Temporal filtering of successive MODIS data in monitoring a locust outbreak

The emergence of high temporal resolution satellite data such as MODIS enables timely monitoring of locust outbreaks from space. This monitoring is hampered by the effect of random atmospheric variations on satellite imagery, which may be suppressed through temporal filtering. This paper aims to evaluate the utility of temporally filtering successive MODIS data in monitoring an outbreak in East China. Of the eight vegetation indices examined, the commonly used NDVI was the most indicative of varying vegetation conditions caused by locust infestation inside the study area. The averaging of three successive days of satellite data improves the R 2 value of NDVI regression models by 0.227 over single‐day data. It also outperforms the data averaged from two successive days (a broader window size was not attempted due to the short span of the study period). Temporally, NDVI changed at varying rates daily during the outbreak. Early in the outbreak it increased at a reduced pace until 7.5 days. Afterwards it started to decrease at an accelerated rate. If temporally filtered with a proper window size, successive MODIS data allow the outbreak to be monitored accurately (R 2 = 0.696).