## 1 Introduction  
Terrestrial organisms, including humans, are dependent upon the fixation of carbon from the atmosphere into accumulated dry matter per unit area. The rate at which this occurs is Net Primary Production (NPP). This is a fundamental ecosystem function that supports food production, climate regulation and the formation and stability of soils, and has also been mechanistically and empirically linked to various forms of biodiversity [1; 2]. Modelling, mapping and prediction of NPP at fine-resolution but across large domains is important because of human dependence on NPP. For example, an estimated 28.8% of global NPP [3] is appropriated by humans as food, fibre and fuel. NPP is, however, difficult to measure and predict accurately [4; 5] but there is a pressing need to do so because global change drivers such as land-use, climate change, novel pests and atmospheric pollution are all likely to change NPP in the coming decades. Here, we combine remotely-sensed data with ground-based measurements of annual above-ground NPP (ANPP) and associated plant traits to develop and test a new capability for estimating terrestrial ANPP at fine resolution. The advantage of such an approach is that ANPP can be estimated at spatial resolutions fine enough to accommodate variation imposed by land-use across farmed and fragmented habitat mosaics. This is in contrast to models that simulate NPP at larger grid cell resolution and then apportion sub-grid NPP in a non-spatially explicit way between Plant Functional Types (e.g., [5]). We focus on ANPP rather than total NPP due to practicalities of measurement and because this is the fraction that is most readily consumed by humans as food, timber and fibre and that contributes to and impacts above-ground biodiversity [1]. Furthermore, ANPP is a sensitive response variable to land use and climate change. While the need for a high-resolution ANPP product is clear, this work was also motivated by the demand for new approaches for representing the landscape to produce products that meet the needs of a wide range of users. Traditional remote-sensing classification is a categorical process that identifies a single land cover type for each pixel or parcel of land (e.g., [6]), rarely capturing the complexity of the landscape. Fuzzy classification methods capture heterogeneity by identifying a number of classes for a particular pixel or parcel [7; 8]. This enables a more sophisticated description of the spatial distribution and co-occurrence of different classes, but is still categorical and fails to capture within-class variability. For example, both the traditional and fuzzy classifications would map a large area of grassland spanning an environmental gradient, as a single land-cover type, despite vegetation productivity varying widely. For some applications, this lack of sensitivity to environmental condition/quality may be acceptable, however, users of environmental datasets are increasingly demanding a more nuanced picture of the landscape to enable remote sensing to routinely be used to monitor changes in both the extent and condition of land cover/habitat [9]. To meet these emerging requirements, novel methods are required to enhance and supplement traditional land cover products. Continuous field biophysical products provide an alternative approach to representing the land surface [10; 11; 12]. The advantage of these products is that they: (i) capture within-class variability, so gradients in grassland productivity across a specific field will be mapped, as will the wider variations across a region, or across different regions; and (ii) are a key requirement of condition monitoring and early detection gradual of land cover change. Methods for remote sensing of ANPP have been investigated extensively, mainly at a small-scale (i.e., site-level) or at a global scale from MODIS data [13]. The extreme difference in the two spatial scales (site compared to global) is due to the different methods underpinning estimation/modelling of NPP.