remote sensing

This webinar series on climate smart agriculture innovation aims to explore, identify and encourage research and development collaboration between Dutch and Indian partners. The first three webinars focus on breeding, remote sensing, and water and soil. »

This article reviews developments in the application of remote sensing technologies in sub-Saharan Africa with a explicit emphasis on soil erosion monitoring. The remote sensing of soil erosion has gained substantial consideration, with considerable scientific research work having been conducted in the past, due to technological improvements that have resulted in the release of robust, cheap and high resolution datasets with a global footprint »

This article demonstrates the potential to track smallholder maize yield variation in western Kenya, using a combination of 1-m imagery and intensive field sampling on thousands of fields over 2 years. The emergence of satellite sensors that can routinely observe millions of individual smallholder farms raises possibilities for monitoring and understanding agricultural productivity in many regions of the world. »

This article uses remote sensing with a high spatial resolution (30 m) to monitor agricultural expansion in Burkina Faso. Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. »

This working paper by the International Institute for Environment and Development (IIED) highlights opportunities and limitations for understanding agricultural drivers of deforestation through remote sensing. In this working paper, 14 of the most commonly used land cover data products are identified, with summarised spatial data, temporal and thematic properties, and estimates of forest and cropland area are compared. The limitations to land cover data that result from their divergent predictions are described and the implications for using this data to assess the agricultural drivers of deforestation in Africa are discussed. »