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The performance of seven operational high-resolution satellitebased rainfall products - Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Timeseries (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates - was investigated for Burkina Faso. These were compared to ground data for 2001-2014 on a point to- pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north-south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash-Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso. [ABSTRACT FROM AUTHOR]

Abstract: Water productivity in agriculture needs to be improved significantly in the coming decades to secure food supply to a growing world population. To assess on a global scale where water productivity can be improved and what the causes are for not reaching its potential, the current levels must be understood. This paper describes the development and validation of a WATer PROductivity (WATPRO) model for wheat that is based on remote sensing-derived input data sets, and that can be applied at local to global scales. The model is a combination of Monteith''s theoretical framework for dry matter production in plants and an energy balance model to assess actual evapotranspiration. It is shown that by combining both approaches, the evaporative fraction and the atmospheric transmissivity, two parameters which are usually difficult to estimate spatially, can be omitted. Water productivity can then be assessed from four spatial variables: broadband surface albedo, the vegetation index NDVI, the extraterrestrial radiation and air temperature. A sensitivity analysis revealed that WATPRO is most sensitive to changes in NDVI and least sensitive to changes in air temperature. The WATPRO model was applied at 39 locations where water productivity was measured under experimental conditions. The correlation between measured and modelled water productivity was low, and this can be mainly attributed to differences in scales and in the experimental and modelling periods. A comparison with measurements from farmer''s fields in areas surrounded by other wheat fields located in Sirsa District, NW India, showed an improved correlation. Although not a validation, a comparison with SEBAL-derived water productivity in the same region in India proved that WATPRO can spatially predict water productivity with the same spatial variation. [Copyright &y& Elsevier]

Abstract: The growing pressure on fresh water resources demands that agriculture becomes more productive with its current water use. Increasing water productivity is an often cited solution, though the current levels of water productivity are not systematically mapped. A global map of water productivity helps to identify where water resources are productively used, and identifies places where improvements are possible. The WATPRO water productivity model for wheat, using remote sensing data products as input, was applied at a global scale with global data sets of the NDVI and surface albedo to benchmark water productivity of wheat for the beginning of this millennium. Time profiles of the NDVI were used to determine the time frame from crop establishment to harvest on a pixel basis, which was considered the modelling period. It was found that water productivity varies from approximately 0.2 to 1.8kg of harvestable wheat per cubic metre of water consumed. From the 10 largest producers of wheat, France and Germany score the highest country average water productivity of 1.42 and 1.35kgm−3, respectively. The results were compared with modelling information by who applied the GEPIC model at a global scale to map water productivity, and by who used FAO statistics to determine water productivity per country. A comparison with Liu et al. showed a good correlation for most countries, but the correlation with the results by Chapagain and Hoekstra was less obvious. The global patterns of the water productivity map were compared with global data sets of precipitation and reference evapotranspiration to determine the impact of climate and of water availability reflected by precipitation. It appears that the highest levels of water productivity are to be expected in temperate climates with high precipitation. Due to its non-linear relationship with precipitation, it is expected that large gains in water productivity can be made with in situ rain water harvesting or supplemental irrigation in dry areas with low seasonal precipitation. A full understanding of the spatial patterns by country or river basin will support decisions on where to invest and what measures to take to make agriculture more water productive. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Identifying the biophysical factors that affect the performance of irrigated crops in semi-arid conditions is pivotal to the success of profitable and sustainable agriculture under variable climate conditions. In this study, soil physical and chemical variables and plots characteristics were used through linear mixed and random forest-based modeling to evaluate the determinants of actual evapotranspiration (ET a) and crop water productivity (CWP) in rice in the Kou Valley irrigated scheme in Burkina Faso. Multi-temporal Landsat images were used within the Python module for the Surface Energy Balance Algorithm for Land model to calculate rice ET a and CWP during the dry seasons of 2013 and 2014. Results showed noticeable spatial variations in PySEBAL-derived ET a and CWP in farmers' fields during the study period. The distance between plot and irrigation scheme inlet (D PSI), plot elevation, sand and silt contents, soil total nitrogen, soil extractable potassium and zinc were the main factors affecting variabilities in ET a and CWP in the farmers' fields, with D PSI being the top explanatory variable. There was generally a positive association, up to a given threshold, between ET a and D PSI , sand and silt contents and soil extractable zinc. For CWP the association patterns for the top six predictors were all non-monotonic; that is a mix of increasing and decreasing associations of a given predictor to either an increase or a decrease in CWP. Our results indicate that improving irrigated rice performance in the Kou Valley irrigation scheme would require growing more rice at lower altitudes (e.g. < 300 m above sea level) and closer to the scheme inlet, in conjunction with a good management of nutrients such as nitrogen and potassium through fertilization. • Biophysical factors affecting irrigation rice performance were assessed. • Landsat images were used to estimate ET a and CWP. • Large spatial variations were observed in ET a and CWP in farmers' fields. • Soil properties and plots position and elevation explained variabilities in ET a and CWP. • Improved plot and nutrient management can increase irrigated rice performance. [ABSTRACT FROM AUTHOR]

Small reservoirs are one of the most important sources of water for irrigation, domestic and livestock uses in the Upper East Region (UER) of Ghana. Despite various studies on small reservoirs in the region, information on their spatial-temporal variations is minimal. Therefore, this study performed a binary Random Forest classification on Sentinel-2 images for five consecutive dry seasons between 2015 and 2020. The small reservoirs were then categorized according to landscape positions (upstream, midstream, and downstream) using a flow accumulation process. The classification produced an average overall accuracy of 98% and a root mean square error of 0.087 ha. It also indicated that there are currently 384 small reservoirs in the UER (of surface area between 0.09 and 37 ha), with 20% of them newly constructed between the 2016-17 and 2019-20 seasons. The study revealed that upstream reservoirs have smaller sizes and are likely to dry out during the dry season while downstream reservoirs have larger sizes and retain substantial amounts of water even at the end of the dry season. The results further indicated that about 78% of small reservoirs will maintain an average of 54% of their water surface area by the end of the dry season. This indicates significant water availability which can be effectively utilized to expand dry season irrigation. Overall, we demonstrate that landscape positions have significant impact on the spatial-temporal variations of small reservoirs in the UER. The study also showed the effectiveness of remote sensing and machine learning algorithms as tools for monitoring small reservoirs. • There are currently 384 small reservoirs in the Upper East Region of Ghana. • Twenty percent of small reservoirs were constructed between the 2016-17 and 2019-20 seasons. • Upstream reservoirs have smaller sizes and are likely to dry out in the dry season. • Downstream reservoirs have larger sizes and retain substantial amounts of water by the end of the dry season. • About 78% of small reservoirs will maintain an average of 54% of their water surface area by the end of the dry season. [ABSTRACT FROM AUTHOR]