Water resources engineering requires data that cover space and time accurately, and this is hard to provide in a country like Iraq, where studies report that meteorological stations were out of service for a long period and some were damaged by military operations. Here remote sensing comes in as an alternative or complementary source, since satellites monitor rainfall, evaporation, soil moisture, stored water, and the extent of water bodies, and cover areas that ground measurements do not reach. Foremost among these data is the CHIRPS product, a quasi-global rainfall record with a spatial resolution of 0.05 degrees, which blends rainfall estimates derived from infrared and cold cloud duration with rain gauge station data. The product comes in two versions: a preliminary one available within about two days, and a final one released about three weeks later after merging the best available station data. The third version of CHIRPS has been operational since January 2025, covering about forty years, from 1981 to near-present. Studies of the Middle Euphrates basin that we discussed earlier relied on it, and its correlation with ground stations ranged between 0.83 and 0.97, depending on the station. Its closest relative is IMERG, part of NASA’s Global Precipitation Measurement (GPM) mission. It estimates rainfall at a resolution of about 10 square kilometers every half hour since June 2000, which makes it suitable for studying short storms and flash floods. It is run in three versions: an early run after about four hours, a late run after about fourteen hours, and a final run after several months, in which the estimates are adjusted using station measurements. Its developers caution that the skill of the estimates is lower over complex terrain, coastal areas, and frozen surfaces, and that changes in the satellite constellation over the years may introduce artificial effects in long-term trend studies. There is also a broader family of products, including GSMaP, PERSIANN, GPCP, and the ERA5-Land reanalysis. Several studies have compared them in arid regions. In Iraq, one study concluded that satellite rainfall data must be corrected with station measurements to reduce over- and underestimation, and that the corrected version of GSMaP estimates rainfall reasonably well in an arid climate like Iraq’s. In Sudan, CHIRPS was found to underestimate by 5–13%, while IMERG overestimates by 25–42%. These figures represent a specific region and should not be generalized, but they show that the best product differs from place to place. Remote sensing is not limited to rainfall. The GRACE satellites measure changes in the gravity field, and thereby detect changes in total water storage, including groundwater, which researchers describe as currently the only method for estimating groundwater change from space. GRACE data revealed that the Tigris and Euphrates basins, including parts of Turkey, Syria, Iraq, and Iran, lost about 144 km³ of stored freshwater between 2003 and 2009, with about 60% of the loss attributed to groundwater pumping. In the same study, Landsat images were used to show the shrinking of Lake Qadisiyah between 2006 and 2009. In addition, other data enter the daily work of the water resources engineer: digital elevation models such as SRTM to delineate basins and drainage paths, Landsat and Sentinel imagery to produce land-use maps and measure the areas of marshes and reservoirs, and evapotranspiration and soil moisture data to estimate water requirements. Each of these layers feeds a hydrological model or a drought index, as in SPI and HEC-HMS. However, this abundance does not mean that satellite figures are automatically correct. Bias in satellite rainfall varies with terrain, cloud type, and rainfall intensity, so it is recommended to correct it with local measurements. In hot desert climates, monitoring rainfall with satellite sensors remains a challenge. Relying on the correlation coefficient alone can also be misleading, because a high correlation often hides a clear bias: a product may follow the shape of the series well and still be wrong in its magnitude. In addition, CHIRPS in its daily series depends on infrared and reanalysis data for daily variability, while monthly variability is corrected using station measurements, so it is more reliable at the monthly scale than at the daily scale. In light of this, the engineer can follow practical steps: choose the product according to the purpose, since CHIRPS is suitable for monthly indicators and drought analysis, while IMERG is suitable for short events and flash floods. Then compare it with available stations using several metrics, such as the correlation coefficient, RMSE, MAE, and NSE, rather than a single metric, correct the bias if it appears, and then feed it into the models. CHIRPS can be accessed easily through platforms such as Google Earth Engine, which provides it at a five-day time step and a resolution of about 5.5 km. Thus, remote sensing gives the engineer what was missing in data-scarce regions, but it does not exempt them from verification. The real value of satellite products appears when they are read as estimates that need local calibration, and integrated with ground measurements rather than fully replacing them. Presented by: Asst. Lect. Zahraa Hussein Mohammed Sources: Funk et al. (2015), Scientific Data, on CHIRPS; the Climate Hazards Center (UCSB); NASA documentation for IMERG Version 7; Voss et al. (2013) and NASA’s statement on GRACE in the Tigris and Euphrates basins; studies evaluating satellite products in Iraq, Egypt, Sudan, and Saudi Arabia (MDPI and others).