Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Emerging satellite observations for diurnal cycling of ecosystem processes



Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Conceptual diagram of plant photosynthesis and transpiration over the course of a day.
Fig. 2: ECOSTRESS images from the Nile Delta within the same day.
Fig. 3: SIF at different times of the day as measured by the OCO-3 in SAM mode.
Fig. 4: Diurnal variations in plant photosynthesis derived from geostationary satellite data and a light-use efficiency model.
Fig. 5: The synergy between ECOSTRESS and OCO-3 data enables diurnal monitoring of WUE of terrestrial ecosystems.
Fig. 6: Synergistic use of observations from a geostationary satellite and two ISS instruments for studying diurnal cycling of ecosystem processes.

Data availability

The data that support the findings of this study are available from (ECOSTRESS), (ECOSTRESS), (OCO-3), (flux tower data), (Himawari-8), (Himawari-8) and (global geostationary satellite LST data).


  1. 1.

    Hennessey, T. L., Freeden, A. L. & Field, C. B. Environmental effects on circadian rhythms in photosynthesis and stomatal opening. Planta 189, 369–376 (1993).

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Steed, G., Ramirez, D. C., Hannah, M. A. & Webb, A. A. R. Chronoculture, harnessing the circadian clock to improve crop yield and sustainability. Science 372, eabc9141 (2021).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Zhao, T. B. & Dai, A. G. The magnitude and causes of global drought changes in the twenty-first century under a low–moderate emissions scenario. J. Clim. 28, 4490–4512 (2015).

    Article  Google Scholar 

  4. 4.

    Perkins-Kirkpatrick, S. E. & Gibson, P. B. Changes in regional heatwave characteristics as a function of increasing global temperature. Sci. Rep. 7, 12256 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Bates, L. M. & Hall, A. E. Stomatal closure with soil-water depletion not associated with changes in bulk leaf water status. Oecologia 50, 62–65 (1981).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Roessler, P. G. & Monson, R. K. Midday depression in net photosynthesis and stomatal conductance in Yucca-Glauca—relative contributions of leaf temperature and leaf-to-air water-vapor concentration difference. Oecologia 67, 380–387 (1985).

    PubMed  Article  Google Scholar 

  7. 7.

    Tenhunen J. D., Pearcy R. W. & Lange O. L. in Stomatal Function (eds Zeiger, E. et al.) Ch. 15 (Stanford Univ. Press, 1987).

  8. 8.

    Tucci, M. L. S., Erismann, N. M., Machado, E. C. & Ribeiro, R. V. Diurnal and seasonal variation in photosynthesis of peach palms grown under subtropical conditions. Photosynthetica 48, 421–429 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    Kosugi, Y. & Matsuo, N. Seasonal fluctuations and temperature dependence of leaf gas exchange parameters of co-occurring evergreen and deciduous trees in a temperate broad-leaved forest. Tree Physiol. 26, 1173–1184 (2006).

    PubMed  Article  Google Scholar 

  10. 10.

    Koch, G. W., Amthor, J. S. & Goulden, M. L. Diurnal patterns of leaf photosynthesis, conductance and water potential at the top of a lowland rain-forest canopy in Cameroon—measurements from the Radeau-Des-Cimes. Tree Physiol. 14, 347–360 (1994).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Olioso, A., Carlson, T. N. & Brisson, N. Simulation of diurnal transpiration and photosynthesis of a water stressed soybean crop. Agric. For. Meteorol. 81, 41–59 (1996).

    Article  Google Scholar 

  12. 12.

    Cowan, I. R. & Farquhar, G. D. Stomatal function in relation to leaf metabolism and environment: stomatal function in the regulation of gas exchange. Symposia Soc. Exp. Biol. 31, 471–505 (1977).

    CAS  Google Scholar 

  13. 13.

    Bollig, C. & Feller, U. Impacts of drought stress on water relations and carbon assimilation in grassland species at different altitudes. Agric. Ecosyst. Environ. 188, 212–220 (2014).

    Article  Google Scholar 

  14. 14.

    Koyama, K. & Takemoto, S. Morning reduction of photosynthetic capacity before midday depression. Sci. Rep. 4, 4389 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Nelson, J. A., Carvalhais, N., Migliavacca, M., Reichstein, M. & Jung, M. Water-stress-induced breakdown of carbon–water relations: indicators from diurnal FLUXNET patterns. Biogeosciences 15, 2433–2447 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    Xu, H., Xiao, J. F. & Zhang, Z. Q. Heatwave effects on gross primary production of northern mid-latitude ecosystems. Environ. Res. Lett. 15, 074027 (2020).

    Article  Google Scholar 

  17. 17.

    Baldocchi, D. et al. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434 (2001).

    Article  Google Scholar 

  18. 18.

    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).

    Article  Google Scholar 

  19. 19.

    Xiao, J. F. et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sens. Environ. 114, 576–591 (2010).

    Article  Google Scholar 

  20. 20.

    Anderson, M. C., Allen, R. G., Morse, A. & Kustas, W. P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 122, 50–65 (2012).

    Article  Google Scholar 

  21. 21.

    Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536 (2007).

    Article  Google Scholar 

  23. 23.

    Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour. Res. 56, 1–20 (2020).

    Article  Google Scholar 

  24. 24.

    Hook, S. J. et al. In-flight validation of ECOSTRESS, Landsat 7 and 8 thermal infrared spectral channels using the Lake Tahoe CA/NV and Salton Sea CA automated validation sites. IEEE Trans. Geosci. Remote Sens. 58, 1294–1302 (2019).

    Article  Google Scholar 

  25. 25.

    Hulley, G., Shivers, S., Wetherley, E. & Cudd, R. New ECOSTRESS and MODIS land surface temperature data reveal fine-scale heat vulnerability in cities: a case study for Los Angeles County, California. Remote Sens. 11, 2136 (2019).

    Article  Google Scholar 

  26. 26.

    Anderson, M. C. et al. Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sens. Environ. 252, 112189 (2021).

    Article  Google Scholar 

  27. 27.

    Anderson, M. C. et al. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US drought monitor classifications. J. Hydrometeorol. 14, 1035–1056 (2013).

    Article  Google Scholar 

  28. 28.

    Li, X., Xiao, J., Fisher, J. B. & Baldocchi, D. D. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station. Remote Sens. Environ. 258, 112360 (2021).

    Article  Google Scholar 

  29. 29.

    Hulley, G. C. et al. Validation and quality assessment of the ECOSTRESS level-2 land surface temperature and emissivity product. IEEE Trans. Geosci. Remote Sens. (2021).

  30. 30.

    Aragon, B., Houborg, R., Tu, K., Fisher, J. B. & McCabe, M. CubeSats enable high spatiotemporal retrievals of crop-water use for precision agriculture. Remote Sens. 10, 1867 (2018).

    Article  Google Scholar 

  31. 31.

    Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).

    Article  Google Scholar 

  32. 32.

    Turner, N. C., Schulze, E.-D. & Gollan, T. The responses of stomata and leaf gas exchange to vapour pressure deficits and soil water content. Oecologia 65, 348–355 (1985).

    PubMed  Article  Google Scholar 

  33. 33.

    Moore, G. W. & Heilman, J. L. Proposed principles governing how vegetation changes affect transpiration. Ecohydrology 4, 351–358 (2011).

    Article  Google Scholar 

  34. 34.

    Hulley, G. C. & Hook, S. J. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research. IEEE Trans. Geosci. Remote Sens. 49, 1304–1315 (2011).

    Article  Google Scholar 

  35. 35.

    Fisher, J. B., Whittaker, R. H. & Malhi, Y. ET Come Home: a critical evaluation of the use of evapotranspiration in geographical ecology. Glob. Ecol. Biogeogr. 20, 1–18 (2011).

    Article  Google Scholar 

  36. 36.

    Talsma, C. J. et al. Partitioning of evapotranspiration in remote sensing-based models. Agric. For. Meteorol. 260, 131–143 (2018).

    Article  Google Scholar 

  37. 37.

    Otkin, J. A. et al. Examining rapid onset drought development using the thermal infrared-based evaporative stress index. J. Hydrometeorol. 14, 1057–1074 (2013).

    Article  Google Scholar 

  38. 38.

    Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evolution 1, 0194 (2017).

    Article  Google Scholar 

  39. 39.

    Taylor, T. E. et al. OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals. Remote Sens. Environ. 251, 112032 (2020).

    Article  Google Scholar 

  40. 40.

    Frankenberg, C. et al. The Orbiting Carbon Observatory (OCO-2): spectrometer performance evaluation using pre-launch direct sun measurements. Atmos. Meas. Tech. 8, 301–313 (2015).

    CAS  Article  Google Scholar 

  41. 41.

    Bilger, W., Schreiber, U. & Bock, M. Determination of the quantum efficiency of photosystem-II and of nonphotochemical quenching of chlorophyll fluorescence in the field. Oecologia 102, 425–432 (1995).

    PubMed  Article  Google Scholar 

  42. 42.

    Maguire, A. J. et al. On the functional relationship between fluorescence and photochemical yields in complex evergreen needleleaf canopies. Geophys. Res. Lett. 47, e2020GL087858 (2020).

    Article  Google Scholar 

  43. 43.

    Marrs, J. K. et al. Solar-induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophys. Res. Lett. 47, e2020GL087956 (2020).

    CAS  Article  Google Scholar 

  44. 44.

    Li, X. et al. Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: first global analysis based on OCO-2 and flux tower observations. Glob. Change Biol. 24, 3990–4008 (2018).

    Article  Google Scholar 

  45. 45.

    Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).

    Article  CAS  Google Scholar 

  46. 46.

    Li, X. & Xiao, J. F. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).

    Article  Google Scholar 

  47. 47.

    Liu, J. J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino. Science 358, eaam5690 (2017).

    PubMed  Article  CAS  Google Scholar 

  48. 48.

    Parazoo, N. C. et al. Towards a harmonized long-term spaceborne record of far-red solar-induced fluorescence. J. Geophys. Res. 124, 2518–2539 (2019).

    Article  Google Scholar 

  49. 49.

    He, L. Y. et al. Tracking seasonal and interannual variability in photosynthetic downregulation in response to water stress at a temperate deciduous forest. J. Geophys. Res. 125, e2018JG005002 (2020).

    Google Scholar 

  50. 50.

    Lin, C. J. et al. Evaluation and mechanism exploration of the diurnal hysteresis of ecosystem fluxes. Agric. For. Meteorol. 278, 107642 (2019).

    Article  Google Scholar 

  51. 51.

    Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Yang, X. et al. FluoSpec 2—an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors (Basel) 18, 2063 (2018).

    Article  CAS  Google Scholar 

  53. 53.

    Miura, T., Nagai, S., Takeuchi, M., Ichii, K. & Yoshioka, H. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Sci. Rep. 9, 15692 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Bessho, K. et al. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteorological Soc. Jpn 94, 151–183 (2016).

    Article  Google Scholar 

  55. 55.

    Schmit, T. J. et al. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 98, 681–698 (2017).

    Article  Google Scholar 

  56. 56.

    Oh, S. M., Borde, R., Carranza, M. & Shin, I. C. Development and intercomparison study of an atmospheric motion vector retrieval algorithm for GEO-KOMPSAT-2A. Remote Sens. 11, 2054 (2019).

    Article  Google Scholar 

  57. 57.

    Yang, J., Zhang, Z. Q., Wei, C. Y., Lu, F. & Guo, Q. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Am. Meteorol. Soc. 98, 1637–1658 (2017).

    Article  Google Scholar 

  58. 58.

    Ouaknine, J. et al. The FCI on Board MTG: optical design and performances. In International Conference on Space Optics—ICSO 2014 (eds Sodnik, Z. et al.) 1056323 (SPIE, 2014).

  59. 59.

    Wang, W. et al. An introduction to the Geostationary-NASA Earth Exchange (GeoNEX) products: 1. Top-of-atmosphere reflectance and brightness temperature. Remote Sens. 12, 1267 (2020).

    Article  Google Scholar 

  60. 60.

    Yamamoto, Y., Ishikawa, H., Oku, Y. & Hu, Z. Y. An algorithm for land surface temperature retrieval using three thermal infrared bands of Himawari-8. J. Meteorological Soc. Jpn. 96B, 59–76 (2018).

    Article  Google Scholar 

  61. 61.

    Yu, Y. & Yu, P. in The GOES-R Series. A New Generation of Geostationary Environmental Satellites (eds Goodman, S. J. et al.) Ch. 12 (2020).

  62. 62.

    Takenaka, H. et al. Estimation of solar radiation using a neural network based on radiative transfer. J. Geophys. Res. 116, D08215 (2011).

    Google Scholar 

  63. 63.

    Hashimoto, H. et al. Hourly GPP estimation in Australia using Himawari-8 AHI products. In IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium 4513–4515 (IEEE, 2020).

  64. 64.

    Yan, K. et al. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: consistency and improvements. Remote Sens. 8, 359 (2016).

    CAS  Article  Google Scholar 

  65. 65.

    Moore, B. et al. The potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Front. Environ. Sci. (2018).

  66. 66.

    Zoogman, P. et al. Tropospheric emissions: monitoring of pollution (TEMPO). J. Quant. Spectrosc. Radiat. Transf. 186, 17–39 (2017).

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Courrèges-Lacoste, G. B. et al. Knowing what we Breathe: Sentinel 4: a Geostationary Imaging UVN Spectrometer for Air Quality Monitoring. In International Conference on Space Optics—ICSO 2016 (eds Karafolas, N. et al.) 105621J (SPIE, 2017).

  68. 68.

    Wekerle, T., Pessoa, J. B., da Costa, L. & Trabasso, L. G. Status and trends of smallsats and their launch vehiclesan up-to-date review. J. Aerosp. Technol. Manag. 9, 269–286 (2017).

    Article  Google Scholar 

  69. 69.

    Ryswyk, M. V. Planet announces 50 cm SkySat imagery, tasking dashboard and up to 12× revisit. Planet (9 June 2020);

  70. 70.

    Blackwell, W. J. et al. An overview of the TROPICS NASA Earth Venture mission. Q. J. R. Meteorol. Soc. 144, 16–26 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218 (2006).

    Article  Google Scholar 

  72. 72.

    Franco, A. C. & Luttge, U. Midday depression in savanna trees: coordinated adjustments in photochemical efficiency, photorespiration, CO2 assimilation and water use efficiency. Oecologia 131, 356–365 (2002).

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Keller, M., Schimel, D. S., Hargrove, W. W. & Hoffman, F. M. A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ. 6, 282–284 (2008).

    Article  Google Scholar 

Download references


This study was supported by the NASA’s ECOSTRESS Science and Applications Team (80NSSC20K0167) (J.X.), NASA’s Climate Indicators and Data Products for Future National Climate Assessments (NNX16AG61G) (J.X.), the National Science Foundation (Macrosystem Biology & NEON-Enabled Science program: DEB-2017870, EF-1638688) (J.X.), NASA’s ECOSTRESS (J.B.F.), NASA Earth Exchange (NEX) from NASA’s Earth Science Division (H.H.), the Virtual Laboratory (VL) project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (K.I.), a research grant of the Japan Society for the Promotion of Science (JSPS), KAKENHI (20K20487) (K.I.), and the Earth Science Division OCOST program (N.C.P.). J.B.F. and N.C.P. carried out their research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. California Institute of Technology. Government sponsorship acknowledged. A portion of these data were produced by the OCO-3 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the OCO-3 data archive at the NASA Goddard Earth Science Data and Information Services Center. G. Halverson provided ECOSTRESS visualization. Himawari-8 AHI-based LST data used in this study were provided by Y. Yamamoto, Chiba University, Japan.

Author information




J.X. conceived the idea. J.X., J.B.F., H.H., K.I. and N.C.P. designed the research, conducted the analyses, interpreted the data and wrote the paper.

Corresponding author

Correspondence to Jingfeng Xiao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Gerbrand Koren, Steve Running and Changliang Shao for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiao, J., Fisher, J.B., Hashimoto, H. et al. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing