Comparison of AVHRR, MODIS and VEGETATION for land cover mapping and drought monitoring at 1 km spatial resolution

Low spatial resolution remote sensors are one of the best data sources for large area land cover mapping and drought monitoring. This study was concerned with identifying which of the three most operational such sensors (AVHRR, MODIS, and VEGETATION), were likely to help produce the best results within the mentioned applications. A rigorous review of the sensors’ characteristics led to the hypothesis that in land cover mapping and drought monitoring applications MODIS is most likely to achieve the best results followed by VEGETATION and lastly by AVHRR. This hypothesis was tested against experimental results generated within this study. A methodology was developed allowing for unbiased relative comparison of the capacity of the sensors’ Solar Reflective Bands (SRBs) to map land cover, and was applied to data collected over the UK and Greece, for which maps were produced using data collected by each sensor over the same dates and sites, and accuracy estimated using reference data. In the majority of cases the most accurate maps were produced by MODIS data; however, there were cases when maps produced by AVHRR and particularly VEGETATION data were more accurate. Drought monitoring methodologies for low resolution data require historical Normalised Difference Vegetation Index (NDVI) records extending longer than MODIS and VEGETATION operational times. Towards solving this limitation, the relationships between the sensors’ NDVI measurements over the same targets were investigated. It was found that NDVI data for one sensor could be predicted from NDVI data collected by another sensor with considerable accuracy. Consequently, MODIS and VEGETATION historical NDVI records could be extended based on past AVHRR data, and applications could be benefited by interchanging sensors for provision of NDVI data in the event of a sensor failure. These extended datasets were used to assess drought conditions over Ethiopia with the aim of using the Vegetation Productivity Indicator (VPI) methodology. The sensors’ NDVI data responsiveness to rainfall was assessed, finding MODIS NDVI data to best reflect rainfall conditions, and likely to produce more accurate VPI results. Overall the experimental results generated in this study supported the initial hypothesis.