## 5 Conclusions  
This paper has demonstrated an alternative approach for the operational regional-scale mapping of vegetation productivity at high spatial resolution, allowing between- and within-field spatial patterns in ANPP to be observed. A combination of NDVI imagery and ground-based ANPP estimates were used to develop models which were then applied to predict ANPP values outside of field survey squares. Our approach differs from other empirical methods as it uses widely-distributed, stratified field data to ensure that the derived models can be applied over large spatial scales. Furthermore, we use single-date rather than annually-integrated NDVI so that the high-spatial resolution sensor Landsat 8 can be utilised, despite its limited revisit frequency and high levels of cloud cover in the study area. Our methodological framework is limited by the need for in situ data for model calibration, however, we have demonstrated that ground-truth ANPP values can be estimated from simply measured plant trait variables. The increasing requirement for countries to keep track of their natural capital will result in similar spatially representative vegetation plot data becoming available for other countries, as well as better links between field and EO monitoring, as demonstrated here, rather than reliance on large scale models which can be meaningless for management. The method generates a high-resolution product that would not be feasible with LUE-based approaches, as these require meteorological data which are only available at coarse scale spatial resolution; hence, our approach offers significant advantages for applications that require fine-scale information such as for habitat condition monitoring. The timing of the NDVI images determined the strength of the relationship, with results from a narrow temporal window providing the most accurate ANPP estimates. The optimum time period occurred during the spring green-up when within-habitat variability in NDVI was low and between-habitat variability was high. The exact timing of this green-up will vary from year to year [38]; however, our approach accounts for this by searching for images that give the best agreement between the ground-truth data and the satellite data. This study investigated the NDVI-ANPP relationship across a broad range of temperate habitats and strong relationships were achieved (R\({}^{2}\) = 0.706), which were higher when only grasslands were considered (R\({}^{2}\) = 0.833). When tested against the validation dataset, the derived models were able to predict ANPP from NDVI imagery with an RMSE of 15-21% (Figure 8). The approach developed here for high resolution ANPP mapping has a huge range of potential applications and it opens up new ways of representing the landscape as a continuous surface, providing a valuable product for users and in particular for modelling applications. Our methodological framework, demonstrated here for temperate regions, has potential to be applied to any terrestrial ecosystem with a pronounced green up period, such as tropical grasslands; however, further work would be needed to confirm its reliability in these regions. Utilizing this method in different locations and for other time periods would require sufficient NDVI imagery during the vegetation green up period and widely distributed, representative ground-based ANPP estimates, in order to derive a robust relationship. Application of this method is limited by the availability of cloud free imagery; however, the launch of new satellites such as Sentinel-2A (2015) and 2B (2017) has increased availability of suitable imagery, making annual ANPP maps feasible in future. **Acknowledgments:** Fieldwork and preliminary analysis were funded by GMEP (Glastir Monitoring & Evaluation Programme) (contract reference: C147/2010/11) and NERC/Centre for Ecology & Hydrology (CEH Project: NEC05782). Remaining analysis and writing of the paper were funded by NERC/CEH National Capability Project\"Land Cover Monitoring with Satellites\" (NEC05567).