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Land cover information plays an elementary role for regional water and land management, and is an essential variable for the assessment of ecosystem services and regional climate impact. This paper describes the generation of a regionally optimized land cover dataset for West Africa with a spatial resolution of 250 m, which is based on earth observation data from three optical and radar instruments. The choice of sensors is based on their individual strengths and weaknesses in assessing specific land surface types. Annual profiles of the optical Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed for the classification of vegetated classes including agriculture. The classification approach builds on random forest classification with learning data extracted from higher resolution land cover maps. Envisat Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS) time series are used, in combination with MODIS data, to delineate permanent and seasonal water bodies. Here, an approach integrating threshold classification and morphological operations is applied. Built-up areas of different densities are identified based on a seamless coverage of radar imagery collected by the satellites TanDEM-X and TerraSAR-X. The detection of settlements is based on an unsupervised classification scheme which exploits texture metrics and backscattering amplitudes of the fine resolution radar sensors. The accuracy assessment of the multi-sensor land cover map yields an overall accuracy of 80% at legend level 1 (9 classes) and 73% at the more detailed legend level 2 (14 classes). Comparisons with available wall-to-wall datasets of the region demonstrate the valuable information content of the presented West African land cover map. [ABSTRACT FROM AUTHOR]

Abstract: Evaluations of existing land cover maps have revealed that high landscape heterogeneity and small patch sizes are a major reason for misclassification. These problems globally occur in biomes of mixed vegetation structure and are particularly relevant for African savannas. This paper presents a multi-resolution approach to derive fractional cover of vegetation growth forms at sub-pixel level, aiming at an improved mapping of land cover in the African grassland, savanna and shrubland biome. Fractional cover is delineated for woody growth forms (trees and shrubs), herbaceous growth forms, and bare surface. The approach incorporates very high resolution (QuickBird/IKONOS, 0.6–1m), high resolution (Landsat TM/ETM+, 30m), and medium resolution data (MODIS, 250m). While QuickBird/IKONOS data are classified into discrete classes, at Landsat and MODIS resolutions, sub-pixel cover is delineated using non-parametric ensemble regression trees from the random forest family. The propagation of errors in the hierarchical multi-resolution approach is assessed with Monte Carlos simulations. The multi-resolution approach allows the adequate description of the heterogeneous vegetation structure in selected study regions of Southern Africa. The RMSE of the delineated fractional cover values range between 3.1% and 8.2% when compared with higher resolution data and between 4.4% and 9.9% when compared with field surveys. Errors at the Landsat resolution show minor influence on the accuracy of the MODIS results. Regarding the inter-resolution error propagation, for 90% of the Monte Carlo simulations, errors at the Landsat resolution resulted in RMSEs for MODIS increased by less than 4% (woody vegetation), 3.5% (herbaceous vegetation) and 2% (bare surface). [Copyright &y& Elsevier]

• Different vegetation drought response strategies are reflected differently in satellite data. • Land surface temperature reflects drought-induced reduction in surface conductance. • Optical satellite indices reflect forest and grassland drought response differently. Temperate forests and grasslands have different drought response strategies. Trees often control their stomatal opening to reduce water loss to prevent hydraulic failure and ensure the sustainable above-ground biomass production. In contrast, grasses generally have a less strong stomatal control and maintain high photosynthesis and transpiration until the soil moisture gets depleted. That is when their leaves wilt and the grasslands reduce their aboveground green biomass. Both the increased stomatal control and the reduction in aboveground biomass decrease the canopy-surface conductance and decrease the exchange of water and carbon between the leaves and the atmosphere. Here, we study to which extent remote sensing data reflect the drought-induced reduction in canopy-surface conductance for forests and grasslands. We use eddy covariance observations over 63 sites across the northern hemisphere to infer the conductance. We identify severe droughts from low soil moisture content and reduced canopy-surface conductance. We further analysed how the drought response is reflected in thermal and optical data derived from MODIS satellite data. The results show that the land surface temperature increases with drought-induced reductions in canopy-surface conductance for both forests and grasslands. By contrast, the optical indices (e.g., the normalized difference vegetation index) show a much stronger response for grasslands as compared to the forests. We conclude that the different canopy-level drought response strategies of trees and grasses are widespread and that these different responses are reflected in remote sensing data. Hence, a combination of thermal and optical satellite data should be used to monitor and study vegetation drought responses of forests and grasslands to ensure accurate inference on the implications on water, energy, and carbon fluxes. [ABSTRACT FROM AUTHOR]