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Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.

Introduction: The FAO-56 Penman-Monteith (PM) is one of the most solid and commonly used methods for estimating reference evapotranspiration (ETo); however, it requires meteorological data that are not always available, so an alternative is the use of reanalysis data. Objective: To estimate the error that the NASA-POWER (NP) system data can generate in the ETo of the Comarca Lagunera, Mexico. Methodology: Daily and decadal average ETo were estimated in five different ways. In each case, a different method was used to estimate ETo (FAO-56 PM or Hargreaves and Samani [HS]) and a different meteorological data source (measured, NP data or combination of both). Results: NP data can be used to provide temperature, solar radiation and relative humidity variables, but not wind speed. The NP data overestimate the measured ETo, an RMSE of 1.15 and 0.89 mm∙d-1 was found for daily and decadal periods, respectively. Limitations of the study: A grid error analysis could not be carried out because the number of stations is limited. Originality: The use of reanalysis data to estimate ETo has not been analyzed locally. Conclusion: When measured data are not available, NP data and the HS equation can be used. When using the FAO-56 PM method and NP data, the in situ wind speed must be available.

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