## 4 Discussion  
Reliably mapping cropland within smallholder-dominated savannas is critical to determining total area under crop and the distribution of cropland on the African continent. The workflow, and application of techniques, outlined in this paper delineates cropland within the savanna landscape of Zambia. The potential future applications of the protocol presented here could support policy initiatives aimed at achieving food security and support the frequent monitoring of food security in the region, as well as improve our understanding how seasonal climate variability affects regional crop production. The suite of complementary remote sensing techniques used in this analysis are familiar to remote sensing researchers. Our contribution lies in the integration of these techniques in a manner that capitalizes on the salient utility of each one. The data-driven unsupervised clustering algorithm (ISODATA) segments pixels into natural clusters that reflect the underlying spectral structure of the data [71]. Through random sampling of unsupervised clusters, we are able to capture variability within primary land cover categories that is representative of conditions throughout the study area in an unbiased training dataset. Likewise, clustering training data into primary category subgroups and parameterizing a MLC with subgroup spectra has two distinct advantages. First, thematic output is produced where commission error can be detected and isolated within strata of primary categories. Second, spectral variability is reduced within  
Figure 5: Thematic land cover map of combined scenes at Path: 172 Row: 071, Path: 172 Row: 072, and Path: 173 Row: 071. White areas were obscured by cloud. classes which minimizes the number of confused primary categories within subgroups and reduces spectral noise, helping prevent over-fitting of the logistic model. The targeted reclassification that we adopt in this approach accounts for large spectral variability that is typically characteristic of generic land cover categories. By reclassifying within subgroups of primary categories we are able to treat spectral confusion between identical pairs of primary land covers uniquely within different subgroups. While our selection of model variables is consistent with previous research [42, 43, 44, 45, 21, 22, 42] we have reduced the temporal requirements in the model and combined phenological measures with sub-pixel fractions of pure surface components. With this approach, we are able to address spectro-temporal similarity that cannot be addressed with phenological variables alone. From a pool of ten candidate variables, logit models are utilized to largely correct commission error in select primary category subgroups affecting the accuracy of the cropland category. For example, commission error resulting from savanna misclassified as cropland was reduced by over 22% (absolute change) for the combined scenes; commission error resulting from cropland misclassified as settlement was reduced by almost 35% (absolute change) for the combined scenes. These correction measures drove errors of omission and commission within the overall cropland class down to 12% and 10%, respectively. Although image data acquired during the rainy season could potentially enhance spectral separation between cropland and the surrounding land cover, cloud-free data was not available. We have, however, demonstrated that data acquired at time points bracketing the temporal window of the growing season can be used to accurately delineate cropland. Admittedly, separation between settlement and cropland is more challenging without an image acquired during the growing season. We were able to eliminate much of the detected commission error within high-error settlement subgroups but omission error remained rather high at 22%, but these areas constitute a fraction of 1 percent of the study area.