Europe is a continent with diverse climatic conditions. The dominant climates are the Oceanic, the Mediterranean and the Continental ones. The western part of Europe has an oceanic climate, southern Europe has a Mediterranean climate and eastern Europe has a continental climate. Because of such heterogeneities, a vast range of extreme climatic events might occur in different areas. We define extreme climatic events the droughts, floods and heavy snowfall. Those events will be generically referred to in this research as anomalies. The purpose of this study is the identification of these extreme climatic events in the area of Europe, with the use of Special Sensor Microwave Imager (SSM/I) data at 37GHz frequency. The data that are used are Brightness Temperature (TB) values. The detection of the events will be achieved with the Polarization Difference Brightness Temperature (PDBT). The PDBT values can be related to changes to surface wetness and the surface geometry. It could be used as an indicator of an anomaly, because the higher the values of PDBT the higher the surface wetness. The methodological steps of the work consist in a statistical analysis of the SSM/I time-series, in the design of a detection algorithm of the anomalies under investigation and on the debate of its performance. The analysis of the temporally long SSM/I data will provide a first understanding of the data sensitivity to events under investigation and of their distribution for the statistical modelling of the Normalized Polarization Difference Brightness Temperature (NPDBT) indicator. The calculation of the NPDBT exploits the same principles as the well-known z-score index. The detection of the anomalies will be then achieved through thresholding the NPDBT index. Further information for the detection of anomalies is provided by the soil moisture time series from the Soil Moisture Active Passive (SMAP) sensor and the precipitation data from the Global Satellite Mapping of Precipitation (GSMAP). The soil moisture data appear to be more useful for the dry events, whereas the precipitation data for the flooding and the heavy snowfall events.

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.