In Manica Province in Central Mozambique, agricultural activities consist for a significant part of farmer-led irrigated agriculture. These farmers construct and maintain their own irrigation systems with local inputs and have a commercial intent. Organization is often individual and farmers receive zero to minimal external support from donors, government or non-governmental organizations. This type of agriculture seems to work quite well. It is increasing quite fast and has a high production rate per hectare. In addition, it can boost agricultural production and contributes to higher food security, poverty alleviation and economic growth. However, it is unclear what the actual extent of irrigated agriculture is. A way to obtain this might be making use of satellite imagery and remote sensing. However, the feasibility of this depends on various factors and is not proved yet. Therefore, this study aims to provide insight in the possibilities and limitations of remote sensing regarding the identification and mapping of farmer-led irrigated agriculture in Central Mozambique, by using optical satellite imagery combined with ground data. This study consists of three parts: ground data collection, classification and additional analysis. Ground data is collected during fieldwork in Central Mozambique in three catchments. Classification is performed with a Maximum Likelihood classifier and uses optical satellite imagery acquired by Sentinel 2. Additional analysis consists of terrain analysis with a Height Above Nearest Drainage raster, examination of distances to streams for land uses throughout the research area, determination of the reach of irrigation canals and looking into the possibilities of thermal remote sensing. The classification results are mixed and inconsistent, especially regarding irrigated fields and light seasonal vegetation. A more detailed analysis of spectral signatures and scatterplots shows spectral overlap between these land uses. Analysis on field level or with a more detailed division in land uses shows that spectral responses reflect agricultural practices in some cases, but in general results are unreliable. Results of the additional analysis show similarities between irrigated fields and light vegetation as well for Height Above Nearest Drainage and thermal remote sensing. Also, distance to streams is not suitable as an indicator for irrigation, because irrigation canals increase the reach of the streams. It can be concluded that optical remote sensing as applied by this study does not give accurate results regarding the identification and mapping of farmer-led irrigated agriculture in the study area, because of similarities in the spectral responses of irrigated fields and light vegetation. This low inter-class separability is mainly a result of the heterogeneity of the area and the flexibility of agricultural practices. Due to these diverse practices, the agricultural plots show different and unique patterns both over time and over space, which makes it hard to generalize and classify them. However, even though an accurate substantiation of the extent of farmer-led irrigated agriculture is not feasible, valuable information obtained by this study contributes to better grasping the presence of irrigated agriculture in Ruaca, Chirodzo and Godi catchments.

More than half of Vietnam's national rice production is produced in the Mekong Delta. Many hectares of rice were damaged due to extensive salinity intrusion during the dry season of 2016. The impact of salinity intrusion on rice production is studied by (1) mapping the impact using remote sensing data (MODIS NDVI time-series) and (2) investigating how the impact affects farmers' lives. Ground data is collected in Soc Trang province through interviews with commune officials and farmers. Quantitative data includes damages to rice areas as aggregated by the local government and land use points for classification.

Rice areas are mapped through supervised classification, using extreme gradient boosting. Peak detection analysis is used to distinguish distinct rice cropping schedules within district boundaries. Compared against an evaluated climatology, hampered rice growth is mapped for two hot-spots (Long Phu and Nga Nam district). This can be attributed to salinity problems through interpretation of ground collected information and an understanding of the local salt-intrusion mechanisms. Reconciling affected rice grid cells mapped through satellite analysis with hectares of damaged rice recorded by the local government is difficult.

Analyzed perspectives of salinity affected farmers highlight the uncertain current conditions and future for rice production in the Mekong Delta. Local government officials and farmers feel the changing climate conditions and the urge to respond through preventive or adaptive measures to avoid similar impacts of salinity intrusion in the future. In this research, soft information gathered through interviews provided contextual learning opportunities to better direct, develop and interpret remote sensing analysis and results.