# Review of Remote Sensing Methods to Map Coffee Production Systems
## 1 Introduction  
Coffee is an important globally traded commodity, crucial to the economies of many tropical countries, and has the capacity to either positively or negatively impact forest cover, biodiversity, and human wellbeing. Consumers drink over 400 billion cups of coffee each year, underpinning a global industry worth over 100 billion USD [1; 2; 3]. Given its importance as a global commodity as well as its potential environmental impacts, there are important questions surrounding the global impact of the sector on land use and change, including how coffee production affects the regrowth, maintenance, or clearance of tropical forests. Answering these questions will help inform sustainable coffee production practices aimed to be both environmentally beneficial and socially responsible as well as profitable for producers today and in the future. More specifically, such practices aim toimprove the livelihoods of coffee farm workers and their families, increase the productivity of existing coffee production areas, and prevent either the clearing of high-conservation-value forests or the depleting of other natural resources [4]. Many stakeholders, including coffee traders, roasters, retailers, and governments, are also dependent on information related to coffee landscape dynamics and the extent of regional coffee production in order to ensure that sourcing and investment decisions are robust and directly contribute to zero-deforestation commitments (e.g., Refs. [5; 6]), including those embedded in certification and verification programs within the Sustainable Coffee Challenge [4].  
Some research has contributed to the need to understand the various landscape dynamics affected by coffee production. [PERSON] [7] suggests that coffee expansion, driven in part by expanding markets, may be tied to forest loss. Other research indicates that certain types of coffee production systems, such as shade cover systems, are associated with forest reclamation and regrowth [8]. Research in Mexico and Indonesia, for example, reveals that agroforestry practices can relieve pressures on forests by meeting local needs through farm and community tree resources [9; 10]. Planted shade trees commonly provide coffee growers with fruit, nut, and timber products that can be used for household consumption or sale on local markets, thus potentially reducing the demand for forest products. Such research is limited, however, and the complete picture of the global interrelatedness of coffee production and forest cover change remains unclear. To further understand these relationships, researchers need comprehensive and accurate national- or global-scale coffee maps. These, in turn, depend on the development of scalable, replicable, and affordable approaches to coffee mapping.  
The lack of successful, widespread coffee extent maps to-date has hindered a complete understanding of coffee's full environmental impact. To guide future coffee mapping and coffee sustainability research, we conducted a systematic review of existing approaches to mapping coffee using remote sensing in a diverse range of production systems varying in size and shade characteristics. First, we provide a brief overview of coffee systems. Next, we analyze different methodological approaches to landscape, sensor, and scalar dynamics as well as relative accuracy, replicability, scalability, and rigor based on a review of 43 articles from the mid-2000s onward. These articles utilize a range of recent innovations in data accessibility, cloud computing, and algorithmic development to more accurately map coffee systems. Our results are guidelines for using these methods and their current limitations and recommendations for further research given the rapid development of satellite sensor technologies and data accessibility. Through this review, we aim to provide technical recommendations to enable accurate and scalable coffee maps that underpin sector-wide measures of sustainability.