## 4 Discussion  
### General Experimental Framework  
This work is important because we present a general method for mapping ANPP from field data and satellite data, which can be adapted to many parts of the world. The general experimental framework we propose (which has been explored here) is outlined schematically in Figure 2, and involves the following steps:  
1. Collect field data for ANPP across a productivity gradient for the habitats in question (future work will explore the use of the satellite data for targeting field work).
2. Acquire and pre-process satellite NDVI data.
3. Search through available satellite data to identify the image which gives the best relationship with ANPP (highest R2).
4. Apply the best model to NDVI imagery to map ANPP outside the field survey plots.
5. Evaluate the derived model using a reserved validation dataset to calculate the image-specific uncertainty (RMSE) in ANPP estimates.  
This paper examined the relationship between ANPP and NDVI for a wide range of habitats (one of the few papers to do so). Our results showed that it was possible to derive a strong NDVI-ANPP relationship across a wide range of temperate habitats (R2 = 0.706); however, the method has most potential for grassland habitats (R2 = 0.833, Figure 3). Temperate grasslands are particularly well suited to estimation of ANPP from NDVI because canopy development and photosynthetic activity are in synchrony [18]. Although arable habitats were included in the analysis, and overall followed the same general patterns as other habitat types, the methodology presented here is not suitable for estimating the productivity of arable land, as this would require annually-integrated NDVI over the growing season. Moreover, there is evidence to suggest that the leaf trait variation in crop species that have been subjected to selective breeding can deviate substantially from wild species [29], which could result in less reliable trait versus productivity relationships.  
The framework developed here offers a number of advantages over existing approaches. For example, most current remotely-sensed NPP products use low resolution satellite imagery (e.g., MODIS [13]) and hence do not capture variability at the field scale. Some previous studies have used higher resolution sensors for mapping NPP (e.g., Landsat ETM+ for maize crops [15]), but this study is the first to do so across a wide range of habitat types. Through the combination of single-date NDVI imagery and extensively distributed ground-truth data, representative of a wide range of habitat types, the approach developed here allows both high resolution mapping and a regional scale product. The use of single-date NDVI imagery removes the need for frequent overpasses making high spatial resolution sensors such as Landsat and Sentinel-2 practical. Furthermore, our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface ANPP product, particularly useful for capturing spatial patterns and representing small scale heterogeneity; the resulting maps are well suited for modelling applications and without discrete jumps in parameters across habitat boundaries. Conversely, our approach allows the calibration of spectral data from satellites into physically meaningful parameters. Hence, this framework delivers a valuable product for a wide range of users that would not be possible from EO or field data alone but instead requires careful integration of these two types of data.