# Review of Remote Sensing Methods to Map Coffee Production Systems
## 5 Conclusions  
Routine mapping of coffee systems remains a critical need to understand how coffee drives landscape dynamics and, thus, develop pathways to ensure that coffee is a sustainably produced global commodity. Our literature review highlights the variety of approaches and sensors that researchers have previously employed to accurately map coffee, to inform future coffee mapping studies on the most effective methods for replicable mapping of diverse coffee systems. The most common methods were pixel-based methods for classifying both high- and moderate-resolution optical imagery. We did not identify any routine methods that produced consistently accurate results, thus making a specific method recommendation challenging. We also found significant gaps in the literature, including mapping coffee using other sensor types, such as SAR, studies differentiating between multiple production systems, and the scant published literature on mapping coffee in geographies vital to coffee production, such as Vietnam and Ethiopia. Therefore, we use the results of our literature to recommend the following considerations for choosing a method for future coffee mapping studies: minimizing the cost of imagery, balancing the accuracy of a method with the complexity of the analysis, identifying methods that have been applied successfully in similar landscapes and production systems, and planning the accuracy assessment at the start of the project based on best practices and resources available. While researchers can apply these considerations to any remote sensing mapping study, cost-effective, simple analyses are especially crucial for coffee mapping given the importance of developing replicable and scalable methods to produce large-scale coffee classifications that contribute towards sustainability efforts. In addition, mapping coffee extent given the complex landscape characteristics of coffee systems and heterogeneity of production systems requires deliberate evaluations of remote sensing methodological choices for successful mapping. Given these considerations, our study found that object-based and sub pixel-based methods effectively balanced complexity and accuracy; however, object-based methods are most effective with expensive high-resolution imagery, and sub pixel-based methods were limited in the literature. Another highly promising method is a Sentinel-1/Sentinel-2 fusion approach. While this was not used to map coffee in the literature, it was successfully implemented to map cacao, and it fulfills many of the considerations listed above. Most importantly, future research needs to address the significant gaps in the literature highlighted in this review to gain a complete understanding of coffee mapping using remote sensing. The increasing number of satellite constellations and data availability, notably SAR, as well as the recent, increasing accessibility to cloud computing, provides an opportunity to further support this endeavor by facilitating broadscale and robust coffee mapping projects contributing to efforts towards sector-wide sustainability. **Supplementary Materials**: The following are available online at [[http://www.mdpi.com/2072-4292/12/12/2041/s1](http://www.mdpi.com/2072-4292/12/12/2041/s1)]([http://www.mdpi.com/2072-4292/12/12/2041/s1](http://www.mdpi.com/2072-4292/12/12/2041/s1)), Table S1: Classification Methods from the Literature, Table S2: Considerations for Choosing the Best Method, Table S3: Validation Methods from the Literature. **Author Contributions**: Conceptualization, K.T., J.H.H. and M.A.W. ; Funding acquisition, K.T., J.H.H. and M.A.W. ; Investigation, D.A.H., K.T., J.H.H., M.A.W., L.R., K.K., M.S.-H. and L.C.K. ; Project Administration, D.A.H., K.T. and M.A.W. ; Visualization, D.A.H., K.T., J.H.H., M.A.W., L.R. and K.K. ; Writing--Original draft preparation, D.A.H., K.T., J.H.H., M.A.W. and K.K. ; Writing--Review and editing, D.A.H., K.T., J.H.H., M.A.W., L.R., K.K., M.S.-H., L.C.K. and F.F. All authors have read and agreed to the published version of the manuscript.