# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Approaches to Overcome Coffee Mapping Challenges
Generally, these approaches introduce complex methods that are difficult for practitioners to replicate and may be overly specific to a single landscape, preventing scalability of the approach. However, we encourage innovation in this space, but also recommend researchers invest in packaging their methods in a software program or platform to increase accessibility for practitioners. Coffee farms, particularly shaded coffee farms, typically occupy areas smaller than the minimum mapping unit of moderate or low spatial resolution remote sensing products. Therefore, researchers trying to address the issue of scale used spectral pixel-based and object-based approaches with high spatial resolution sensors. High spatial resolution data provide greater detail in each scene, which increases the likelihood of distinguishing smallholder agroforestry systems from the surrounding forest landscapes. Object-based analyses with high-resolution imagery were found to be more successfulthan spectral-pixel based analyses by comparative studies in the literature [47; 88]. High-resolution sensors are also useful in validation methods when data collection in the field is not a viable option. Despite the success using high-resolution imagery, the costs associated with these proprietary data make its use as the primary imagery source in a scalable methodology impractical. There are more cost-effective, scalable approaches to address the challenges related to mapping small-scale coffee farms, however. One method is to examine object-based approaches with moderate resolution sensors. Belgium and Csillik [100] proved the utility of using object-based approaches for moderate resolution Sentinel-2 imagery for croplands. Similar studies should be performed for coffee mapping to understand the true efficacy of object-based approaches with Sentinel-2's higher resolution (10-20 m) compared to similar bands in 30 m resolution Landsat. Additional studies used sub-pixel-based approaches with moderate resolution sensors to account for coffee farms that were smaller than the minimum mapping unit. The results of these studies were variable, but [PERSON] et al. [69] were able to successfully map coffee using a sub-pixel-based approach on Landsat imagery. [PERSON] et al. [101] highlighted the potential for sub-pixel classification approaches to be implemented with Sentinel-2 imagery. As with object-based approaches, sub-pixel approaches to mapping coffee using Sentinel-2 imagery should be further explored. To address the issues of cloudiness in coffee-growing landscapes, researchers have explored using data with various temporal resolutions to perform both time-series and multi-image analyses. High temporal resolutions are useful in landscapes with frequent cloud cover, as well as capturing temporal variability in the production system [44] and deforestation and degradation [73]. For example, [PERSON] et al. [44] utilized the one-day to two-day revisit time of the Terra satellite's MODIS sensor to successfully study coffee yields. High spatial resolution imagery from proprietary data sources, such as RapidEye, can also have nearly daily revisit times due to larger satellite constellations and was used to map coffee [90] and cacao [82]; however, viability for use at larger scales is inhibited by the high cost of proprietary imagery. With a revisit time of only five days, Sentinel-2 could also prove a viable data source for coffee extent mapping to overcome issues of persistent cloud cover. In addition, the \"virtual constellation\" of Landsat and Sentinel sensors further increases the revisit rates [102]. Another strategy to overcome persistent cloud cover is to use SAR data for texture-based analysis and with fusion-based approaches. Cloud cover is an issue with optical sensors and not with SAR sensors because the longer wavelengths can penetrate clouds, so multi-image analysis with radar data is less dependent on high temporal resolution.