Advancing the remote sensing of precipitation

Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable

Advancing the remote sensing of precipitation

Satellite-based global precipitation data has addressed the limitations of rain gauges and weather radar systems in forecasting applications and for weather and climate studies. Inspite of this ability, a number of issues that require the development of advanced concepts to address key challenges in satellite-based observations of precipitation were identified during the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales at the University of California. These include quantification of uncertainties of individual sensors and their propagation into multisensor products warrants a great deal of research. The development of metrics for validation and uncertainty analysis are of great importance. Bias removal, particularly probability distribution function (PDF)-based adjustment, deserves more in-depth research. Development of a near-real-time probabilistic uncertainty model for satellitebased precipitation estimates is highly desirable

A remote sensing-based irrigation performance assessment: A case study of the Office du Niger in Mali

The irrigation performance of the Office du Niger in Mali, a large-scale rice-based irrigation scheme, was analysed with the use of remote sensing technology. The major advantage of remote sensing derived data over field measured data is that it provides system-wide, spatially distributed and objective information. Four irrigation performance indicators, entirely based on remote sensing, were applied at different organisational levels of the system. The surface energy balance algorithm for land model was applied to high-resolution Landsat images to calculate rice production and water consumption spatially. These maps were used to analyse the productivity of water, the uniformity of water consumption and head-/tail-enders issues at the level of the system, the five administrative zones and smaller management units (casiers). The sustainability of the system was assessed using a long-term time series of the normalised difference vegetation index. The results were discussed and interpreted with the irrigation managers of the Office du Niger. The analysis provided new insights in the performance of the system such as existing head–tail patterns in water consumption and rice yields.WatermanagementCivil Engineering and Geoscience