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. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91, 332-334. https://doi.org/ 10.1016/j.rse.2004.03.014Cho, AR., Suh, M.S. 2013. Detection of contaminated pixels based on the short-term continuity of NDVI and correction using spatio-temporal continuity. Asia-Pacific Journal of Atmospheric Sciences, 49(4), 511-525. https://doi.org/10.1007/s13143-013- 0045-7Geng, L., Ma, M., Wang, X., Yu, W., Jia, S. and Wang, H. 2014. Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China. Remote Sensing, 2014, 6, 2024-2049Hird, J.N., McDermid, G.J. 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113, 248-258. https://doi.org/10.3390/rs6032024Holben, B.N. 1986. Characteristics of maximum-value composite image from temporal AVHRR data. International Journal of Remote Sensing, 7, 1417- 1434. https://doi.org/10.1080/01431168608948945Jönsson, P., Eklundh, L. 2004. TIMESAT - A program for analyzing time-series of satellite sensor data. Computers and Geoscience, 30, 833-845. https://doi.org/10.1016/j.cageo.2004.05.006Julien, Y., Sobrino, J.A. 2009. Global land surface phenology trends from GIMMS database. International Journal of Remote Sensing, 30(13), 3495-3513. https://doi.org/ 10.1080/01431160802562255Julien, Y., Sobrino, J.A. 2010. Comparison of cloudreconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, 114, 618-625. https://doi.org/10.1016/j.rse.2009.11.001Julien, Y., Sobrino, J.A. 2012. Correcting Long Term Data Record V3 estimated LST from orbital drift effects. Remote Sensing of Environment, 123, 207- 219. https://doi.org/10.1016/j.rse.2012.03.016Julien, Y., Sobrino, J.A., Verhoef, W. 2006. Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999. Remote Sensing of Environment, 103, 43-55. https://doi.org/10.1016/j.rse.2006.03.011Ke, L., Ding, X., Song, C. 2013. Reconstruction of time series MODIS LST in central Qinghai-Tibet plateau using geostatistical approach. IEEE Geoscience and Remote Sensing Letters, 10(6), 1602-1606. https://doi.org/10.1109/LGRS.2013.2263553Lin, C.H., Lai, K.H., Chen, Z.B., Chen, J.Y. 2014. Patch-based information reconstruction of cloud-contaminated multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 163-174. https://doi.org/10.1109/ TGRS.2012.2237408Ma, M., Veroustraete, F. 2006. Reconstructing pathfinder AVHRR land NDVI timeseries data for the Northwest of China. Advances in Space Research, 37, 835-840. https://doi.org/10.1016/j.asr.2005.08.037Michishita, R., Jin, Z., Chen, J., Xu, B. 2014. Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure. ISPRS Journal of Photogrammetry and Remote Sensing, 91, 17-28. https://doi.org/10.1016/j.isprsjprs.2014.01.003Moreno, A., García-Haro, F.J., Martínez, B., Gilabert, M.A. 2014. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter. Remote Sensing, 6, 8238-8260. https://doi.org/10.3390/rs6098238Munyati, C., Mboweni, G. 2012. Variation in NDVI values with change in spatial resolution for semi-arid savanna vegetation: a case study in northwestern South Africa. International Journal of Remote Sensing, 34(7), 2253-2267. https://doi.org/10.1080/01431161.2012.743692Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., et al. 2007. Generating a long-term land data record from the AVHRR and MODIS instruments. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2007, 1021-1025, https://doi.org/10.1109/IGARSS.2007.4422974Poggio, L., Gimona, A., Brown, I. 2012. Spatiotemporal MODIS EVI gap filling under cloud cover: an example in Scotland. ISPRS Journal of Photogrammetry and Remote Sensing, 72, 56-72. https://doi.org/10.1016/j.isprsjprs.2012.06.003Roerink, G.J., Menenti, M., Verhoef, W. 2000. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. https://doi.org/10.1080/014311600209814Rouse, J.W., Haas, R.H., Scheel, J.A., Deering, D.W. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd Earth Resource Technology Satellite (ERTS) Symposium Proceedings, Vol. 1, 48-62.Sobrino, J.A. Julien, Y. 2011. Global trends in NDVI derived parameters obtained from GIMMS data. International Journal of Remote Sensing, 32(15), 4267-4279. https://doi.org/10.1080/01431161.2010 .486414Sobrino, J.A., Julien, Y. 2016. Exploring the validity of the Long Term Data Record V4 database for land surface monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-8, https://doi.org/10.1109/ JSTARS.2016.2567642Swinnen, E., Veroustraete, F. 2008. Extending the SPOT-VEGETATION time series (1998-2006) back in time with NOAA-AVHRR data (1985- 1998) for Southern Africa. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 558-572. https://doi.org/10.1109/TGRS.2007.909948Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150. https://doi.org/10.1016/0034-4257(79)90013-0Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D.A. Pak, E.W., Mahoney, R., Vermote, E.F., El Saleous, N. 2005. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26(20), 4485-4498. https://doi.org/10.1080/01431160500168686van Dijk, A., Callis, S., Sakamoto, C. and Decker, W. 1987. Smoothing vegetation index profiles: An alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogrammetric Engineering and Remote Sensing, 53, 1059-1067.Viovy, N., Arino, O., Velward, A. 1992. The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series International Journal of Remote Sensing, 13, 1585-1590. https://doi.org/10.1080/01431169208904212Weiss, D.J., Atkinson, P.M., Bhatt, S., Mappin, B., Hay, S.I., Gething, P.W. 2014. An effective approach for gap-filling continental scale remotely sensed time-series. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 106-118. https://doi.org/10.1016/j.isprsjprs.2014.10.001White, M.A., De Beurs, K.M., Didan, K., Inouye, D. W., Richardson, A.D., et al. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 15, 2335-2359. https://doi.org/10.1111/j.1365-2486.2009.01910.xXiao, Z., Liang, S., Wang, T., Liu, Q. 2015. Reconstruction of satellite-retrieved land-surface reflectance based on temporally-continuous vegetation indices. Remote Sensing, 7, 9844-9864. https://doi.org/10.3390/rs70809844Xu, L., Li, B., Yuan, Y., Gao, X., Zhang, T. 2015. A temporal-spatial iteration method to reconstruct NDVI time series datasets. Remote Sensing, 7, 8906- 8924. https://doi.org/10.3390/rs70708906Yang, G., Shen, H., Zhang, L., He, Z. and Li, X. 2015. A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 6008- 6021. https://doi.org/10.1109/TGRS.2015.2431315Zhou, J., Jia, L. and Menenti, M. 2015. Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS). Remote Sensing of Environment, 163, 217-228. https://doi.org/10.1016/j.rse.2015.03.01

A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data

Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics. Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors. Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover. Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California. This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)

Vegetation monitoring through retrieval of NDVI and LST time series from historical databases.

The PhD dissertation presented here falls into the Earth Observation field, specifically vegetation monitoring. This work consists in the extensive exploitation of historical databases of satellite images for vegetation monitoring through two parameters, which are the land surface temperature (LST) and a vegetation index (NDVI). Up to now, vegetation monitoring has been limited to the use of vegetation indices, so the addition of the land surface temperature parameter represents the main innovative character of this PhD study. This dissertation is divided into 5 chapters. The first chapter begins by introducing the theoretical aspects of NDVI and LST parameters, addressing the means for retrieving them from remotely sensed observations, as well as their main limitations. Then, an introduction to vegetal physiology is developed, which allows for understanding how NDVI and LST parameters are linked to plants. A bibliographical study is then presented, which stresses out the gaps in the exploitation of historical databases. The second describes the data used in this PhD. The instrument providing most of these data is embarked on the NOAA (National Oceanic and Atmospheric Administration) satellite series. This instrument is the AVHRR (Advanced Very High Resolution Radiometer). The AVHRR databases used in this work are the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) databases. Additional data used punctually are also described briefly. The third chapter describes the operations applied to the data to prepare their temporal analysis. These operations start with the calculations of vegetation index and land surface temperature parameters. The AVHRR data used in this work are contaminated by the orbital drift of NOAA satellites, so an important part of this doctorate consisted in developing a technique for correcting this effect. We chose to develop our own technique, which we validated by direct comparison with data retrieved by geostationary satellites. In the fourth chapter, the different methods used for data temporal analysis are presented. Those methods consist of trend detection, harmonic analysis, and fitting the temporal series to annual NDVI evolution curves. Then, a phenological analysis is presented, which allows for retrieval of trends in spring and autumn dates for most of the globe. These trends are validated by comparison with previous studies. The trend analysis for spring dates is then extended to the 1948-2006 period using air temperature data. The long-term observation of different NDVI indicators also allows for the detection of land vegetation changes, even in our case of coarse spatial resolution. Finally, two methods for NDVI temporal analysis are compared. In the fifth chapter, a quick presentation of simultaneous study of NDVI and LST is developed through a revision of previous results, followed by the observations carried out from the orbital drift corrected data. These observations allowed for the determination of indicators of NDVI and LST, thus enabling for the characterization of the vegetation at global scale. A harmonic analysis of NDVI and LST at European scale is also presented. The application of the developed indicators for simultaneous monitoring of NDVI and LST shows promising results. As a conclusion, the main results described above are summarized, and plans for a close future are presented. This PhD has also demonstrated that such work could be carried out in a small structure with limited resources. __________________________________________________________________________________________________ RESUMEN El trabajo de tesis doctoral aquí presentado consiste en el uso extensivo de bases de datos históricas de imágenes de satélite para el seguimiento de la vegetación terrestre, a través de dos parámetros; la temperatura de la superficie terrestre (LST por sus siglas en inglés) y el índice de vegetación NDVI. El primer capítulo de la memoria introduce las nociones de NDVI y LST desde una perspectiva teórica, así como sus principales limitaciones y sus vínculos con la fisiología vegetal. Un estudio bibliográfico permite poner el acento sobre las lagunas en el uso de las bases de datos históricas. El segundo capítulo describe los datos utilizados en este trabajo, proporcionados en su mayoría por el instrumento AVHRR (Advanced Very High Resolution Radiometer) a bordo de la serie de satélites de la NOAA (National Oceanic and Atmospheric Administration) a través de las bases de datos PAL (Pathfinder AVHRR Land) y GIMMS (Global Inventory Modeling and Mapping Studies). También se presentan datos adicionales que se usaron puntualmente. El tercer capítulo describe el proceso para obtener las series temporales de NDVI y LST, las cuales están contaminadas por la deriva orbital de los satélites NOAA. Hemos propuesto una técnica propia para su corrección, validada por comparación directa con datos obtenidos por satélites geoestacionarios. En el cuarto capítulo se introducen diferentes métodos utilizados para el análisis temporal de los datos. Se obtuvieron tendencias acerca de parámetros vinculados a la evolución anual de NDVI para la mayor parte del globo, validadas por comparación con estudios previos. En el quinto capítulo se presenta un análisis conjunto del NDVI y de la LST, seguido por la elaboración de indicadores de la evolución anual de estos dos parámetros. A continuación se presenta un análisis armónico del NDVI y de la LST para Europa. El uso de los indicadores desarrollados para el seguimiento simultáneo del NDVI y de la LST revela resultados prometedores. Por último se presentan las conclusiones más relevantes del trabajo realizado, así como planes de trabajo para un futuro próximo

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)