In this section, we discuss the advantages and disadvantages of methodological approaches in the literature in addressing the common challenges of mapping coffee. We present considerations and recommendations to guide the process for choosing the best approaches for mapping coffee production systems. We also highlight opportunities for future research to address the urgent needs regarding where and how the coffee sector can invest to achieve sustainable production.
4.1. 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
] (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. Ortega-Huerta 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. Kelley 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%). Chemura 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, Bourgoin 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. Chemura et al. [98
] used Sentinel-2 to identify the level of rust outbreaks in coffee plants, Chemura et al. [99
] mapped foliar nitrogen in coffee using Sentinel-2, and Xiong 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, Fagan 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. 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 successful than spectral-pixel based analyses by comparative studies in the literature [47
]. 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. Belgiu 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 Schmitt-Harsh et al. [69
] were able to successfully map coffee using a sub-pixel-based approach on Landsat imagery. Radoux 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, Bernardes 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. However, studies using Sentinel-1 radar data [75
] still use multiple images to gather additional data and measure seasonal variability. Sentinel-1′s twelve-day revisit time per satellite, or six days for the constellation, helps effectively create imagery time series. While we found only one study using SAR to map coffee, researchers have used SAR data to map various croplands and forest types and thus, could inform coffee mapping methodologies. SAR data could also allow researchers to study the structure of the observed vegetation, which would be useful in differentiating shaded coffee production systems from surrounding forests. However, the displacement effects of the backscatter signal together with relief shadows can limit the efficacy of SAR data in complex topographies.
Although we aimed to review literature that can successfully map different coffee-growing systems, we only found one publication that mapped five different production systems [92
], and we did not find any literature focused on mapping the five production systems that we had defined to frame our review (unshaded monoculture, shaded monoculture, commercial polyculture, traditional polyculture, and rustic). One reason for the limited number of published studies on coffee mapping could be a positive publication bias, where unsuccessful studies or inaccurate maps are not published. It is also possible that more research on mapping the five coffee systems has been published in non-English journals as coffee is mostly grown in the global tropics where English is not the dominant language. Such publications would not be captured as effectively in our literature review. Despite the limited number and variety of published studies, it is nevertheless still possible to make recommendations based on our findings.
4.2. Considerations for Choosing the Best Method
Analysts and practitioners should consider several factors prior to selecting a mapping approach to ensure that they are employing best practices and will have the most effective results under their respective circumstances (Box 1
). We recommend considering constraints to an analysis, such as budget and complexity of the method. We advise that choosing methods already tested in a specific geographic region and for the target coffee systems is advantageous. Furthermore, establishing an accuracy assessment plan at the start of the project, rather than after the completed analysis, can help improve the robustness of validation. We describe each of these considerations in further detail below, and an overview of the information associated with each consideration for the 43 papers reviewed in this study is provided in Supplementary Table S2
Box 1. Considerations for coffee mapping.
Budget for imagery, software licensing, and validation data.
Complexity of approach (i.e., processing requirements, replicability).
Methods tested in similar geography and for target coffee system(s).
Accuracy assessment plan.
Our recommendations emphasize freely available sources of data and low-cost methods to increase replicability and feasibility at larger scales. Minimizing costs and ensuring scalability to new geographies in the future requires free, globally available imagery such as MODIS, Landsat, Sentinel-1, and Sentinel-2. High resolution imagery that is generally proprietary, while often incredibly effective at mapping coffee accurately [88
], has scenes that cover a small area and would be difficult and expensive to implement in future new studies. Although researchers have not yet adequately explored using Sentinel-2 for coffee mapping, its 10–20 m resolution bands, 5-day revisit time, and free, global coverage, along with its success mapping other crops [93
] and forests [81
], strongly suggests its potential for use in mapping coffee. Sentinel-1 radar data also provides an interesting option to address mapping coffee in cloudy tropical regions. These data also enable a texture-based approach to capture aspects of canopy structure, a key property for distinguishing coffee systems. A drawback to SAR data is the issue of displacement effects associated with the backscatter signal and relief shadows [103
]. This could be problematic for mapping coffee in areas of complex topography such as steep slopes, particularly given the projected shifts in coffee-growing suitability to higher elevations under climate change [104
The level of complexity of a coffee extent mapping methodology reflects the difficulty and amount of effort needed to implement it. While this measure is subjective, it is a useful metric to consider before establishing if an approach is scalable. A researcher needs to balance the complexity of the analysis by considering the amount of detail a model can capture and its accuracy. A simple model that is easily scalable may have poor results, while an overly detailed, accurate model may be so complex that it would be difficult to apply to numerous study areas. For example, the method used by Gomez et al. [91
] to map individual tree canopies is complex and would be difficult to implement on a large scale. Bernardes et al. [44
] tested a simpler and more scalable methodology using the NDVI and EVI MODIS products; however, using this methodology to map coffee would not produce the detail required to adequately map smallholder coffee farms. Cordero-Sancho and Sader [45
] used a simple pixel-based maximum likelihood classifier with five Landsat bands, NDVI, and cos (i) (solar radiance incident angle) to achieve high producer’s accuracy in distinguishing shaded and sun coffee (91.8% and 86.2%). This method produced a low user accuracy (61.1% and 68.4%), potentially showing that this approach does not adequately differentiate coffee systems from surrounding land cover classes. Gaertner et al. [88
] discuss potential shortcomings of pixel-based maximum likelihood methods and compare this approach with an object-based image analysis, concluding that the object-based method improved overall accuracy by about 15%. An object-based classification approach could be a promising method to map coffee extent, although its efficacy may be limited without expensive high-resolution imagery.
Other methods from the literature balance complexity and accuracy more effectively. Schmitt-Harsh et al. [69
] used a pixel-based maximum likelihood classifier on Landsat TM imagery and spectral mixture analysis identifying shade, soil, and green vegetation endmembers. By incorporating the fraction images from these endmembers into the classification, along with optical and thermal bands, this study achieved a producer’s accuracy of 88.6% and a user’s accuracy of 89.7% for coffee agroforests. Spectral mixture analysis can be effective because it accounts for the physical processes causing the observed spectral signature and therefore incorporates mixed pixels [68
]. This could be a particularly useful method as coffee is mostly grown in heterogeneous landscapes. Sensor fusion methods can also achieve high-accuracy results without overly laborious methodologies. Zhou et al. [79
] used Sentinel-1 and Landsat imagery to create many different models with various levels of data fusion. By combining six Sentinel-1 images and the NDVI and the simple ratio index (SR) from one Landsat scene, this study was able to obtain a 99% accuracy using both a random forest and support vector machine classifiers. While this method was applied to map winter wheat extent and not coffee, Joshi et al. [105
] reviewed numerous additional studies that successfully utilized optical and radar data fusion for diverse land use classifications. Researchers should explore similar methods as viable options for mapping coffee extent in the future.
A successful methodology should also be developed based on the climatic and edaphic conditions of the region under investigation. Ideally, this would involve studying previously implemented methodologies from similar locations, although the dearth of existing literature makes this challenging. Specific approaches can still be tailored to geographical considerations, however. For example, Kelley et al. [60
] created seasonal composite images for the dry hot, rainy, and dry cool seasons of the central highlands of Nicaragua. While this added complexity to the method, it also significantly increased the overall accuracy over the non-seasonal composite by 16.1%. This method could be particularly useful for shade-coffee, as the authors cite the seasonal data’s ability to capture “intra-annual phenological variation across common woody and non-woody land cover classes” as crucial to the model’s classification accuracy and the ability to distinguish coffee from other surrounding forest classes. Incorporating precipitation and elevation, slope, and aspect data from the region increased the overall accuracy an additional 8.8%. Additional climatic elements, such as local cloud cover, should also be considered. Image time series could allow researchers to create a relatively gap-free mosaic, and high temporal resolution data or SAR data could also be used in regions that are particularly prone to heavy cloud cover.
Researchers can also examine studies that successfully mapped the same coffee production systems as those in the study area in question. Multiple studies, including Alves et al. [90
], Bernardes et al. [44
], and Gaertner et al. [88
], mapped a general coffee class. Gomez et al. [91
] concentrated just on shaded coffee, Schmitt-Harsh [7
] and Schmitt-Harsh et al. [69
] mapped coffee agroforests, and Kelley et al. [60
] mapped rustic shaded coffee. A few studies we reviewed created multiple coffee classes to distinguish between coffee systems, however. Cordero-Sancho and Sader [45
] mapped shaded coffee and sun coffee as two separate classes, Kawakubo and Machado [68
] mapped three classes, production coffee, mixed coffee, and old/pruned coffee, and Ortega-Huerta et al. [55
] distinguished open versus closed canopy. Widayati et al. [92
] classified five systems that are present in Lampung Province, Indonesia (closed canopy cover (>50% cover), shade polyculture (25–50% cover), sun monoculture (25–50% cover), sun coffee (25% cover), and newly planted (sparse cover) using an integrated pixel-based analysis and object-based approach, with an overall accuracy up to 85.4%. Researchers should heavily weigh the efficacy of the approaches with multiple coffee classes into the decision to determine the best approach to develop scalable, replicable methodologies.
Researchers should develop a validation approach that emphasizes scalability and considers the accuracy assessment best practices identified by Olofsson et al. [95
]. In the early stages of a study, researchers must create a robust sampling design to assess the accuracy, preferably based on stratified sampling to ensure a representative spatial distribution and accounting for the size of the study area, while identifying and integrating the limitations of the region of interest, such as study area accessibility/fieldwork cost and reference data availability. Olofsson et al. [95
] also recommended that the reference dataset used in the response design should be a higher spatial resolution than the imagery used for the classification. In the absence of local independent validation data, many of the studies in this review used high-resolution optical imagery, such as WorldView 2 [88
] and aerial photographs [69
]. Analysts could use Light detection and ranging (LiDAR) data in conjunction with high-resolution imagery in agroforestry coffee systems. Campbell et al. [106
] detailed and compared methods for understory density metrics and Jubanski et al. [107
] presented methods for estimating understory density in Kalimantan, Indonesia. These studies detailed the natural variability observed in Indonesian forests and outlined the optimal configuration of sensors for accurate understory density estimation. Similar methods could be applied to validate the classification of shaded systems where a thick canopy obscures coffee plants. High-resolution satellite data, aerial photographs, and LiDAR data would significantly increase the project costs, therefore, limiting scalability. Liu et al. [81
] instead used high-resolution imagery freely available on Google Earth as reference data. Similar methods using open source software, such as Collect Earth [108
], have gained traction in recent years, and offer the opportunity to quickly, easily, and sustainably implement accuracy assessments in scalable classification methodologies.
4.3. Future Research Directions
In light of these considerations and the outcomes of the literature review, we can provide specific recommendations for researchers to incorporate into future coffee mapping studies. Object-based mapping approaches were deemed successful by the literature at many different scales [47
] and could represent a viable method. Widayati et al. [92
] were able to use a pixel- and object-based hybrid approach to map five different production systems, which would be incredibly useful in a landscape with different levels of canopy coverage. Despite its uses, object-based methods are most effective with high-resolution data, which drastically increases the cost and limits scalability. As freely available data continues to increase in spatial resolution however, this could become more feasible. Spectral mixture methods, which can be implemented with moderate or low resolution data, could present a more practical scalable approach. These methods were limited in the literature, however, and need further exploration. The method that seems most promising, however, is an optical/SAR fusion method using Sentinel-1 and Sentinel-2. Similar optical/SAR fusion methods have been successfully implemented in the literature [79
], and although these studies mapped other commodities and forests, they could represent an effective solution for future global coffee mapping. These approaches fulfill many of the criteria outlined in the considerations section: the imagery is low cost at a relatively high resolution, the fusion strategy leverages the strengths of both sensors without being overwhelmingly complex, SAR data is not limited by cloud cover and can observe vegetation structure, and this method is applicable across many geographies, lending itself to be a replicable approach. Although fusion methods can be more laborious to implement, the imagery is readily available to analyze on platforms such as Google Earth Engine. Additionally, we recommend incorporating ancillary data into coffee classifications for any chosen method Many studies added DEM data [60
], forest cover maps [89
], or precipitation and climatic data [60
] to their models with great success.
Our review also highlights the sparsity of literature documenting remote sensing methods for mapping coffee systems. Determining the best methods and practices is inherently limited when numerous approaches have not been tested. Based on our results, we see gaps in the current research for researchers to address to fully actualize scalable, mapping methods for coffee systems. This includes the lack of research differentiating between production systems and the absence of important coffee-growing geographies. Fortunately, we see three recent advances as pivotal for advancing this space. First, the variety and availability of satellite constellations of high resolution and satellite sensors of moderate resolution improve the revisit rate, increasing the likelihood of cloud-free looks. Future research should more thoroughly explore Sentinel-2; its use, along with Landsat, represents the most practical resolution option to expand into new geographies and for large-scale coffee mapping. Second, recent investments in new sensors to measure ecosystem structure include SAR P-band and L-band SAR sensors [105
], and space-borne Lidar (Global Ecosystem Dynamics Investigation (GEDI)) [109
] may enable mapping and differentiation of complex shade coffee systems by measuring understory and canopy structures. Given the promising results from radar and multispectral sensor fusion approaches, particularly to map cacao systems, exploring the efficacy of these methods in mapping coffee extent is key for future research. This is an important literature gap since SAR data is not limited by cloud cover, a crucial advantage for mapping coffee-growing regions. Finally, advances in cloud computing and deep learning algorithms can improve classification in complex landscapes [110
]. Deep learning has been successfully used in recent studies to support analyses of image fusion, LCLUC, object detection, and scene recognition, among others [111
]. By stressing these research opportunities, we hope researchers can focus on the specific urgent needs for coffee mapping to inform global investments in coffee production communities to improve livelihoods, support biodiversity conservation, reduce deforestation, and improve sustainable production.