# Review of Remote Sensing Methods to Map Coffee Production Systems
## 1 Introduction
### Using Remote Sensing to Map Coffee  
Remote sensing represents the most feasible approach to scalable, replicable, and affordable land cover and land use change (LCLUC) mapping. The most accessible satellite data for LCLUC mapping are from the U.S. Geological Survey's Landsat program due to the sensor's specific design for natural resource mapping, the longevity of the Landsat mission dating back to 1972, and the free release of Landsat imagery [40; 41]. The Landsat sensor is optimized for mapping and monitoring land cover at the landscape-scale and is successfully applied to forest, non-forest, and agricultural LCLUC [42]. However, to date, the published literature on mapping coffee production areas and quantifying associated LCLUC has been limited. This is due in part to the complexity and variety of coffee production systems in addition to limitations of cost-effective, accessible satellite data. Coffee is mostly grown on small plots in heterogeneous landscapes, at relatively higher elevations in complex topographies [43; 44; 45; 46]. Coffee is also spectrally similar to multiple other woody cover types and is often obscured from the view of most satellites by an overstory canopy, and, therefore, it is commonly confused in existing remote sensing classification approaches with other crops and land cover types [7; 43; 47]. For example, not only are some shaded coffee systems spectrally confused with primary and secondary forests [43; 45; 46], but younger unshaded coffee systems can be confused with pastures and other unshaded crops [44]. Additionally, the spectral signature of coffee plants changes with age and throughout its phenological cycle (the phenological cycle of coffee lasts for two years, unlike many other crops, which complete their reproductive cycle in one year) as well as during coffee rust outbreaks [44; 48]. Persistent cloud cover and steep topography in many tropical coffee-growing regions further compound mapping challenges by creating issues with image interpretation. These characteristics reduce the accuracy of coffee mapping using moderate-resolution optical satellite data, such as Landsat. This is problematic as such data underpin the most accessible, affordable, and reliable methods for LCLUC mapping. Fortunately, recent advances in satellite technologies and investment in a range of sensors and satellite constellations by governments and the private sector are helping overcome tradeoffs in spectral, temporal, and spatial resolution [49; 50]. For this paper, we refer to high spatial resolution data as less than 5 m, moderate spatial resolution as between 5 m and 60 m, and coarse resolution as greater than 60 m. The increased coverage and availability of data from Synthetic Aperture Radar (SAR) sensors since 2015, mainly as a result of the European Space Agency's Copernicus Programme, can help address the limitations presented by optical imagery in cloudy regions [50]. SAR sensors utilize the microwave portion of the electromagnetic spectrum, actively emitting energy pulses to the earth and measuring the returned backscatter. Unlike optical sensors, energy in the longer wavelengths used by SAR sensors can penetrate clouds; thus SAR instruments can complement optical sensors in cloudy areas and fill data-gaps due to cloud cover [50]. This is particularly relevant for coffee mapping, given the majority of coffee cultivation occurs in persistently cloudy tropical regions. Another advance has been the number of high resolution optical sensors, providing reliable, high spatial resolution imagery without compromising temporal frequency; this is particularly useful considering the small area of most coffee farms. Planet Labs, for example, currently has a constellation of over 140 Dove satellites in orbit able to capture high resolution imagery (3.7 m) with daily global coverage [51].