Integrating multiple land cover maps through a multicriteria analysis to improve agricultural monitoring in Africa Abstract   Monitoring agricultural land cover is highly relevant for global early warning systems such as ASAP (Anomaly hot Spots of Agricultural Production), because it represents the basis for detecting production deficits in food security assessment. Given the significant inconsistencies among existing land cover datasets, there is a need to obtain a more accurate representation of the spatial distribution and extent of agricultural area in Africa. In this research, we explore a fusion approach that combines the strength of individual datasets and minimises their limitations. Specifically, a semiautomatic method is developed, relying on multicriteria analysis (MCA) complemented with manual finetuning using the bestrated datasets, to generate two hybrid and static agricultural masks xe2x80x93 one for cropland and another for grassland. Following a comprehensive selection of land cover maps, each dataset is evaluated at country level according to five criteria: timeliness, spatial resolution, comparison with FAO statistics, accuracy assessment and expert evaluation. A sensitivity analysis is performed, based on an evaluation of the impact of weight settings on the resulting land cover. The proposed methodology is capable of improving agricultural characterisation in Africa. As a result, two static masks at 250xe2x80xafm spatial resolution for the nominal year 2016 are provided.

Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping The multidecadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clearsky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16day timeseries of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multitemporal data processing and characterization using machine learning provided with GLAD ARD serves as an endtoend solution for Landsatbased natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.