TISSBERT: una referencia para la validación y la comparación de métodos para la reconstrucción de series temporales de NDVI

[EN] This paper introduces the Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) dataset, intended to provide a benchmark for the validation and comparison of time series reconstruction methods. Such methods are routinely used to estimate vegetation characteristics from optical remotely sensed data, where the presence of clouds decreases the usefulness of the data. As for their validation, these methods have been compared with previously published ones, although with different approaches, which sometimes lead to contradictory results. We designed the TISSBERT dataset to be generic so that it could simulate realistic reference and cloud-contaminated time series at global scale. To that end, we estimated both cloud-free and cloud-contaminated Normalized Difference Vegetation Index (NDVI) statistics for randomly selected control points and each day of the year from the Long Term Data Record Version 4 (LTDR-V4) dataset by assuming different statistical distributions. The best approach was then applied to the whole dataset, and validity of the results were estimated through the Kolmogorov-Smirnov statistic. The dataset elaboration is described thoroughly along with how to use it. The advantages and drawbacks of this dataset are then discussed, which emphasize the realistic simulation of the cloud-contaminated and reference time series. This dataset can be obtained from the authors upon demand. It will be used in a next paper to compare widely used NDVI time series reconstruction methods.[ES] En este trabajo se presenta la base de datos titulada Time Series Simulation for Benchmarking of Reconstruction Techniques (TISSBERT) con el propósito de ofrecer una herramienta para la validación y la comparación de métodos para la reconstrucción de series temporales. Tales métodos se usan de manera rutinaria para la estimación de características de la vegetación a partir de datos obtenidos por teledetección óptica, donde la presencia de nubes disminuye su utilidad. En cuanto a su validación, estos métodos se han comparado con otros publicados anteriormente, aunque desde perspectivas diferentes, lo cual conduce a resultados contradictorios. La base de datos TISSBERT se ha diseñado como una herramienta genérica para una simulación realista a escala global de series temporales de referencia o contaminadas por nubes. Para ello, se estimaron estadísticas de Normalized Difference Vegetation Index (NDVI) con y sin contaminación de nubes para unos píxeles de control seleccionados de manera aleatoria, y para cada día del año, usando la base de datos Long Term Data Record Version 4 (LTDR-V4), y probando con varias distribuciones estadísticas. La mejor metodología se aplicó al conjunto de la base de datos, y la validez de los resultados se comprobó con la prueba de Kolmogorov-Smirnov. La elaboración de la base de datos se describe detalladamente así como la manera de usarla. Finalmente, se analizan las ventajas y los inconvenientes de la base de datos TISSBERT, los cuales enfatizan la simulación realista de series temporales de referencia y con contaminación nubosa. Esta base de datos se puede obtener gratuitamente de los autores, y se usará en un futuro para comparar métodos usuales de reconstrucción de series temporales de NDVI.This work was supported by the Spanish Ministerio de Economía y Competitividad (CEOS-SPAIN2, project ESP2014-52955-R and SIM, project PCIN-2015-232). The authors also thank NASA for the free access to the LTDRV4 data.Julien, Y.; Sobrino, JA. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección. (51):19-31. https://doi.org/10.4995/raet.2018.9749SWORD193151Beck, P., Atzberger, C., Hogda, K.A., Johansen, B. Skidmore A. 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100, 321-334. https://doi.org/ 10.1016/j.rse.2005.10.021Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L. 2004. 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