PhenoApp. A Google Earth Engine based tool for monitoring phenology


  • Diego García-Díaz Estación Biológica de Doñana (CSIC)
  • Ricardo Díaz-Delgado Estación Biológica de Doñana (CSIC)



Phenology, Phenocams, Google Earth Engine, Geemap, Python


PhenoApp application have been developed within the framework of the eLTER Plus and SUMHAL projects, as a tool aimed at scientists and managers of the sites integrated in the eLTER network, for which long-term phenology monitoring can be assessed. The application provides a dynamic map that allows the selection of any site in the network and queries the phenological metrics of each pixel or group of pixels generated with the Sentinel-2 time series of images using the Ndvi2Gif and PhenoPY python libraries. The application also integrates phenology products from MODIS (MCD12Q2.006) and Copernicus Sentinel 2 High Resolution Vegetation Phenology Product (HR VPP). In addition, the application incorporates a web form that allows the user to provide the phenology data obtained in situ (through direct observation or phenocams), which will be used to perform a validation of the different products obtained via satellite. As an example, we carried out a preliminary validation in one of the sites of the eLTER network located in the Doñana Natural Area (END). We used in situ data provided by the network of phenocams in the Doñana Biological Reserve since 2016 installed by the Singular Scientific and Technical Infrastructure of Doñana (ICTS-Doñana). A preliminary validation analysis highlights the need to consider the discrepancies between the different products and methods according to the phenological variability inherent in each ecosystem.


Download data is not yet available.

Author Biographies

Diego García-Díaz, Estación Biológica de Doñana (CSIC)

Laboratorio de Sistemas de Información Geográfica y Teledetección

Ricardo Díaz-Delgado, Estación Biológica de Doñana (CSIC)

Laboratorio de Sistemas de Información Geográfica y Teledetección


Amani, M., Ghorbanian, A., Ali Ahmadi, S., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q. and Brisco, B. 2020. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350.

Friedl, M., Gray, J., Sulla-Menashe, D., 2019. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. 2022-02-09.

García, D. 2020. Ndvi2Gif, Python Package Index - PyPI. Recuperado en mayo de 2020 de

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.

Haase, P., Frenzel, M., Klotz, S., Musche, M., Stoll, S. 2016. The long-term ecological research (LTER) network: Relevance, current status, future perspective and examples from marine, freshwater and terrestrial long-term observation. Ecological Indicators, 65 1-3.

Haase, P., Tonkin, J.D., Stoll, S., Burkhard, B., Frenzel, M., Geijzendorffer, I.R., Häuser, C., Klotz, S., Kühn, I., McDowell, W.H., Mirtl, M., Müller, F., Musche, M., Penner, J., Zacharias, S., Schmeller, D.S. 2018. The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Science of The Total Environment 613-614, 1376-1384.

Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., Jupyter development team. 2016. Jupyter Notebooks - a publishing format for reproducible computational workflows. Loizides, Fernando and Scmidt, Birgit (eds.) In Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press. pp. 87-90.

Lopatín, J., Paredes, J. 2021. PhenoPY. Recuperado en diciembre de 2019 de

Moore, R.T., Hansen, M.C. 2011. Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis 2011:IN43C-02. American Geophysical Union, Fall Meeting 2011.

Morgen W.V. Burke, Bradley C. Rundquist., 2021. Scaling Phenocam GCC, NDVI, and EVI2 with Harmonized Landsat-Sentinel using Gaussian Processes. Agricultural and Forest Meteorology, 300, 108316,

Richardson, A., Hufkens, K., Milliman, T. et al., 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data 5, 180028.

Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., Smets, B., Van Hoolst, R., Bonte, K., Ivits, E., Tong, X., Ardö, J., Eklundh, L. 2021. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment, 260,

Wohner, C., Peterseil, J., Klug, H. 2022. Designing and implementing a data model for describing environmental monitoring and research sites. In Ecological Informatics, 70, p. 101708). Elsevier BV.

Wu, Q. 2020. Geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software, 5(51), 2305.

Wu, Q. 2021. Interactive mapping and geospatial analysis with Leafmap and Jupyter. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science (SpatialAPI '21). Association for Computing Machinery, New York, NY, USA, Article 1, 1-2.





Practical cases

Funding data