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A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO_2 Using GOSAT and MODIS Observations

He, Zhonghua and Zeng, Zhao-Cheng and Lei, Liping and Bie, Nian and Yang, Shaoyuan (2017) A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO_2 Using GOSAT and MODIS Observations. Remote Sensing, 9 (3). Art. No. 251. ISSN 2072-4292. https://resolver.caltech.edu/CaltechAUTHORS:20170428-082449561

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Abstract

Using measurements of the column-averaged CO_2 dry air mole fraction (XCO_2) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO_2. A unique global land mapping dataset of XCO_2 with a resolution of 1° by 1° in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO_2 retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO_2 and NDVI, and implement correlation analyses of seasonal variation between XCO_2 and the vegetation parameters. As a result, the spatial distribution of XCO_2 SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO_2 from CarbonTracker. The spatial pattern of XCO_2 SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere–atmosphere interactions at local scales. Results from correlation analyses between XCO_2 and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO_2 and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO_2 compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere–atmosphere interactions on the seasonal cycle pattern of atmospheric CO_2 columns.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.3390/rs9030251DOIArticle
http://www.mdpi.com/2072-4292/9/3/251PublisherArticle
http://www.mdpi.com/2072-4292/9/3/251/s1PublisherSupplementary Material
Additional Information:© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Received: 20 October 2016; Accepted: 6 March 2017; Published: 8 March 2017. This research was supported by the National Research Program on Global Changes and Adaptation: “Big data on global changes: data sharing platform and recognition” (Grant No. 2016YFA0600303, Grant No. 2016YFA0600304). We acknowledge The ACOS-GOSAT v3.5 data were produced by the ACOS/OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the ACOS/OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. We also acknowledge the GOSAT Project for acquiring the spectra. CarbonTracker CT2015 results are provided by NOAA ESRL, Boulder, Colorado, USA from the website at http://carbontracker.noaa.gov. Terra MODIS GPP/NPP Product MOD17A2 was downloaded from the University of Montana and Aqua or Terra MODIS NDVI/EVI, LAI products (MYD13C2, MOD11C3) were download from USGS websites (http://e4ftl01.cr.usgs.gov). Land cover products were downloaded from ESA (http://www.esa-ghg-cci.org/). And TCCON data were obtained from the TCCON Data Archive website at http://tccon.ornl.gov/. Bottom-up estimation of fossil fuel emissions (FFCO2) is obtained from ODIAC (Open-source Data Inventory for Anthropogenic CO2: http://www.odiac.org/index.html) and CDIAC (Carbon Dioxide information Analysis Center: http://cdiac.ornl.gov/). Author Contributions: L.L., Z.H., and Z.-C.Z. conceived and designed the experiments; Z.H. performed the experiments; Z.-C.Z. analyzed the data; N.B., S.Y., and Z.-C.Z. contributed analysis tools; Z.H., L.L., and Z.-C.Z. wrote the paper. The authors declare no conflict of interest.
Funders:
Funding AgencyGrant Number
National Research Program on Global Changes and Adaptation2016YFA0600303
National Research Program on Global Changes and Adaptation2016YFA0600304
Subject Keywords:GOSAT; MODIS; XCO2; seasonal cycle pattern; biosphere absorption
Issue or Number:3
Record Number:CaltechAUTHORS:20170428-082449561
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170428-082449561
Official Citation:He, Z.; Zeng, Z.-C.; Lei, L.; Bie, N.; Yang, S. A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations. Remote Sens. 2017, 9, 251
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:77043
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Apr 2017 16:10
Last Modified:03 Oct 2019 17:52

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