## 1 Introduction
### Review of Techniques for Remote Sensing Cropland in Savanna Landscapes  
Savannas are common ecosystems that are distributed globally across the tropic and sub-tropic latitudes. Comprised of grasslands interspersed with bushes and trees in varying densities, savannas provide essential services in the form of carbon storage and climate regulation, and habitat for many mega-fauna species [20]. A variety of remote sensing approaches have been proposed to map cropland within savanna landscapes. In the Brazilian Cerrado, land cover was classified into discrete map categories, including pasture and cropland, by applying a random forest classifier to Landsat derived spectral-temporal statistical metrics [21]. The temporal window of Landsat data used in this study spanned a period of several years. This method achieved an overall accuracy of 92%, but the temporal depth of 3 years may prohibit the use of this approach in locations where savannas are undergoing rapid conversion and more frequent monitoring is desired, such as those on the African continent. [PERSON] _et al._[22] applied support vector machines (SVM) to temporal Landsat data in a classification of the greater Serengeti ecosystem, achieving high accuracies for both stable and conversion categories, however, the approach poses some potential challenges related to training and validating the conversion category, notably the reliable identification of converted area and the directionality of conversion. Temporal measures of the Normalized Difference Vegetation Index (NDVI), coupled with mono-temporal refinement techniques, have also been utilized in a supervised land cover classification of a marginal semi-arid region within the Sahelian belt of Niger [23]. Omission and commission errors were 5% and 12% respectively for the cultivated category, but this success is largely dependent on having images available throughout the entire season. In addition to issues related to data availability, using NDVI in savannas can be problematic given that the index is affected by soil color and scale [24, 25, 26, 27, 28]. Object-based classifiers (OBC) have received increasing attention because of their ability to incorporate spatial data into the classification process. Utilized in conjunction with high resolution satellite data, OBCs have been used to identify cropland [29] and to differentiate woodland from surrounding land cover in a savanna environment [30]. However, research has shown the only significant advantage of an OBC over a maximum likelihood classifier (MLC) is discriminating vegetation classes with complex forest stand structures [31]. Additionally, cost and data storage are significant logistical challenges, particularly when the spatial extent of a study area is large. Results from other remote sensing studies in semi-arid environments suggest that fraction cover estimates made using unmixing methods are less affected by soil background than NDVI [32, 33, 34, 35, 36, 37].There are several methods for estimating sub-pixel fractions of land cover, including multi-resolution approaches and spectral mixture analysis (SMA). [PERSON] _et al._[38] introduced a method of scaling up from an initial classification of high-resolution data to medium and coarse resolution imagery through a process of sampling and the use of random forest regression trees. A major limitation of this method is that the requisite high-resolution data are often unavailable. SMA estimates the fractional composition of land cover within a pixel by using the spectral signatures of endmembers, or \"pure pixels\" [39], treating a pixel's spectrum as a linear combination of the spectra of its physical elements [40]. Spectral unmixing has been suggested to be among the most promising techniques for obtaining data on surface cover in savanna environments where there is a large presence of non-photosynthetic vegetation (NPV), and reflectance is affected by scattering, mixing, and variability in soil composition [34].