Due to increasing global population and increasing food demand, crop yield needs to be improved to forestall potential food shortages. To achieve optimal crop yield, irrigation is applied to replace water losses due to evapotranspiration (ET). Information on ET should be applied to optimize water allocations and water use. In this study, the correlation between ET and yield will be investigated on field-scale level to assess the potential of high resolution ET products as a tool to detect yield variation. The variability of crop yield and transpiration are caused by the variability in the topography, groundwater and soil properties.
Agricultural practices and field-scale water management demand high resolution (in meters) and high temporal resolution (daily to sub-daily) remote sensing products. With the arrival of new satellite platforms, such as Sentinel-2, the aforementioned remote sensing data can be improved significantly in spatial and temporal resolution. In order to compare the functionality of different remote sensing products, an assessment is executed for two satellite derived vegetation indices: normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), and two satellite derived evaporation products: WaPOR (Water Productivity through Open access of Remotely sensed derived data) and a newly developed evaporation algorithm from VanderSat. Within this research, the focus lies on assessing which dataset is able to observe the spatial difference and temporal patterns on field-scale level. Using a large sugarcane plantation in Xinavane, Mozambique, as a case study, we demonstrate how the spatial variability of the remote sensing results are correlated to the sugarcane yield. To assist irrigated agriculture we demonstrate that a high resolution evaporation product is needed to incorporate spatial variability in evaporation estimates. The analysis shows that the high resolution satellite derived vegetation indices are related to the spatial variability of yield. Our results indicate that NDWI has a strong positive correlation of 0.73 with yield, but NDVI has only 0.64. The actual evapotranspiration estimates have a moderately positive correlation with yield of 0.5 for WaPOR and 0.57 for VanderSat. Evaporation estimates should be related to yield to control irrigation properly. WaPOR and VanderSat use NDVI as a input for crop stress, these existing evaporation algorithms should incorporate high resolution spatial imagery as NDWI instead of NDVI to assist irrigation adequately. In order to use the satellite derived evaporation algorithms for agricultural practices and field-scale water management, future research should be focus on improving the relation between satellite derived evaporation algorithms and yield.

Accurate weather forecasting plays an important role in predicting precipitation events. With the warming climate the precipitable water vapour in the atmospheric is increasing. Since weather parameters as precipitable water vapor have a high spatial variability, interpolation of water vapor data over an area of hundreds of kilometer does not have a sufficient quality for weather prediction applications. Nowadays, researchers are investigating if the precipitable water vapour can be quantified using GPS transmitted signals in a densified GPS network. An accurate quantification of the ionospheric delay is important to efficiently calculate the precipitable water vapour. Moreover, the ionospheric delay is the biggest error and limitation of the GPS signal. It is important to understand how the ionospheric delay varies spatially and in time. Therefore, variability in the ionospheric delay is an interesting factor in weather forecasting and climate change. To monitor the ionospheric delay a high temporal (in minutes) and spatial resolution (in km-grid) is needed, because the ionospheric delay changes spatially and throughout the day. A possibility to achieve this is to densify GPS networks. Previous research has shown that it is possible to measure the ionospheric delay with dual frequency receivers. In developing countries this densification of GPS networks cannot be achieved with expensive dual-frequency receivers. This study investigates if a higher receiver network density can be achieved with the help of low-cost single frequency receivers. Therefore, a densified GPS network of dual and single frequency receivers is set-up in and around Kampala, Uganda. This research demonstrates how the Satellite-specific Epoch-difference Ionospheric Delay model (SEID) can be used to compute the ionospheric delay for a single frequency receiver through time. The SEID model creates a second frequency for a single frequency receiver which is used to resolve the ionospheric delay. The intensity of the ionospheric delay depends on the electrons in the ionosphere. The number of free electrons in the path of a signal is expressed as the total electron content. This research shows how to compute the total electron content in the ionospheric layer of the atmosphere. After computing the second frequency for the single frequency receivers the observations need to be processed using Precise Point Positioning (PPP) to compute the precipitable water vapour. As a case study Uganda is chosen, because it is located on the equator. The ionospheric delay fluctuates more at the equator so this is an interesting region to investigate the variability. The analysis shows that an high accuracy of the GPS signal is needed to create desirable results. Therefore, field campaigns with single frequency and dual frequency receivers should incorporate antennas with noise reduction. In order tot assess the accuracy of the ionospheric delay obtained by using single and dual frequency receivers, future research should be focus on better network set-up and getting the right equipment with better noise reduction