Assessing the quality of temporal high-resolution classifications with low-resolution satellite image time series

Abstract

Upcoming temporally and spatially high-resolution satellites such as Venus, SENTINEL-2, and Landsat Data Continuity Mission (LDCM) will provide very valuable data for land-cover and vegetation monitoring. However, owing to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. In this work, we propose using the higher temporal resolution of satellites with mid to low spatial resolutions such as the upcoming PROBA-V. The aim of this work is to study how images provided by satellites with a lower spatial resolution but with a higher temporal one can be used to obtain information about the temporal classification derived from satellite image time series (SITS) provided by high-resolution satellites such as Venus, SENTINEL-2, or LDCM.We show that the low-spatial-resolution SITS can be used to inform about the stability and relevance of high-spatial-resolution classification. Experiments include a wide variety of resolution ratios and study the use of each ratio from global to class-specific invalidation of the high-resolution classification map (computed from the high-spatial-resolution SITS).This work contributes to the assessment of the usefulness of the joint use of PROBA-V data and Venus/SENTINEL-2/LDCM images for land-cover monitoring.