## 1 Introduction  
Isoprene comprises the largest fraction of globally emitted biogenic volatile organic compounds (BVOCs). Isoprene emissions are of great importance in plant biology and ecology and they can substantially influence atmospheric chemistry and composition, and processes of the climatic system [1]. For example, isoprene plays a role in the formation of photochemical smog, ozone and other secondary pollutants such as peroxyacyl nitrates and secondary organic aerosols [2; 3; 4] and has implications for regional air quality and global climate change [5]. The quantification of isoprenoid emissions at the ecosystem level with available models and field measurements is not entirely satisfactory. Isoprene emissions at canopy to regional levels can be directly measured by applying techniques of eddy covariance [6; 7; 8], but these measurements are limited to a few sites. Isoprenoid emissions can also be estimated using models based on capacities of foliar emission, such as the Model of Emissions of Gasses and Aerosols from Nature (MEGAN) model [9], with algorithms based on foliar emissions that are driven by temperature and light [10]. Model simulations, however, remain unsatisfactory because foliar-emission capacities are highly variable and acclimate seasonally and over environmental gradients [11; 12]. For example, MEGAN was not able to detect the peak of emissions concurrent with episodic and extreme droughts [13; 14]. A reassessment of current models is therefore required to better predict changes in the seasonal dynamics of the capacities of both basal and total isoprenoid emissions, especially under the increasing occurrence of drought stress [15]. Current efforts are now being made to base the modeling on a fundamental understanding of the links between emissions and the biological processes that affect these emissions [11; 16; 17; 18; 19], but uncertainty remains high [16; 17]. Remote-sensing techniques have the potential to offer quick and intensive monitoring of spatial and temporal isoprene emissions at the ecosystem level with global coverage. Various approaches have been developed for remotely sensing isoprenoid emissions, such as indirect estimation by the remote detection of formaldehyde, a product of isoprenoid oxidation, in the atmosphere [20; 21; 22]. This approach, however, is based on highly uncertain assumptions associated with the oxidant chemistry of isoprenoids to formaldehyde [21; 23] and require a model of atmospheric chemistry to apply satellite observations to isoprene emissions. A new simple approach has recently been suggested for estimating isoprenoids using remotely sensed data [24]. This approach is a descendant of the model proposed by [PERSON] et al. [18] that [PERSON] et al. [19] adapted for implementation into regional and global models. It is based on the unifying model for isoprene emission by photosynthesizing leaves, which hypothesizes that isoprene biosynthesis depends on a balance between the supply of photosynthetic reducing power and the needs of carbon assimilation [25]. Assuming that photosynthetic reducing power for isoprenoid production was higher under lower light-use efficiencies (LUEs) [25; 26; 27], [PERSON] et al. [24] found that the photochemical reflectance index (PRI), a proxy of LUE, at the foliar level together with factors of basal emission could predict isoprenoid emission similarly to some standard emission models, and combined with these models improved their predictions of isoprene emission [24].