Published November 9, 2026 | Version v0.3.18
Software Open

MonitorEO

Authors/Creators

  • 1. Universidad de Granada

Description

MonitorEO-OBSNEV is an open monitoring tool of Essential Biodiversity Variables based on satellite sensors and models generated by the University of Granada. It enables citizens, managers, and scientists to create maps and graphs of key ecosystem metrics to support decision-making.

MonitorEO is an output of the EVEREST Project (PID2023-151939OB-I00), funded by MICIU/AEI 10.13039/501100011033, and co-funded by ERDF – European Union, as well as of the NevadEO Project (C-EXP-074-UGR23), co-funded by the ERDF 2014-2020 Program and the Regional Ministry of Economy, Knowledge, Enterprise, and Universities of the Regional Government of Andalusia. It is also an output of the PRESINMED Project (Preserving the singularity of Mediterranean high-mountain biodiversity hotspots: a NBS approach, PCI 2025-163175) of the Biodiversa+ NBS 2023 call.

MonitorEO computes with Google Earth Engine (Gorelick et al., 2017). The references are those of the formulas and algorithms of its variables, of the Earth Engine datasets that it uses (cited as their pages of the Earth Engine catalog suggest) and of its methods; each result of MonitorEO lists its own.

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Software documentation: https://github.com/monitoreo-obsnev/monitoreo/wiki (URL)
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Software: https://github.com/monitoreo-obsnev/monitoreo (URL)

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Repository URL
https://github.com/monitoreo-obsnev/monitoreo

References

  • Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5, 170191. https://doi.org/10.1038/sdata.2017.191
  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007
  • Climate Hazards Center (2025). CHIRPS: Rainfall estimates from rain gauge and satellite observations, version 3 [Data set]. University of California, Santa Barbara. https://doi.org/10.15780/G2JQ0P
  • Cook, M., Schott, J., Mandel, J., & Raqueno, N. (2014). Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a Land Surface Temperature (LST) product from the archive. Remote Sensing, 6(11), 11244–11266. https://doi.org/10.3390/rs61111244
  • Didan, K. (2021). MODIS/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MYD13Q1.061
  • Didan, K. (2021). MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD13Q1.061
  • Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., et al. (2017). An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534–545. https://doi.org/10.1093/biosci/bix014
  • Earth Resources Observation and Science (EROS) Center (1982). Landsat 4-5 Thematic Mapper Level-1, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P918ROHC
  • Earth Resources Observation and Science (EROS) Center (1999). Landsat 7 Enhanced Thematic Mapper Plus Level-1, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P9TU80IG
  • Earth Resources Observation and Science (EROS) Center (1999). Landsat 7 Enhanced Thematic Mapper Plus Level-2, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P9C7I13B
  • Earth Resources Observation and Science (EROS) Center (2013). Landsat 8-9 Operational Land Imager / Thermal Infrared Sensor Level-1, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P975CC9B
  • Earth Resources Observation and Science (EROS) Center (2013). Landsat 8-9 Operational Land Imager / Thermal Infrared Sensor Level-2, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P9OGBGM6
  • Earth Resources Observation and Science (EROS) Center (2020). Landsat 4-5 Thematic Mapper Level-2, Collection 2 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P9IAXOVV
  • European Space Agency (2022). Sentinel-2 MSI Level-2A BOA Reflectance [Data set]. https://doi.org/10.5270/S2_-znk9xsj
  • European Space Agency, & Airbus (2022). Copernicus DEM [Data set]. European Space Agency. https://doi.org/10.5270/ESA-c5d3d65
  • FAO (2025). Global Administrative Unit Layers (GAUL) [Data set]. Food and Agriculture Organization of the United Nations. Licence: CC BY 4.0. https://data.apps.fao.org
  • Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183
  • Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., & Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. https://doi.org/10.1016/j.rse.2017.03.026
  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66
  • Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
  • García, M. J. L., & Caselles, V. (1991). Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International, 6(1), 31–37. https://doi.org/10.1080/10106049109354290
  • 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. https://doi.org/10.1016/j.rse.2017.06.031
  • Hall, D. K., & Riggs, G. A. (2021). MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MYD10A1.061
  • Hall, D. K., & Riggs, G. A. (2021). MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD10A1.061
  • Hall, D. K., Riggs, G. A., & Salomonson, V. V. (1995). Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54(2), 127–140. https://doi.org/10.1016/0034-4257(95)00137-P
  • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978. https://doi.org/10.1002/joc.1276
  • Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011JC007395
  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
  • Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93. https://doi.org/10.1093/biomet/30.1-2.81
  • Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA): Sampling and analysis methods. In D. C. Lutes, R. E. Keane, J. F. Caratti, C. H. Key, N. C. Benson, S. Sutherland, & L. J. Gangi (Eds.), FIREMON: Fire effects monitoring and inventory system (Gen. Tech. Rep. RMRS-GTR-164-CD, pp. LA-1–LA-55). USDA Forest Service, Rocky Mountain Research Station. https://www.fs.usda.gov/rm/pubs_series/rmrs/gtr/rmrs_gtr164/rmrs_gtr164_13_land_assess.pdf
  • Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2), 213–238. https://doi.org/10.1016/S0034-4257(00)00205-4
  • Llodrà-Llabrés, J., Pérez-Girón, J. C., Postma, T., Pérez-Martínez, C., Alcaraz-Segura, D., & Martínez-López, J. (2026). Satellite solutions: Facing chlorophyll-a retrieval in small mountain lakes in the Sierra Nevada, Spain. Water Resources Research, 62(3), e2026WR043523. https://doi.org/10.1029/2026WR043523
  • Lyapustin, A., & Wang, Y. (2022). MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD19A2.061
  • Lyapustin, A., Wang, Y., Korkin, S., & Huang, D. (2018). MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10), 5741–5765. https://doi.org/10.5194/amt-11-5741-2018
  • Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., & Gascon, F. (2017). Sen2Cor for Sentinel-2. In L. Bruzzone, F. Bovolo, & J. A. Benediktsson (Eds.), Image and Signal Processing for Remote Sensing XXIII. SPIE. https://doi.org/10.1117/12.2278218
  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
  • McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
  • Mishra, S., & Mishra, D. R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406. https://doi.org/10.1016/j.rse.2011.10.016
  • Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019
  • Muñoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1950 to present [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb30
  • Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., & Thépaut, J.-N. (2021). ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349–4383. https://doi.org/10.5194/essd-13-4349-2021
  • Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R., & Running, S. W. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1–2), 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3
  • Myneni, R., Knyazikhin, Y., & Park, T. (2021). MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD15A3H.061
  • NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group (2017). MODIS-Aqua Level 3 Mapped Chlorophyll Data Version R2018.0 [Data set]. NASA Ocean Biology DAAC. https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018
  • NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group (2018). MODIS-Terra Level 3 Mapped Chlorophyll Data Version R2018.0 [Data set]. NASA Ocean Biology DAAC. https://doi.org/10.5067/TERRA/MODIS/L3M/CHL/2018
  • NASA JPL (2013). NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003
  • O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., & McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research: Oceans, 103(C11), 24937–24953. https://doi.org/10.1029/98JC02160
  • Parks, S. A., Dillon, G. K., & Miller, C. (2014). A new metric for quantifying burn severity: The Relativized Burn Ratio. Remote Sensing, 6(3), 1827–1844. https://doi.org/10.3390/rs6031827
  • Pasquarella, V. J., Brown, C. F., Czerwinski, W., & Rucklidge, W. J. (2023). Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 2125–2135). https://doi.org/10.1109/CVPRW59228.2023.00206
  • Riggs, G. A., Hall, D. K., & Román, M. O. (2017). Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records. Earth System Science Data, 9(2), 765–777. https://doi.org/10.5194/essd-9-765-2017
  • Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., & Moreira, A. (2017). Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 119–139. https://doi.org/10.1016/j.isprsjprs.2017.08.008
  • Running, S., Mu, Q., & Zhao, M. (2021). MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD16A2.061
  • Sayre, R., Noble, S., Hamann, S., Smith, R., Wright, D., Breyer, S., Butler, K., Van Graafeiland, K., Frye, C., Karagulle, D., Hopkins, D., Stephens, D., Kelly, K., Basher, Z., Burton, D., Cress, J., Atkins, K., Van Sistine, D. P., Friesen, B., et al. (2019). A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12(sup2), S47–S56. https://doi.org/10.1080/1755876X.2018.1529714
  • Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont, R. P., Hu, B., et al. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1–2), 135–148. https://doi.org/10.1016/S0034-4257(02)00091-3
  • Schaaf, C., & Wang, Z. (2021). MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MCD43A3.061
  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
  • Silva-Cardoza, A. I., Vega-Nieva, D. J., Briseño-Reyes, J., Briones-Herrera, C. I., López-Serrano, P. M., Corral-Rivas, J. J., Parks, S. A., & Holsinger, L. M. (2022). Evaluating a new relative phenological correction and the effect of Sentinel-based Earth Engine compositing approaches to map fire severity and burned area. Remote Sensing, 14(13), 3122. https://doi.org/10.3390/rs14133122
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
  • UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA) [On-line]. Cambridge, UK: UNEP-WCMC and IUCN. https://www.protectedplanet.net
  • Vermote, E., & Wolfe, R. (2015). MOD09GA MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V006 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD09GA.006
  • Vermote, E., & Wolfe, R. (2021). MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MYD09GA.061
  • Vermote, E., & Wolfe, R. (2021). MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD09GA.061
  • Wan, Z. (2014). New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140, 36–45. https://doi.org/10.1016/j.rse.2013.08.027
  • Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 892–905. https://doi.org/10.1109/36.508406
  • Wan, Z., Hook, S., & Hulley, G. (2021). MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MYD11A1.061
  • Wan, Z., Hook, S., & Hulley, G. (2021). MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V061 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD11A1.061
  • Wilson, A. M., & Jetz, W. (2016). Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLOS Biology, 14(3), e1002415. https://doi.org/10.1371/journal.pbio.1002415
  • Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore, B., & Ojima, D. (2004). Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91(2), 256–270. https://doi.org/10.1016/j.rse.2004.03.010
  • Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F., & Arino, O. (2022). ESA WorldCover 10 m 2021 v200 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7254221
  • Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47–56. https://doi.org/10.1002/esp.3290120107