## 2 Data Acquisition and Processing
### VHR Satellite Series  
A dataset of 15 images was acquired by the Digital Globe (DG) satellite constellation during the 2014 growing season over a 10 km \(\times\) 10 km area centered on the Sukumba village (Table 1). The spatial resolution of these images varies from 0.5 m to 0.61 m for the panchromatic band and from 2 m to 2.44 m for the multispectral bands. This constitutes an unprecedented dataset for Sudano-Sahelian smallholder agriculture. In order to ensure accurate imagery geo-location in a landscape characterized by relatively few artificial man-made structures, a set of 65 Ground Control Points (GCP) was used. Some GCPs were located on rock outcrops but most GCPs were concrete structures with a standard cross shape (width: 120 cm; thickness: 20 cm). All GCPs were painted white around mid-August of 2014. They are easily identified on satellite images while they were initially designed for the georeferencing of images acquired by a UAV operating only in the Northern part of the 10 km \(\times\) 10 km study area (_cf._ Section 2.4). Additional GCPs corresponding to fence or hedge corners were identified in the Southern part of the study area. The geographic coordinates of GCPs were recorded with a differential GPS system with centimeter accuracy. Figure 1: Sukumba site, near Koutiala in southeastern Mali. The 10 km \(\times\) 10 km blue box locates the VHR imagery acquisition. The _in-situ_ bi-weekly monitored fields represented by green boundaries are spread along a catena transect from plateau (north) to lowland (south). Background imagery is a GeoEye RGB color composite from 24 June 2014. The complete set of GCPs covering the entire study area is only visible on images acquired after mid-August. Moreover, various processing levels have been applied by DG on the time series; some were orthorectified (the so-called _Standard_ and _Orthorectified_ product types), while others were not (_Ortho ready_ product type). Therefore, combining these two constrains, four dedicated geometric correction approaches were used to ensure that the time series were geometrically consistent. The four correction methods as reported in Table 1 are:  
1. _Ortho ready DG-products_ with no DEM correction by DG (base elevation) were orthorectified using SRTM 30 m DEM and GCPs when available, _i.e._, after 26 August 2014. Out of the total set of 65 GCPs, a subset of 38 GCPs was used to correct the 18 October 2014 image. Orthorectification resulted in a geometric accuracy of 0.84 m (RMSE), assessed on remaining GCPs not used for orthorectification. This image was thereafter taken as master image to geometrically correct the other images in the absence of GCPs (methods 2 and 4 in Table 1). The same set of GCPs was used to orthorectify the 3 last ortho ready standard images of the series using the same method. 2. _Ortho ready DG-products_ acquired before 26 August 2014 were first orthorectified with the SRTM 30 m DEM without any GCP, and were subsequently co-registered to the master image (_i.e._, 18 October 2014 image orthorectified with the first method as describe here above) thanks to an automatic image-to-image registration algorithm providing 25 tie-points well distributed over the image. 3. Products orthorectified by DG are of two types: _standard product_ orthorectified using a coarse DEM (SRTM 90 m) and _orthorectified product_ pre-processed by DG with fine DEM (SRTM 30 m). Both product types were simply georeferenced with the set of 38 GCPs for dates after 26 August 2014. 4. Products orthorectified by DG (both _standard product_ and _orthorectified_ product types) acquired before 26 August 2014 were georeferenced using the master image by automatic image-to-image registration (as mentioned above for case 2). Orthorectified image DNs were then converted to top-of-atmosphere reflectance [21; 22]. Clouds and cloud shadows were detected using an unsupervised classification and then the areas where masked out. All the processing methods resampled the images at a 2 m pixel size using the nearest neighbor algorithm.