# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Considerations for Choosing the Best Method
An object-based classification approach could bea promising method to map coffee extent, although its efficacy may be limited without expensive high-resolution imagery. Other methods from the literature balance complexity and accuracy more effectively. [PERSON] et al. (2019) used a pixel-based maximum likelihood classifier on Landsat TM imagery and spectral mixture analysis identifying shade, soil, and green vegetation endmembers. By incorporating the fraction images from these endmembers into the classification, along with optical and thermal bands, this study achieved a producer's accuracy of 88.6% and a user's accuracy of 89.7% for coffee agroforests. Spectral mixture analysis can be effective because it accounts for the physical processes causing the observed spectral signature and therefore incorporates mixed pixels ([PERSON] _et al._, 2018). This could be a particularly useful method as coffee is mostly grown in heterogeneous landscapes. Sensor fusion methods can also achieve high-accuracy results without overly laborious methodologies. [PERSON] et al. (2019) used Sentinel-1 and Landsat imagery to create many different models with various levels of data fusion. By combining six Sentinel-1 images and the NDVI and the simple ratio index (SR) from one Landsat scene, this study was able to obtain a 99% accuracy using both a random forest and support vector machine classifiers. While this method was applied to map winter wheat extent and not coffee, [PERSON] et al. (2015) reviewed numerous additional studies that successfully utilized optical and radar data fusion for diverse land use classifications. Researchers should explore similar methods as viable options for mapping coffee extent in the future. A successful methodology should also be developed based on the climatic and edaphic conditions of the region under investigation. Ideally, this would involve studying previously implemented methodologies from similar locations, although the dearth of existing literature makes this challenging. Specific approaches can still be tailored to geographical considerations, however. For example, [PERSON] et al. (2019) created seasonal composite images for the dry hot, rainy, and dry cool seasons of the central highlands of Nicaragua. While this added complexity to the method, it also significantly increased the overall accuracy over the non-seasonal composite by 16.1%. This method could be particularly useful for shade-coffee, as the authors cite the seasonal data's ability to capture \"intra-annual phenological variation across common woody and non-woody land cover classes\" as crucial to the model's classification accuracy and the ability to distinguish coffee from other surrounding forest classes. Incorporating precipitation and elevation, slope, and aspect data from the region increased the overall accuracy an additional 8.8%. Additional climatic elements, such as local cloud cover, should also be considered. Image time series could allow researchers to create a relatively gap-free mosaic, and high temporal resolution data or SAR data could also be used in regions that are particularly prone to heavy cloud cover. Researchers can also examine studies that successfully mapped the same coffee production systems as those in the study area in question. Multiple studies, including [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2019), mapped a general coffee class. [PERSON] et al. (2019) concentrated just on shaded coffee, [PERSON] (2019) and [PERSON] et al. (2019) mapped coffee agroforests, and [PERSON] et al. (2019) mapped rustic shaded coffee. A few studies we reviewed created multiple coffee classes to distinguish between coffee systems, however. [PERSON] and [PERSON] (2019) mapped shaded coffee and sun coffee as two separate classes, [PERSON] and [PERSON] (2019) mapped three classes, production coffee, mixed coffee, and old/pruned coffee, and [PERSON] et al. (2019) distinguished open versus closed canopy. [PERSON] et al.