Aportación al problema general de redes locales de alta precisión: condicionantes específicos de fijación de criterios teóricos y prácticos, de calificación de parámetros intermedios y resultados finales

Al diseñar, observar y calcular una Red Local de Alta precisión, cualquiera que sea su objetivo, se plantea siempre el problema de la fiabilidad de los datos, parámetros intermedios y resultados finales. La tendencia en el momento presente es que la exigencia en rigor y precisión, y en definitiva, de calidad, crezca y crezca. El concepto de precisión es fundamental en cualquier trabajo geodésica, y junto al de fiabilidad y costo, definen los tres parámetros fundamentales en cualquier actuación geodésica. [Nuñez et. al. 1991]. Se pude definir la fiabilidad como la capacidad de la red para detectar errores groseros en las observaciones. [Nuñez et. al. 1991]. Por tanto es necesario revisar los conceptos de figuras de error y algoritmos de cálculo. En ellos, incluso los redondeos practicados al realizar sucesivas operaciones aritméticas por el ordenador que se trate, tienen una importancia notable y no es indiferente la utilización de uno u otro equipo de los disponibles en el mercado. En esta de tesis se tratará, como ejemplo de lo expuesto, al resolver un sistema de ecuaciones por mínimos cuadrados Ax - K = R, (sabiendo que A es la matriz de diseño, K el vector de los términos independientes, y R el vector de los residuos), ya sea como red ligada o como red libre, nos podemos encontrar con que la matriz S = ATPA sea altamente sensible a cualquier operación de redondeo, resultando importantes discrepancias sobre el vector solución de las incógnitas X. En general, la sensibilidad detectada es inevitable y puede suponer un serio inconveniente en la obtención de resultados fiables en redes que requieran altos niveles de rigor y precisión, como es el caso del control de deformaciones. Sin embargo, antes de tomar la decisión extrema de repetir la observación, probablemente con nueva metodología e incluso, cambio de instrumentación, pueden intentarse mejorar la situación aplicando algún otro nuevo artificio de cálculo. Así pues debemos tener en cuentaAnquela Julián, AB. (2001). Aportación al problema general de redes locales de alta precisión: condicionantes específicos de fijación de criterios teóricos y prácticos, de calificación de parámetros intermedios y resultados finales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/5907

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

[EN] Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture moni-toring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific com-munity to introduce new users to the GNSS-IR technique.The authors want to thank the staff of the Cajamar Center of Experiences, and especially Carlos Baixauli, for their support and collaboration in the Paiporta experiment. The authors also want to thank Alfred Leick and Steve Hilla for their valuable comments and suggestions.Martín Furones, ÁE.; Luján García Muñoz, R.; Anquela Julián, AB. (2020). Python software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solutions. 24(4):1-7. https://doi.org/10.1007/s10291-020-01010-0S17244Chen Q, Won D, Akos DM, Small EE (2016) Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna. IEEE J Select Top Appl Earth Obs Rem Sens 9(10):4771–4780. https://doi.org/10.1109/JSTARS.2016.2565687Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture. IEEE T Geosci Rem Sens 52(1):537–543. https://doi.org/10.1109/TGRS.2013.2242332Chew CC, Small EE, Larson KM, Zavorotny UZ (2015) Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE T Geosci Rem Sens 53(5):2755–2764. https://doi.org/10.1109/TGRS.2014.2364513Chew CC, Small EE, Larson KM (2016) An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 20(3):525–537. https://doi.org/10.1007/s10291-015-0462-4Gurtner W, Estey L (2015) RINEX: the receiver independent exchange format version 3.03. ftp://igs.org/pub/data/format/rinex303.pdfLarson KM, Small EE, Gutmann ED, Bilich AL, Axelrad A, Braun JJ (2008a) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177. https://doi.org/10.1007/s10291-007-0076-6Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU (2008b) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35:L24405. https://doi.org/10.1029/2008GL036013Larson KM, Gutmann E, Zavorotny VU, Braun J, Williams M, Nievinski FG (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36:L17502. https://doi.org/10.1029/2009GL039430Larson KM, Braun JJ, Small EE, Zavorotny VU (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J Selec Top Appl Earth Obs Rem Sens 3(1):91–99. https://doi.org/10.1109/JSTARS.2009.2033612Larson KM, Nievinski FG (2013) GPS snow sensing: results from the EarthScope plate boundary observatory. GPS Solut 17(1):41–52. https://doi.org/10.1007/s10291-012-0259-7Leick A, Rapoport L, Tatarnikov D (2015) GPS satellite surveying, 4th edn. Wiley, Hoboken, p 840Martín A, Ibañez S, Baixauli C, Blanc S, Anquela AB (2020) Multi-constellation interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-24-3573-2020Nievinski GG, Larson KM (2014) An open source GPS multipath simulator in Matlab/Octave. GPS Solut 18:473–481. https://doi.org/10.1007/s10291-014-0370-zNischan T (2016) GFZRNX—RINEX GNSS data conversion and manipulation toolbox (Version 1.05). GFZ Data Serv. https://doi.org/10.5880/GFZ.1.1.2016.002Roesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut. https://doi.org/10.1007/s10291-018-0744-8Roussel N, Ramilien G, Frappart F, Darrozes J, Gay A, Biancale R, Striebig N, Hanquiez V, Bertin X, Allain A (2015) Sea level monitoring and sea estimate using a single geodetic receiver. Remote Sens Environ 171:261–277. https://doi.org/10.1016/j.rse.2015.10.011Roussel N, Frappart F, Ramillien G, Darroes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2º to 70º. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4781–4794. https://doi.org/10.1109/JSTARS.2016.2537847Sanz J, Juan JM, Hernández-Pajares M (2013) GNSS data processing. Volume I: fundamentals and algorithms. European Space Agency Communications, 223 ppSmall EE, Larson KM, Chew CC, Dong J, Ochsner TE (2016) Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects. IEEE J Selec Top Appl Earth Obs Rem Sens 9(10):4759–4770. https://doi.org/10.1109/JSTARS.2015.2504527Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2016) Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solut 20:641–654. https://doi.org/10.1007/s10291-015-0474-0Wan W, Larson KM, Small EE, Chew CC, Braun JJ (2015) Using geodetic GPS receivers to measure vegetation water content. GPS Solut 19:237–248. https://doi.org/10.1007/s10291-014-0383-7Zhang S, Roussel N, Boniface K, Ha MC, Frappart F, Darrozes J, Baup F, Calvet JC (2017) Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop. Hydrol Earth Syst Sci 21:4767–4784. https://doi.org/10.5194/hess-21-4767-201

Jurisdictional boundaries in Spain, survey and marking of boundaries in Teruel (Spain)

In the 1890 s and early 1900s, the National Geographic Institute (IGN) of Spain carried out geodetic studies required to georeference the boundaries of every single municipality in Spain, survey the boundaries and mark them out. The field notes for these studies still exist and can still be referenced today. Nowadays, most of the landmarks that were located in these studies have disappeared; replacing these monuments could be of great interest to the local government, both administratively and economically. The indeterminacy or change of municipal boundaries can lead to tax collection and even supply problems. This paper studies the accuracy of those studies. Furthermore, a technical method for locating the lost monuments is shown; this method could also be used to map the monuments in a precise and reliable way. In this way, the problem of replacing boundaries is subsequently analysed.Garrido-Villén, N.; Berné Valero, JL.; Antón Merino, A.; Anquela Julián, AB. (2014). Jurisdictional boundaries in Spain, survey and marking of boundaries in Teruel (Spain). Survey Review. 46(336):205-212. doi:10.1179/1752270613Y.0000000071S20521246336Berné J. L.et al. 2008. Catastro en España, Ed. UPV, Valencia.Capdevila i Subirana J. 2005. Treballs de la SCG, 60.Collier, P. (2009). International Boundary Surveys and Demarcation in the Late 19thand Early 20thCenturies. Survey Review, 41(311), 2-13. doi:10.1179/003962608x325457Dale, P. F. (2006). Reflections on the Cadastre. Survey Review, 38(300), 491-498. doi:10.1179/sre.2006.38.300.491Demir, O., Uzun, B., & Çete, M. (2008). Turkish cadastral system. Survey Review, 40(307), 54-66. doi:10.1179/003962608x253484Dirección General del Catastro, www.catastro.meh.es/esp/normativa1.asp?lu = m6&im = m6i#menu1_3 (accessed 23 June 2010).Fábrega Golpe J.et al. 2007. Proyecto que desarrolla de la Metodología para la optimización de la base de datos de líneas límite del Instituto Geográfico NacionalNational Geographical Institute of Spain (IGN), http://www.fomento.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/INSTITUTO_GEOGRAFICO/Geodesia/ (accessed 19 October 2010).Pirti, A., Arslan, N., Deveci, B., Aydin, O., Erkaya, H., & Hosbas, R. G. (2009). Real-Time Kinematic GPS for Cadastral Surveying. Survey Review, 41(314), 339-351. doi:10.1179/003962609x45158

Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain)

[EN] This paper presents the big data architecture and work flow used to download georeferenced tweets, store them in a NoSQL database, analyse them using the Apache Spark framework, and visualize the results. The study covers a complete year (from December 10, 2016 to December 10, 2017) in the city of Valencia (Eastern Spain), which is considered to be the third most important in Spain, having a population of nearly 800,000 inhabitants and a size of 135 km(2). The concepts of a specific event map and a specific event map with positive or negative sentiment are developed to highlight the location of an event. This approach is undertaken by subtracting the heat map of a specific day from the mean daily heat map, which is obtained by taking into account the 365 days of the studied period. This paper demonstrates how the proposed analysis from tweets can be used to depict city events and discover their spatiotemporal characteristics. Finally, the combination of all daily specific events maps in a single map, leads to the conclusion that the city of Valencia city has appropriate urban infrastructures to support these events.The authors would like to thank the comments and suggestions of the anonymous reviewers and the editor, which have helped to improve the original version.Martín Furones, ÁE.; Anquela Julián, AB.; Cos-Gayón López, FJ. (2019). Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain). Cities. (86):37-50. https://doi.org/https://doi.org/10.1016/j.cities.2018.12.014S37508

Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

[EN] Per capita arable land is decreasing due to the rapidly increasing population, and fresh water is becoming scarce and more expensive. Therefore, farmers should continue to use technology and innovative solutions to improve efficiency, save input costs, and optimise environmental resources (such as water). In the case study presented in this paper, the Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique was used to monitor soil moisture during 66¿d, from 3 December 2018 to 6 February 2019, in the installations of the Cajamar Centre of Experiences, Paiporta, Valencia, Spain. Two main objectives were pursued. The first was the extension of the technique to a multi-constellation solution using GPS, GLONASS, and GALILEO satellites, and the second was to test whether mass-market sensors could be used for this technique. Both objectives were achieved. At the same time that the GNSS observations were made, soil samples taken at 5¿cm depth were used for soil moisture determination to establish a reference data set. Based on a comparison with that reference data set, all GNSS solutions, including the three constellations and the two sensors (geodetic and mass market), were highly correlated, with a correlation coefficient between 0.7 and 0.85.Martín Furones, ÁE.; Ibañez Asensio, S.; Baixauli, C.; Blanc Clavero, S.; Anquela Julián, AB. (2020). Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrology and Earth System Sciences. 24(7):3573-3582. https://doi.org/10.5194/hess-24-3573-2020S35733582247Chan, S. K., Bindlish, R., O'Neill, P. E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M. H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martiìnez-Fernaìndez, J., Gonzaìlez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E. E., Zreda, M., Calvet, J.-C., Crow, W., and Kerr, Y.: Assessment of the SMAP passive soil moisture product, IEEE T. Geosci. Remote, 54, 4994–5007, 2016.Chen, Q., Won, D., Akos, D. M., and Small, E. E.: Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna, IEEE J. Sel. Top. Appl., 9, 4771–4780, 2016.Chew, C. C., Small, E. E., Larson, K. M., and Zavorotny, V. U.: Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture, IEEE T. Geosci. Remote, 52, 537–543, 2014.Chew, C. C., Small, E. E., Larson, K. M., and Zavorotny, U.Z.: Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data, IEEE T. Geosci. Remote, 53, 2755–2764, 2015.Chew, C. C., Small, E. E., and Larson, K. M.: An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil, GPS Solut., 20, 525–537, 2016.Hofmann-Wellenhof, B., Lichtenegger, H., and Wasle, E.: GNSS Global Navigation Satellite Systems, GPS, GLONASS, GALILEO and more, Springer, Vienna, Austria, New York, USA, 2008.Katzberg, S. J., Torres, O., Grant, M. S., and Masters, D.: Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: results from SMEX02, Remote Sens. Environ., 100, 17–28, 2005.Kerr, Y., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735, 2001.Larson, K. M. and Nievinski, F. G.: GPS snow sensing: results from the EarthScope plate boundary observatory, GPS Solut., 17, 41–52, 2013.Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Axelrad, A., and Braun, J. J.: Using GPS multipath to measure soil moisture fluctuations: initial results, GPS Solut., 12, 173–177, 2008a.Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Braun, J. J., and Zavorotny, V. U.: Use of GPS receivers as a soil moisture network for water cycle studies, Geophys. Res. Lett., 35, L24405, https://doi.org/10.1029/2008GL036013, 2008b.Larson, K. M., Braun, J. J., Small, E. E., and Zavorotny, V. U.: GPS multipath and its relation to near-surface soil moisture content, IEEE J. Sel. Top. Appl., 3, 91–99, 2010.Leick, A., Rapoport, L., and Tatarnikov, D.: GPS satellite surveying, 4th edn., John Wiley & Sons, Hoboken, New Jersey, USA, 840 pp., 2015.Li, G. and Geng, J.: Characteristics of raw multi-GNSS measurement error from Google Android smart devices, GPS Solut., 23, 1–5, https://doi.org/10.1007/s10291-019-0885-4, 2019.Lomb, N. R.: Least-squares frequency – Analysis of unequally spaced data, Astrophys. Space Sci., 39, 447–462, 1976.Masters, D., Axelrad, P., and Katzberg, S.: Initial results of land-reflected GPS bistatic radar measurements in SMEX02, Remote Sens. Environ., 92, 507–520, 2002.Mattia, F., Balenzano, A., Satalino, G., Lovergine, F., Peng, J., Wegmuller, U., Cartus, O., Davidson, M. W. J., Ki<span id="page3582"/>m S., Johnson, J., Walker, J., Wu, X., Pauwels, V. R. N., McNairn, H., Caldwell, T., Cosh, M., and Jackson, T.: Sentinel-1 & Sentinel-2 for SOIL Moisture Retrieval at Field Scale, IGARSS 2018–2018, IEEE I. Geosci. Rem. Sens. Symposium, 22–27 July 2018, Valencia, Spain, 6143–6146, https://doi.org/10.1109/IGARSS.2018.8518170, 2018.Press, W. H., Teukolsky, S. S., Vetterling, W. T., and Flannery, B. P.: Numerical recipes in Fortran 77, vol. 1, 2nd edn., Cambirdge University Press, New York, USA, 569–573, 1992.Roesler, C. and Larson, K. M.: Software tools for GNSS interferometric reflectometry (GNSS-IR), GPS Solut., 22, 80, https://doi.org/10.1007/s10291-018-0744-8, 2018.Roussel, N., Frappart, F., Ramillien, G., Darroes, J., Baup, F., Lestarquit, L., and Ha, M. C.: Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2∘ to 70∘, IEEE J. Sel. Top. Appl., 9, 4781–4794, 2016.Small, E. E., Larson, K. M., Chew, C. C., Dong, J., and Ochsner, T. E.: Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects, IEEE J. Sel. Top. Appl., 9, 4759–4770, 2016.Strang, G. and Borre, K.: Linear algebra, Geodesy and GPS, Wellesley-Cambride Press, 624 pp., available at: https://www.unavco.org/data/gps-gnss/derived-products/pbo-h2o/documentation/documentation.html#soil (last access: 18 December 2019), 1997.Vey, S., Güntner, A., Wickert, J., Blume, T., and Ramatschi, M.: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa, GPS Solut., 20, 641–654, https://doi.org/10.1007/s10291-015-0474-0, 2016.Wan, W., Larson, K. M., Small, E. E., Chew, C. C., and Braun, J. J.: Using geodetic GPS receivers to measure vegetation water content, GPS Solut., 19, 237–248, 2015.Zavorotny, V. U., Masters, D., Gasiewski, A., Bartram, B., Katzberg, S., Aselrad, P., and Zamora, R.: Seasonal polarimetric measurements of soil moisture using tower-based GPS bistatic radar, IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (IEEE Cat. No.03CH37477), Toulouse, France, 2003, vol. 2, 781–783, https://doi.org/10.1109/IGARSS.2003.1293916, 2003.Zhang, S., Roussel, N., Boniface, K., Ha, M. C., Frappart, F., Darrozes, J., Baup, F., and Calvet, J.-C.: Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop, Hydrol. Earth Syst. Sci., 21, 4767–4784, https://doi.org/10.5194/hess-21-4767-2017, 2017

Computational time reduction for sequential batch solutions in GNSS precise point positioning technique

[EN] Precise point positioning (PPP) is a well established Global Navigation Satellite System (GNSS) technique that only requires information from the receiver (or rover) to obtain high-precision position coordinates. This is a very interesting and promising technique because eliminates the need for a reference station near the rover receiver or a network of reference stations, thus reducing the cost of a GNSS survey. From a computational perspective, there are two ways to solve the system of observation equations produced by static PPP either in a single step (so-called batch adjustment) or with a sequential adjustment/filter. The results of each should be the same if they are both well implemented. However, if a sequential solution (that is, not only the final coordinates, but also those observed in previous GNSS epochs), is needed, as for convergence studies, finding a batch solution becomes a very time consuming task owing to the need for matrix inversion that accumulates with each consecutive epoch. This is not a problem for the filter solution, which uses information computed in the previous epoch for the solution of the current epoch. Thus filter implementations need extra considerations of user dynamics and parameter state variations between observation epochs with appropriate stochastic update parameter variances from epoch to epoch. These filtering considerations are not needed in batch adjustment, which makes it attractive. The main objective of this research is to significantly reduce the computation time required to obtain sequential results using batch adjustment. The new method we implemented in the adjustment process led to a mean reduction in computational time by 45%.This research was supported by the Spanish Science and Innovation Directorate project number AYA2010-18706 and the Generalitat Valenciana Geronimo Forteza research program with project number FPA/2014/056.Martín Furones, ÁE.; Anquela Julián, AB.; Dimas-Pagés, A.; Cos-Gayón López, FJ. (2017). Computational time reduction for sequential batch solutions in GNSS precise point positioning technique. Computers & Geosciences. 105:34-42. doi:10.1016/j.cageo.2017.03.023S344210

Python software to transform GPS SNR wave phases to volumetric water content

[EN] The global navigation satellite system interferometric reflectometry is often used to extract information about the environment surrounding the antenna. One of the most important applications is soil moisture monitoring. This manuscript presents the main ideas and implementation decisions needed to write the Python code to transform the derived phase of the interferometric GPS waves, obtained from signal-to-noise ratio data continuously observed during a period of several weeks (or months), to volumetric water content. The main goal of the manuscript is to share the software with the scientific community to help users in the GPS-IR computation.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Martín Furones, ÁE.; Anquela Julián, AB.; Ibañez Asensio, S.; Baixauli Soria, C.; Blanc Clavero, S. (2022). Python software to transform GPS SNR wave phases to volumetric water content. GPS Solutions. 26(1):1-5. https://doi.org/10.1007/s10291-021-01190-315261Chen Q, Won D, Akos DM, Small EE (2016) Vegetation using GPS interferometric reflectometry: experimental results with a horizontal polarized antenna. IEEE J Sel Top Appl Earth Obs Remote Sens 9(10):4771–4780Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Effects of near-surface soil moisture on GPS SNR data: development and retrieval algorithm for soil moisture. IEEE T Geosci Remote Sens 52(1):537–543Chew CC, Small EE, Larson KM, Zavorotny UZ (2015) Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE Trans Geosci Remote Sens 53(5):2755–2764Chew CC, Small EE, Larson KM (2016) An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 20(3):525–537Larson KM, Nievinski FG (2013) GPS snow sensing: results from the earthscope plate boundary observatory. GPS Solut 17(1):41–52Larson KM, Small EE (2015) PBO H2O data portal: documentation and derived data products. https://www.unavco.org/data/gps-gnss/derived-products/pbo-h2o/documentation/documentation.html#soil. Accessed Dec 2019Larson KM, Small EE, Gutmann ED, Bilich AL, Axelrad A, Braun JJ (2008a) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177Larson KM, Small EE, Gutmann ED, Bilich AL, Braun JJ, Zavorotny VU (2008b) Use of GPS receivers as a soil moisture network for water cycle studies. Geophys Res Lett 35:L24405. https://doi.org/10.1029/2008GL036013Larson KM, Braun JJ, Small EE, Zavorotny VU (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J Sel Top Appl Earth Obs Remote Sens 3(1):91–99Martín A, Ibañez S, Baixauli C, Blanc S, Anquela AB (2020a) Multi-constellation interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-24-3573-2020Martín A, Luján R, Anquela AB (2020b) Python software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut 24:94. https://doi.org/10.1007/s10291-020-01010-0Nievinski GG, Larson KM (2014) An open source GPS multipath simulator in Matlab/Octave. GPS Solut 18:473–481. https://doi.org/10.1007/s10291-014-0370-zRoesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut. https://doi.org/10.1007/s10291-018-0744-8Roussel N, Frappart F, Ramillien G, Darroes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2 to 70°. IEEE J Sel Top Appl Earth Obs Remote Sens 9(10):4781–4794Small EE, Larson KM, Chew CC, Dong J, Ochsner TE (2016) Validation of GPS-IR soil moisture retrievals: comparison of different algorithms to remove vegetation effects. IEEE J Sel Top Appl Earth Obs Remote Sens 9(10):4759–4770Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2016) Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GPS Solut 20:641–654. https://doi.org/10.1007/s10291-015-0474-0Wan W, Larson KM, Small EE, Chew CC, Braun JJ (2015) Using geodetic GPS receivers to measure vegetation water content. GPS Solut 19:237–248Zhang S, Roussel N, Boniface K, Ha MC, Frappart F, Darrozes J, Baup F, Calvet JC (2017) Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop. Hydrol Earth Syst Sci 21:4767–478

Kinematic GNSS-PPP results from various software packages and raw data configurations

[EN] In this study, kinematic precise point positioning (PPP) was tested. The raw data were taken from permanent stations, two airplane trajectories, a car trajectory and a walking trajectory. International GNSS Service (IGS) final products were used in the post-processing phase. The observations were processed using four different on-line software packages: the Canadian Spatial Reference System On-line Global GPS Processing Service (CSRS-PPP), the GPS Analysis and Position Software (GAPS), the Automatic Precise Positioning Service (APPS) and the Magic Global Navigation Satellite System (MagicGNSS). The results and comparisons are described in detail. The main conclusion is that an accuracy better than 10 cm for the planimetric measurements and better than 20 cm for the altimetric measurements can be achieved using the kinematic PPP method in any of the proposed tests. However, at present, the success of the technique is affected by the software used, and differences at the 0.5 m level can be found for the same specific epoch.This research is supported by the Spanish Science and Innovation Directorate project number AYA2010-18706.Martín Furones, ÁE.; Anquela Julián, AB.; Berné Valero, JL.; Sanmartín, M. (2012). Kinematic GNSS-PPP results from various software packages and raw data configurations. Scientific Research and Essays. 7(3):419-431. https://doi.org/10.5897/SRE11.1885S4194317

¿Evaluación continua o pruebas finales? Motivar y evaluar en tiempos de pandemia

[ES] Unos de los principales problemas que afecta a la universidad española es el abandono de los estudios que se concentra, fundamentalmente, en el primer curso. Se debe a varias causas: falta de orientación a la hora de elegir estudios, desajustes en los planes de estudio, uso de metodologías docentes obsoletas y falta de motivación del alumnado entre otras. El abandono en el primer curso de los estudios universitarios se ha visto agravado por la situación de confinamiento, en la que el alumno, que aún se encontraba adaptándose a las plataformas, aplicaciones y docencia del sistema universitario, ha tenido que readaptarse a la docencia no presencial. Este alumno nobel, requiere seguimiento, feedback y mucha motivación que le haga no sucumbir ante tanto reajuste. Por otra parte, la situación de pandemia requiere sensibilidad hacia aquellos alumnos que, por diferentes circunstancias, no tienen la posibilidad de realizar un seguimiento continuo o uniforme de la asignatura. Entonces: ¿Evaluación continua o pruebas finales? Para adaptarse a esta situación, se ha diseñado un plan de evaluación para una asignatura de primer curso de grado de ingeniería con 99 alumnos matriculados. Un sistema que se fundamenta en un par de pruebas finales obligatorias para todo el alumnado y una serie de trabajos e intervenciones optativas planteadas de forma continua a lo largo del cuatrimestre, que permiten controlar el seguimiento de la asignatura casi en tiempo real, motivando al alumnado que se ve recompensado con algunos puntos extra. Además, este trabajo opcional permite que el alumnado detecte y corrija a tiempo los fallos cometidos. Este sistema de evaluación tradicional, apoyado por el trabajo opcional continuo, ofrece las mismas oportunidades a todos los alumnos, a aquellos que requieren más atención para no sucumbir y a los que, por la pandemia, tienen problemas con el seguimiento de los estudios.Los autores agradecen la financiación recibida de la Universitat Politècnica de València a través del proyecto de innovación y mejora educativa PIME/19-20/147.Porres De La Haza, MJ.; Anquela Julián, AB.; Coll Aliaga, PE. (2022). ¿Evaluación continua o pruebas finales? Motivar y evaluar en tiempos de pandemia. En Proceedings INNODOCT/21. International Conference on Innovation, Documentation and Education. Editorial Universitat Politècnica de València. 495-501. https://doi.org/10.4995/INN2021.2021.1339549550

El plan integral de acompañamiento al estudiante en la Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica de la UPV

[EN] For more than two decades, the Universitat Politècnica de València (UPV) has been working on the University Tutorial Action Plan (PATU), focused on actions aimed at new students at the UPV. The tutorial action is a relevant pillar of the student¿s training process and an indicator of the quality of educational institutions. It is important to accompany, support and guide the students during their first year, through tutoring sessions, support activities, and personal attention from their tutors, but they need to be durable throughout their educational stage. The needs will be different, but having a team of tutors that they can support for the problems that arose during their stay at the university is necessary. For all these reasons, this academic year 2020- 21, coordinated by the Institute of Education Sciences (ICE), the UPV as a strategic line promotes the Comprehensive Student Accompaniment Plan (PIAE) in which a tutorial plan is designed for all academic courses. This article presents how the PIAE is developed in the Higher Technical School of Geodetic, Cartographic and Topographic Engineering.[ES] Desde hace más de dos décadas, la Universitat Politècnica de València (UPV) lleva trabajando en el Plan de Acción Tutorial Universitario (PATU), centrado en acciones dirigidas a los y las estudiantes de nuevo ingreso de la UPV. La acción tutorial es un pilar relevante del proceso formativo del estudiantado y un indicador de calidad de las instituciones educativas. Es importante, acompañar, apoyar y orientar al alumnado durante su primer año, mediante sesiones de tutoría, actividades de apoyo, y una atención personal por parte de sus tutores, pero hace falta que sean duraderas durante toda su etapa educativa. Las necesidades serán diferentes, pero disponer de un equipo de tutores en los que se puedan apoyar para los problemas surgidos durante su estancia en la universidad es necesario. Por todo ello, este curso académico 2020-21, coordinado por el Instituto de Ciencias de la Educación (ICE), la UPV como una línea estratégica impulsa el Plan Integral de Acompañamiento al Estudiante (PIAE) en el que se diseña un plan tutorial para todos los cursos académicos. En este artículo se presenta cómo se desarrolla el PIAE en la Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica.Coll-Aliaga, E.; Anquela Julián, AB.; Porres De La Haza, MJ.; Félix García, E. (2021). El plan integral de acompañamiento al estudiante en la Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica de la UPV. Universidad de La Laguna - AIDU. 2624-2638. http://hdl.handle.net/10251/177286S2624263