Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects
Our understanding of the terrestrial carbon cycle has been greatly enhanced since satellite observations of the land surface started. The advantage of remote sensing is that it provides wall-to-wall observations including in regions where in situ monitoring is challenging. This paper reviews how satellite observations of the biosphere have helped improve our understanding of the terrestrial carbon cycle. First, it details how remotely sensed information of the land surface has provided new means to monitor vegetation dynamics and estimate carbon fluxes and stocks. Second, we present examples of studies which have used satellite products to evaluate and improve simulations from global vegetation models. Third, we focus on model data integration approaches ranging from bottom-up extrapolation of single variables to carbon cycle data assimilation system able to ingest multiple types of observations. Finally, we present an overview of upcoming satellite missions which are likely to further improve our understanding of the terrestrial carbon cycle and its response to climate change and extremes.
KeywordsTerrestrial carbon cycle Earth observation Satellite Ecosystem modelling Model data integration
This review stemmed from the workshop “Space-based Measurement of Forest Properties for Carbon Cycle Research” at the International Space Science Institute in Bern during November 2017. Contribution from AAB was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The first and last authors were supported by the Natural Environment Research Council through the National Centre for Earth Observation, contract number PR140015, and the Newton Fund, through CSSP Brazil.
- Baldocchi D, Falge E, Gu LH et al (2001) 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. https://doi.org/10.1175/1520-0477(2001)082%3c2415:FANTTS%3e2.3.CO;2 CrossRefGoogle Scholar
- Baret F, Weiss M, Lacaze R et al (2013) GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: principles of development and production. Remote Sens Environ 137:299–309. https://doi.org/10.1016/J.RSE.2012.12.027 CrossRefGoogle Scholar
- Buchwitz M, Burrows JP (2004) Retrieval of CH4, CO, and CO2 total column amounts from SCIAMACHY near-infrared nadir spectra: retrieval algorithm and first results. In: Schaefer K, Comeron A, Carleer MR, Picard RH (eds) Proceedings of SPIE, p 375Google Scholar
- Freeborn PH, Wooster MJ, Roy DP, Cochrane MA (2014) Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation. Geophys Res Lett 41:1988–1994. https://doi.org/10.1002/2013GL059086 CrossRefGoogle Scholar
- Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. J Clim 15:1141–1158. https://doi.org/10.1175/1520-0442(2002)015%3c1141:COAURA%3e2.0.CO;2 CrossRefGoogle Scholar
- Joiner J, Guanter L, Lindstrot R et al (2013) Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos Meas Tech 6:2803–2823. https://doi.org/10.5194/amt-6-2803-2013 CrossRefGoogle Scholar
- Joiner J, Yoshida Y, Vasilkov AP et al (2014) The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens Environ 152:375–391. https://doi.org/10.1016/j.rse.2014.06.022 CrossRefGoogle Scholar
- Joiner J, Yoshida Y, Guanter L, Middleton EM (2016) New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: simulations and application to GOME-2 and SCIAMACHY. Atmos Meas Tech 9:3939–3967. https://doi.org/10.5194/amt-9-3939-2016 CrossRefGoogle Scholar
- Knyazikhin Y, Martonchik JV, Myneni RB et al (1998) Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res Atmos 103:32257–32275. https://doi.org/10.1029/98JD02462 CrossRefGoogle Scholar
- Running SW, Nemani RR, Heinsch FA et al (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54:547. https://doi.org/10.1641/0006-3568(2004)054%5b0547:ACSMOG%5d2.0.CO;2 CrossRefGoogle Scholar
- Sun Y, Frankenberg C, Jung M et al (2018) Overview of solar-induced chlorophyll fluorescence (SIF) from the orbiting carbon observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens Environ 209:808–823. https://doi.org/10.1016/j.rse.2018.02.016 CrossRefGoogle Scholar
- Zhu Z, Bi J, Pan Y et al (2013) Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3G) for the period 1981 to 2011. Remote Sens 5:927–948. https://doi.org/10.3390/rs5020927 CrossRefGoogle Scholar