Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2019-04, Vol.12 (4), p.1165-1174
• Main Authors: Peter, Brad G., Messina, Joseph P.
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural management; Analytical methods; Anomalies; Data; Data analysis; Data handling; Data mining; Data processing; Data visualization; Detection; Domains; Earth; Ecosystems; Error detection; error detection coding; Errors; Global health; Google; Outliers (statistics); Policies; Productivity; Remote sensing; Spatial data; spatial data structures; Statistical methods; Temporal resolution; Uncertainty; Vegetation mapping
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2019.2901404

Remotely sensed measures of productivity are frequently used to characterize global agriculture and vegetated ecosystems, and are often downscaled to describe local, remote areas where finer spatial and temporal resolution data are regularly unavailable. While data errors may propagate throughout any analytical procedure, those that are missed during delivery and preliminary data mining require more attention. Here, a collection of formerly and presently available global remote sensing products are compiled to demonstrate the temporal and geographic breadth of remote sensing uncertainty. Vegetation productivity measures are invaluable for monitoring global health, but erroneous estimates that go unrecognized may result in serious policy mistakes. It is eminently clear that generalizable and accessible a priori methods for anomaly detection are lacking and urgently needed so that data errors are recognized before public delivery and before widespread use. Simple yet effective statistics such as the modified Z-score, Tukey's outliers, and Geary's C are leveraged here to identify, locate, and visualize the types of outliers that remote sensing data users may elect to omit or correct. Contributing to the growing ensemble of Google Earth Engine methodologies, we propose this generalizable method of detecting spatial outliers for remote sensing error management by users across scientific domains.