# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### Assessment of Planet Imagery
#### 5.1.2 Limitations and Challenges  
Several important limitations remain to be addressed before CubeSat imagery can be fully implemented into larger-scale hydrological observing systems. Currently, a serious limitation is lack of a cloud mask or a regionally/globally applicable automated cloud masking algorithm derived from Planet imagery. Automated cloud detection in multi-temporal visible/near-infrared satellite imagery is non-trivial [43] and will be particularly challenging with PlanetScope given only four bands and variable quality imagery. Until an automated cloud masking product or algorithm is developed, researchers must manually remove clouds which generally precludes broad-scale, high-resolution applications including surface water mapping. While minor, the observed geolocation error, combined with the narrow swath width of Planet CubeSat imagery, make differencing difficult at fine spatial scales (i.e., 50 m \(\times\) 50 m lakes). Use of an object-based approach (Figures 3 and 7) mitigates but does not fully eliminate this problem. Additional limitations associated with CubeSat imagery derive from its inexpensive sensors and multi-satellite constellation approach. PlanetScope imagery is calibrated to a radiometric uncertainty across satellites of 5-6% at 1-sigma [44] and RapidEye is calibrated to 4% uncertainty [45]. These values compare reasonably to Landsat at-sensor calibration uncertainty, which ranges from 3% (OLI) to 5% (TM and ETM+) to 10% (MSS) [46; 47; 48]. However, to enable daily or near-daily high resolution observations of the entire globe, Planet is presently operating 148 satellites in sun-synchronous orbit, meaning a collection of images of the same location likely come from many sensors. While all imagery is calibrated, Planet image quality, including signal to noise ratio and exposure, can vary between sensors, a non-trivial error source when time series analyses involve imagery from tens of sensors [44]. Figure 10 illustrates this variability in near infrared and NDWI for water bodies and surrounding wetlands in five PlanetScope images taken within a 10-day period in 2016. Though some of the observed differences may be due to atmospheric scattering and absorption, the non-linearity of between-image offsets suggests radiometric calibration and/or sensor variability is likely the cause. The lack of atmospherically corrected Planet imagery also complicates the interpretation of time series analyses, particularly those involving global radiance thresholds or complex land surface classifications [28]. Fully automated time series analyses using Planet data may thus require dark body atmospheric correction [49], adaptive thresholding as demonstrated here, or combination with other sensors [28]. The narrow range of the electromagnetic spectrum sampled by Planet's sensors (455-860 nm) also limits utility of the resulting imagery. For example, variability in shortwave infrared (1400-3000 nm) is critically important for distinguishing between cloud, snow, ice and watersurfaces [50], thus restricting the types of observations that can be made from Planet imagery in areas where snow occurs. Given these issues, hydrological applications of Planet imagery should presently be limited to relatively simple classifications, such as water mapping, which do not require atmospheric correction or rely on predefined radiance thresholds. All imagery classified in this analysis was visually checked and in general our automated classification method performed well for all imagery, suggesting image quality problems can be minor. However, further research is needed to examine radiance variability between different PlanetScope satellites and the effect this has on different types of classification. For example, combining PlanetScope imagery with Landsat-8 surface reflectance products may reduce cross-sensor calibration error [28].