## 2 Data and Methods
### Analysis  
Given the slight differences in image acquisition dates, and inherent difficulties related to normalizing reflectance among adjacent scenes, each footprint was classified independently. Land cover within the area delineated by the TM footprint at Path: 172; Row: 071 (WRS 2) was classified first. The method for classifying land cover in this footprint, and classification results, are presented in detail. Classification results from the two adjacent footprints are provided in the supplement. To develop a training dataset, a clustering algorithm (ISODATA) in Erdas IMAGINE was applied to the multi-temporal spectral data to identify statistical patterns and segment pixels into natural occurring clusters. Utilizing a minimum spectral distance formula, pixels were grouped into ten clusters and randomly sampled, generating 75 points within each unsupervised cluster. Square buffers, delineating an area of 900 m\({}^{2}\), were constructed around sample points and a ground cover label assigned to each polygon through interpretation of high resolution Google Earth imagery [22,59,60]. Training locations were coded with Google Earth imagery acquired between 2007 and 2010. Of the total sample of 750 points, 498 were interpretable and intersected with suitable land cover reference imagery. Training data were labeled as 1 of 5 primary land cover classes: (1) _forest_; (2) _cropland_; (3) _savanna_; (4) _settlement_; and (5) _water_. The dominant land covers were adequately sampled by this scheme, however, the _settlement_ class was underrepresented so purposeful sampling was performed to supplement the training data for that category with an additional 25 points. A processing diagram is provided to illustrate the workflow that follows (Figure 2). As operations are described, text is provided directing to specific points in the diagram, e.g., \"Operation 1, Figure 2\" refers to the operation icon in the workflow labeled \"1\" (hierarchical clustering of training signatures). Spectral signatures were extracted from the multi-temporal data at sample locations and imported into DataDesk statistical software [61]. Because intra-class spectral variability was high, the compliment of signaturesassociated with each primary land cover category was grouped into spectrally similar subsets, or subgroups, using hierarchical clustering (Operation 1, Figure 2). Mean subgroup signatures were produced by averaging signature values for each unique cluster of signatures. Separability was tested among subgroup signatures, within primary categories, by calculating the Transformed Divergence (TD) [62], merging signature pairs with a TD value less than 1700 [63]. Spectral  
Figure 2: Classification workflow using objects and syntax analogous to the model builder in ERDAS Imagine. Note that reclassification iterations are performed by repeating the loop of Operations 5, 8–11 (in bold), the remaining operations are performed once. The dashed line, directing to Operation 5 in the diagram, represents data not used directly in the operation but rather to establish parameters for Operation 5.  
variability was high among all non-water categories. The greatest variability was observed in the cropland class where spectral signatures were partitioned into 10 clusters with minimal overlap (Figure 3). The bulk of training samples (\(\sim\)67%) was distributed among the cropland and savanna categories. Hierarchical clustering of training signatures produced 3 to 10 subgroups for non-water categories with cropland and savanna represented by the greatest number of subgroups (Table 1). The mean signatures used to parameterize the initial classification represented the average spectral value of each of these subgroups. We categorized the spectral data in Erdas IMAGINE with a MLC, selecting all layers in the temporal dataset as input to the classifier and applying the 25 subgroup signatures (Operation 2, Figure 2).