# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Considerations for Choosing the Best Method  
Analysts and practitioners should consider several factors prior to selecting a mapping approach to ensure that they are employing best practices and will have the most effective results under their respective circumstances (Box 1). We recommend considering constraints to an analysis, such as budget and complexity of the method. We advise that choosing methods already tested in a specific geographic region and for the target coffee systems is advantageous. Furthermore, establishing an accuracy assessment plan at the start of the project, rather than after the completed analysis, can help improve the robustness of validation. We describe each of these considerations in further detail below, and an overview of the information associated with each consideration for the 43 papers reviewed in this study is provided in Supplementary Table S2. Our recommendations emphasize freely available sources of data and low-cost methods to increase replicability and feasibility at larger scales. Minimizing costs and ensuring scalability to new geographies in the future requires free, globally available imagery such as MODIS, Landsat, Sentinel-1, and Sentinel-2. High resolution imagery that is generally proprietary, while often incredibly effective at mapping coffee accurately [88], has scenes that cover a small area and would be difficult and expensive to implement in future new studies. Although researchers have not yet adequately explored using Sentinel-2 for coffee mapping, its 10-20 m resolution bands, 5-day revisit time, and free, global coverage, along with its success mapping other crops [93] and forests [81], strongly suggests its potential for use in mapping coffee. Sentinel-1 radar data also provides an interesting option to address mapping coffee in cloudy tropical regions. These data also enable a texture-based approach to capture aspects of canopy structure, a key property for distinguishing coffee systems. A drawback to SAR data is the issue of displacement effects associated with the backscatter signal and relief shadows [103]. This could be problematic for mapping coffee in areas of complex topography such as steep slopes, particularly given the projected shifts in coffee-growing suitability to higher elevations under climate change [104]. The level of complexity of a coffee extent mapping methodology reflects the difficulty and amount of effort needed to implement it. While this measure is subjective, it is a useful metric to consider before establishing if an approach is scalable. A researcher needs to balance the complexity of the analysis by considering the amount of detail a model can capture and its accuracy. A simple model that is easily scalable may have poor results, while an overly detailed, accurate model may be so complex that it would be difficult to apply to numerous study areas. For example, the method used by [PERSON] et al. [91] to map individual tree canopies is complex and would be difficult to implement on a large scale. [PERSON] et al. [44] tested a simpler and more scalable methodology using the NDVI and EVI MODIS products; however, using this methodology to map coffee would not produce the detail required to adequately map smallholder coffee farms. [PERSON] and [PERSON] [45] used a simple pixel-based maximum likelihood classifier with five Landsat bands, NDVI, and cos (i) (solar radiance incident angle) to achieve high producer's accuracy in distinguishing shaded and sun coffee (91.8% and 86.2%). This method produced a low user accuracy (61.1% and 68.4%), potentially showing that this approach does not adequately differentiate coffee systems from surrounding land cover classes. [PERSON] et al. [88] discuss potential shortcomings of pixel-based maximum likelihood methods and compare this approach with an object-based image analysis, concluding that the object-based method improved overall accuracy by about 15%.