Automatic unmixing of MODIS multi-temporal data for inter-annual monitoring of land use at a regional scale (Tensift, Morocco)

Abstract

The objective of this study is to develop an approach for monitoring land use over the semi-arid Tensift–Marrakech plain, a 3000 km² intensively cropped area in Morocco. In this objective, the linear unmixing method is adapted to process a 6-year archive of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 16-day composite data at 250 m spatial resolution. The result of the processing is a description of land use in terms of fractions of three predominant classes: orchard, non-cultivated area and annual crop. The typical signatures of land classes – endmembers – are retrieved on a yearly basis using an automated algorithm that detects the most pure pixels in the study area. The algorithm first extracts typical NDVI profiles as potential endmembers, then selects the profiles that have the best ability to reproduce the variability of MODIS NDVI time series over the study area. The endmembers appear stable over the 6 years of study and coherent with the vegetation seasonality of the three targeted land classes. Validation data allow us to quantify the error on land-use fractions to about 0.10 at 1 km resolution. Land-use estimates are consistent in space and time: the orchard class is stable, and differences in water availability (irrigation and rainfall) partly explain a part of the inter-annual variations observed for the annual crop class. The advantages and drawbacks of the approach are discussed.

Acknowledgements

This research was part of the SUDMED programme. In addition to the Institut de Recherche pour le Développement (IRD), this research was funded by the PLEIADeS project of the European Commission (Contract GOCE 037095) and by the Programme d'Action Intégrée du Comité Mixte Interuniversitaire Franco-Marocain (PAI Volubilis 06/148). We are indebted to the staff and the directors of ORMVAH and Agence du Bassin Hydraulique du Tensift (ABHT) for their technical assistance in the collection of irrigation and rainfall data. We thank Albert Olioso (Unit Mixte de Recherche Climat Sol et Environnement (UMR CSE), Avignon, France) and Hervé Cardot (Unité Mixte de Recherche Centre d'Economie et Sociologie Appliquées à l'Agriculture et aux Espaces Ruraux (UMR CESAER), Dijon, France) for their scientific help during the course of this study. French CNRS-PNTS (Centre National de Recherche Scientifique-Programme National de Télédétection Spatiale) and CNES-ISIS (Centre National d'Etudes Spatiales-Incitation à l'utilisation Scientifique des Images SPOT) programmes are acknowledged for their support.