# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results
### Spectral Pixel-Based Approaches  
Sixteen articles utilized spectral pixel-based methods with optical imagery exclusively (IKONOS-2, Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-2, Satellite Pour I'Observation de la Terre (SPOT), RapidEye), while an additional thirteen used spectral pixel-based approaches in conjunction with other methods. Such methods classify pixels in an image into land cover \begin{table}
\begin{tabular}{c c c} \hline
**Coffee** & **Remote Sensing** & **Geography** \\ \hline coffee & remote sensing & mapping \\ agroforestry & satellite & tropics \\ shade-coffee & Sentinel-1 & global \\ sun-coffee & Sentinel-2 & Indonesia \\ polyculture & MODIS & Colombia \\ monoculture & SAR & Brazil \\ Arabica & Landsat & Vietnam \\ Robusta & optical & Ethiopia \\  & high resolution & \\ \hline \end{tabular}
\end{table} 
Table 2: Search terms used in Google Scholar to find studies that examine effective remote sensing methods for mapping coffee extent. classes based on a given pixel's similarity to the spectral signature of the land cover types in the study area. Many of these articles used training datasets collected in the field and implemented a range of classifiers including: a hybrid approach [55], maximum likelihood [7; 45; 56; 57], random forest [58; 59; 60], support vector machine [61], and convolutional neural networks [61]. Some spectral pixel-based approaches used only the bands present in the imagery. For example, [PERSON] et al. [62] compared Landsat bands 3, 4, and 5 with the IKONOS-2 panchromatic image; [PERSON] et al. [63] utilized Landsat 8 bands 2-7; [PERSON] et al. [64] used bands 3, 4, and 5 from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), finding them the most effective at differentiating coffee from other scene targets; and [PERSON] [7] used bands 1-7 from Landsat TM. Multiple studies also incorporated band ratios and indices to extract more information from the data, most commonly the normalized difference vegetation index (NDVI). NDVI is frequently used to map vegetation productivity [63; 64; 65; 66; 44; 57; 60; 67]. Numerous other spectral indices we reviewed included the enhanced vegetation index (EVI) [12], land surface water index (LSWI) [57; 65], normalized difference red edge index (NDRE) [66], and moisture stress index (MSI) [66], among others. Other methodological approaches also utilized additional indices, located in Supplementary Table S1. Some approaches used principal component analysis techniques, such as Tasseled Cap transformations, instead of utilizing simple band ratios. For example, [PERSON] et al. [60] and [PERSON] et al. [57] used greenness, brightness, and wetness bands produced by a Tasseled Cap transformation. While spectral pixel-based methods can be less complex to implement when employed alone, they can be limited by cloud cover and are potentially unable to adequately incorporate coffee heterogeneity into models [68]. Therefore, a few studies added ancillary data beyond spectral values or functions to improve mapping accuracy. [PERSON] et al. [56] found that incorporating data from community-based mapping into a spectral analysis increased the accuracy by 1.6% (from 73% to 74.6%). While they did not deem this modest increase statistically significant, consulting local experts on community mapping did reduce the time and resources of the study, a potentially valuable lesson for future classifications in rural areas with limited funds. [PERSON] et al. [60] also included three elevation products (elevation, slope, and aspect) and meteorological values in their study. Each of their spectral composite images included a surface temperature band as well as an additional layer with a time-lagged correlation of precipitation and later shifts of NDVI. They found that including elevation data increased the overall accuracy of their non-seasonal model by 20.1% (to 85.7%) and their seasonal model by 7.8% (to 89.5%).