The input datasets selected for this analysis were MODIS (1) MOD11A2 Land Surface Temperature (LST) 8-day composite data (Wan et al., 2002 ), and (2) MCD43B4 Bidirectional Reflectance Distribution Function (BRDF) – corrected 16-day composite data (Schaaf et al., 2002 ), from which Enhanced Vegetation Index (EVI) was derived using the equation defined in Huete et al. (1999) . The MODIS LST dataset consists of both daytime and nighttime average temperatures aggregated, respectively, from the descending and ascending paths of the NASA Terra Satellite. The BRDF dataset contains 16-day products, with overlapping temporal windows that result in an 8-day temporal resolution, which were derived from data collected by the MODIS sensors on both the Aqua or Terra satellites.
The MODIS data were collected on a per-tile basis and then merged using the MODIS reprojection tool (Dwyer and Schmidt, 2006 ) to create seamless mosaics for all of Africa. A total of 42 tiles were required to cover the continent for each image date (i.e., the day of the year corresponding to the center of the composite temporal window). The BRDF mosaics each consisted of seven spectral bands, three of which were needed to derive the EVI, and mosaics were created for each of these bands prior to deriving the EVI for each image date. The resulting data archives consisted of 594 EVI mosaics (from day 049, 2000 to 361, 2012), and 590 LST-day and LST-night mosaics (from day 065, 2000 to 361, 2012). Temporal mean and standard deviation images were derived on a per-pixel basis from the full mosaic archives for each of the three variables for subsequent use in the gap filling algorithms. Producing images of summary statistics was also useful for identifying pixels that never contain usable data (e.g., ocean pixels) that could be ignored in the gap-filling procedures, thus reducing run-time.
The initial step in the gap filling process was to identify gap pixels in need of filling through the use of a despeckling algorithm, which is a processing step that need only be used if corresponding datasets describing pixel-level data quality do not exist. While MODIS products have associated quality assurance datasets useful for identify potential gaps, we developed a generic gap-finding approach to demonstrate the potential utility of our gap filling approach for a wide range of remotely sensed products. Gaps were identified by finding all pixels that contained a no-data or otherwise unacceptable value within the input mosaic that corresponded to usable pixels within the mean image, thus indicating that the pixel in question contained usable data on other dates. Unacceptable pixel values were identified by calculating a z-score for each pixel based on the mean and standard deviation images, and then searching for any pixel with an absolute z-score exceeding a user-defined threshold (we used 2.58, which corresponds to the 0.99 confidence interval, seesupplemental information for more details). When such a pixel was found we examined neighboring pixels (we used a neighborhood size of 40 to 80 pixels) to determine if they were similarly unusual with respect to the mean value of the pixel. If the original z-score was beyond a second user-defined threshold (we used ±0.2) from the median neighborhood z-score, or if too few neighboring pixels were found within a user-defined search radius (we used 10 km), the original pixel was reclassified as a gap. In practice, pixels removed by the despeckling algorithm typically represent approximately 5% of gap pixels or 0.5% of all usable pixels present in the final output images.
Based on the results of the gap identification process the flag image was modified to indicate whether pixels were (1) a no-data pixel that should be ignored in subsequent processing, (2) a usable raw value that could be passed directly through to the final output (and is suitable for use in the gap-filling models), or (3) a gap to be filled. A preliminary analysis of the raw imagery mosaics indicated that, on average, approximately 5–15% of the pixels within an image were gaps in need of filling (Table 1 ).
The MODIS data were collected on a per-tile basis and then merged using the MODIS reprojection tool (Dwyer and Schmidt, 2006 ) to create seamless mosaics for all of Africa. A total of 42 tiles were required to cover the continent for each image date (i.e., the day of the year corresponding to the center of the composite temporal window). The BRDF mosaics each consisted of seven spectral bands, three of which were needed to derive the EVI, and mosaics were created for each of these bands prior to deriving the EVI for each image date. The resulting data archives consisted of 594 EVI mosaics (from day 049, 2000 to 361, 2012), and 590 LST-day and LST-night mosaics (from day 065, 2000 to 361, 2012). Temporal mean and standard deviation images were derived on a per-pixel basis from the full mosaic archives for each of the three variables for subsequent use in the gap filling algorithms. Producing images of summary statistics was also useful for identifying pixels that never contain usable data (e.g., ocean pixels) that could be ignored in the gap-filling procedures, thus reducing run-time.
The initial step in the gap filling process was to identify gap pixels in need of filling through the use of a despeckling algorithm, which is a processing step that need only be used if corresponding datasets describing pixel-level data quality do not exist. While MODIS products have associated quality assurance datasets useful for identify potential gaps, we developed a generic gap-finding approach to demonstrate the potential utility of our gap filling approach for a wide range of remotely sensed products. Gaps were identified by finding all pixels that contained a no-data or otherwise unacceptable value within the input mosaic that corresponded to usable pixels within the mean image, thus indicating that the pixel in question contained usable data on other dates. Unacceptable pixel values were identified by calculating a z-score for each pixel based on the mean and standard deviation images, and then searching for any pixel with an absolute z-score exceeding a user-defined threshold (we used 2.58, which corresponds to the 0.99 confidence interval, see
Based on the results of the gap identification process the flag image was modified to indicate whether pixels were (1) a no-data pixel that should be ignored in subsequent processing, (2) a usable raw value that could be passed directly through to the final output (and is suitable for use in the gap-filling models), or (3) a gap to be filled. A preliminary analysis of the raw imagery mosaics indicated that, on average, approximately 5–15% of the pixels within an image were gaps in need of filling (
The mean and standard deviation percentages of gap pixels within the full Africa mosaics as calculated from the full imagery time-series (e.g., approximately 15% of a typical EVI mosaic consists of gap pixels).
| Dataset | Proportion of missing pixels per image (%) | |
|---|---|---|
| Mean | Standard deviation | |
| EVI | 14.77 | 5.93 |
| LST day | 5.25 | 2.28 |
| LST night | 8.51 | 3.28 |
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