The growing competition for finite land and water resources and the need to feed an ever-growing population require new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO's portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) are increasingly applied as a cost-effective means to support irrigation performance assessment and identify possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators, including uniformity, equity, adequacy, and land and water productivity differentiated by irrigation method (furrow, sprinkler, and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land, and climate are in near-real time and spatially distributed, with the finest spatial resolution in the area of 100gm. The WaPOR data were first validated agronomically by examining the biomass response to water, and then the data were used to systematically analyse seasonal indicators for the period 2015 to 2018 on g1/48000gha. The WaPOR-based yield estimates were found to be comparable to the estate-measured yields with ±20g% difference, a root mean square error of 19±2.5gtgha-1 and a mean absolute error of 15±1.6gtgha-1. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity but lower water productivity. The three irrigation methods have excellent uniformity (g1/494g%) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agroclimatic zones within FAO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

Efficient irrigation water management for an 18,000 ha sugarcane plantation in Xinavane in southern Mozambique is a challenge. Sugarcane is an irrigation intensive crop and its productivity is sensitive to water stress. Options to adopt field water management best practices and proper irrigation scheduling are limited due to the lack of plot-level information on the actual crop water use and stress levels throughout the growing season. Due to heterogeneity in cropping calendar within the sugarcane plantation, at a certain point of time, different plots are at different growth stages. This makes scheme level irrigation scheduling complex and calls for frequent crop water use information. To fill this gap, this study presents a novel approach where a combination of satellite imagery with local weather data is used to provide daily evaporation rates. The Priestley–Taylor equation is applied to quantify evaporation (soil evaporation + transpiration) using radiation and temperature data from a meteorological station and spatial albedo estimates derived from the Sentinel-2 satellites. The results show 20 meter resolution maximum crop evaporation estimates can be derived with the proposed methodology. Additionally, the results show NDVI in the last two crop stages is able to distinguish between poor and good performing fields. Therefore, NDVI can be a useful index to estimate actual evaporation. First, the evaporation estimates were corrected for the crop stage using NDVI proxies and an additional stress indicator was used to calculate the actual evaporation flux spatially. The spatial evaporation estimates provide the water manager with information on actual crop water use and biomass development, which is relevant to both crop monitoring and irrigation management water management when drought-related stress is filtered.