# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Approaches to Overcome Coffee Mapping Challenges  
Our results indicate that researchers use a variety of approaches and sensors to map coffee. Spectral pixel-based approaches using moderate resolution optical imagery were the most common methods, highlighting the utility of multispectral data for separating coffee from spectrally similar land cover in a complex landscape. The sensor used most frequently was Landsat (30 m), with accuracies ranging from approximately 50-95% [7; 36; 45; 47; 55; 56; 57; 58; 59; 60; 62; 63; 64; 65; 66; 69; 71; 72; 73; 79; 80; 83; 85; 89; 93] (see Supplementary Table S1). Researchers frequently relied on indices such as NDVI, EVI, VIN, LSWI, SAVI, NDRE, and MSI to extract more information from multispectral bands to map features within their study area. Freely available multispectral data, such as Landsat 8 and Sentinel-2, have 11 and 10 bands respectively, ranging from 20 nm to over 200 nm in bandwidth. This is sufficient to provide an adequate number of bands for analysis and to calculate a range of indices to successfully map coffee extent. Another approach to map complex coffee-growing landscapes is to capture phenological change with multi-temporal data. Coffee's two-year phenological cycle, recognized by coffee yield forecasters as an important characteristic for modeling coffee production, may also lend itself to remote sensing detection of the crop [44]. If this biennial growth cycle can be accurately captured using a remote sensing classification time series, this could increase the separability of coffee-growing areas from their surrounding environment. [PERSON] et al. [55] used Landsat to perform a classification based on multi-date imagery to capture seasonal variations, even with its temporal resolution of 16 days. Researchers have also used imagery from multiple Landsat systems to increase image frequency. [PERSON] et al. [60] utilized 143 Landsat scenes from multiple years to effectively differentiate dry hot, rainy, and dry cool seasons. This differentiation gave a significant boost to overall accuracy (81.7% for seasonal data) compared to a non-seasonal dataset of the same variables (65.6%). [PERSON] et al. [65] used Landsat 8 imagery to track NDVI and LSWI values over time to track trends in crop growth. While the use of Sentinel-2 to map coffee extent with spectral pixel-based approaches is currently limited in the literature, this is most likely due to its recent launch in 2015. For example, [PERSON] et al. [58] used Sentinel-2 to map land cover, including coffee extents, in Vietnam; however, the study produced an overall landscape analysis and ecological vulnerability assessment and not an analysis of the accuracy of their approach to mapp coffee. [PERSON] et al. [98] used Sentinel-2 to identify the level of rust outbreaks in coffee plants, [PERSON] et al. [99] mapped foliar nitrogen in coffee using Sentinel-2, and [PERSON] et al. [93] used Sentinel-2 to map cropland extents, though not specifically coffee extents. Hyperspectral sensors provide even greater spectral detail to separate coffee production systems in complex landscapes. Optical data from high spectral resolution sensors provide an increased ability to distinguish between landcover classes with similar spectral profiles, such as shaded coffee systems and forests. For example, [PERSON] et al. [83] used hyperspectral imagery from Hymap 2005 with 126 spectral bands, along with multispectral imagery, to successfully classify forest composition with a fusion approach. The number of fine spectral bands in hyperspectral imagery could be valuable in mapping coffee and distinguishing it from surrounding species, although its efficacy is mostly unstudied in the literature and data availability are more limited than multispectral data. Furthermore, a handful of studies in our review applied hybrid approaches by combining several methods to separate coffee plants from spectrally similar vegetation to overcome challenges of landscape heterogeneity.