSON] et al. [18] and [PERSON] et al. [19], is able to track photosynthetic performance and RUE, the actual or maximum photochemical efficiency of photosystem II (PSII) and non-photochemical quenching (NPQ) over a wide range of species, plant functional types and nutrient levels at foliar, canopy and ecosystem levels under various stress conditions (e.g., water shortage, nutrient levels, disease and contamination) [19; 20; 23; 28; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. PRI has also been useful for studying aquatic vegetation [47], mosses [48; 49; 50] and lichens [51]. Photosynthetic activation can be efficiently tracked by PRI, particularly during special seasonal periods of alteration such as spring recovery in coniferous [52; 53; 54; 55], and across years in boreal deciduous forests [56], Mediterranean holm-oak forests [29] and shrubland [22]. PRI obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) [25; 29; 56; 57; 58], AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) [59] and CHRIS/PROBA (Compact High Resolution Imaging Spectrometer launched aboard the Proba satellite) [60] can also track changes in photosynthetic performance and primary productivity at larger spatial scales.