# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Future Research Directions  
In light of these considerations and the outcomes of the literature review, we can provide specific recommendations for researchers to incorporate into future coffee mapping studies. Object-based mapping approaches were deemed successful by the literature at many different scales ([PERSON] et al., 2017; [PERSON] et al., 2016; [PERSON] et al., 2017) and could represent a viable method. [PERSON] et al. (2017) were able to use a pixel- and object-based hybrid approach to map five different production systems, which would be incredibly useful in a landscape with different levels of canopy coverage. Despite its uses, object-based methods are most effective with high-resolution data, which drastically increases the cost and limits scalability. As freely available data continues to increase in spatial resolution however, this could become more feasible. Spectral mixture methods, which can be implemented with moderate or low resolution data, could present a more practical scalable approach. These methods were limited in the literature, however, and need further exploration. The method that seems most promising, however, is an optical/SAR fusion method using Sentinel-1 and Sentinel-2. Similar optical/SAR fusion methods have been successfully implemented in the literature ([PERSON] et al., 2017; [PERSON] et al., 2016; [PERSON] et al., 2017), and although these studies mapped other commodities and forests, they could represent an effective solution for future global coffee mapping. These approaches fulfill many of the criteria outlined in the considerations section: the imagery is low cost at a relatively high resolution, the fusion strategy leverages the strengths of both sensors without being overwhelmingly complex, SAR data is not limited by cloud cover and can observe vegetation structure, and this method is applicable across many geographies, lending itself to be a replicable approach. Although fusion methods can be more laborious to implement, the imagery is readily available to analyze on platforms such as Google Earth Engine. Additionally, we recommend incorporating ancillary data into coffee classifications for any chosen method Many studies added DEM data ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017), forest cover maps ([PERSON] et al., 2017), or precipitation and climatic data ([PERSON] et al., 2017) to their models with great success. Our review also highlights the sparsity of literature documenting remote sensing methods for mapping coffee systems. Determining the best methods and practices is inherently limited when numerous approaches have not been tested. Based on our results, we see gaps in the current research for researchers to address to fully actualize scalable, mapping methods for coffee systems. This includes the lack of research differentiating between production systems and the absence of important coffee-growing geographies. Fortunately, we see three recent advances as pivotal for advancing this space. First, the variety and availability of satellite constellations of high resolution and satellite sensors of moderate resolution improve the revisit rate, increasing the likelihood of cloud-free looks. Future research should more thoroughly explore Sentinel-2; its use, along with Landsat, represents the most practical resolution option to expand into new geographies and for large-scale coffee mapping. Second, recent investments in new sensors to measure ecosystem structure include SAR P-band and L-band SAR sensors [105], and space-borne Lidar (Global Ecosystem Dynamics Investigation (GEDI)) [109] may enable mapping and differentiation of complex shade coffee systems by measuring understory and canopy structures. Given the promising results from radar and multispectral sensor fusion approaches, particularly to map cacao systems, exploring the efficacy of these methods in mapping coffee extent is key for future research.