Multitemporal analysis of hydrological soil surface characteristics using aerial photos: A case study on a Mediterranean vineyard

Soil surface characteristics (SSC) constitute an important land surface property that drives the partitioning between infiltration and runoff. Therefore, knowledge of SSC is crucial for runoff-forecasting in hydrology. However, the difficulties in measuring spatial variabilities and temporal dynamics of SSC have limited the use of this property in operational hydrology at the catchment extent. Recent progresses have permitted to characterize hydrological SSC classes (H-SSC) with distinct infiltration rates, by implementing monotemporal classifications along with aerial photos. However, when dealing with Mediterranean vineyards, some classes still are difficult to discriminate on the basis of remotely sensed spectral and spatial information only.The objective of the current study was to propose a multitemporal classification that integrates a priori information about possible H-SSC evolutions, such as it is possible improving their characterization. H-SSC evolutions could be either natural, depending on rainfall events, or anthropogenic, driven by soil management practices. Knowledge of possible H-SSC evolutions was translated in the form of decision rules. It was applied to a time series of H-SSC class maps derived from a monotemporal classification of monthly aerial photos. As compared to the monotemporal classification, the multitemporal classification had two advantages for the identification of H-SSC classes. First, it allowed improving the discrimination of classes related to crusting processes, with increased performances between 35 and 48% relative. Second, it was able to detect H-SSC temporal evolutions in relation to soil management practices.

► We propose a classification for monitoring hydrological soil surface characteristics (H-SSC) in vineyards.
► This multitemporal classification relies on expert knowledge about possible H-SSC transitions.
► As compared to a monotemporal approach, it improved discrimination of H-SSC related to crusting processes.
► It also allowed detecting soil management practices such as mechanical and chemical weeding.