Estimating CO₂ concentration during the growing season from MODIS and GOSAT in East Asia

Abstract

Because of the limited number of observation stations and the short time series of orbiting carbon satellite data, it is difficult to monitor CO₂ concentrations (XCO₂) at broad spatial scales for long time spans. Therefore, we are limited in accurately forecasting change in XCO₂. Studies based on the approach of using satellite sensor-derived data as independent variables to model CO₂ exchange show promising results for closed forest stands. There is a need to extend this approach to other land-cover types to monitor XCO₂ at large spatial scales. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices were used to model XCO₂. Greenhouse Gases Observing Satellite (GOSAT) data and MODIS-derived indices in 2010 and 2011 were selected to construct XCO₂ models during the growing season (May–October). We selected three ground stations to assess the accuracy of the modelled XCO₂ for each month from 2011 to 2013. The accuracy of the results indicates that the average bias was 2.25, 4.53, and 4.43 ppm at the three ground stations, respectively, although the largest bias was 10.03 ppm (at Shangdianzi Station in June 2013). We also used GOSAT Thermal and Near Infrared Sensor for Carbon Observation (TANSO) point data in 2012 and 2013 as the observed data to assess the accuracy of the XCO₂ models, and achieved a slightly favourable result for each month, except June. The overall conclusion of this study is that the proposed new approach to obtaining XCO₂ at the regional scale needs to be perfected in the future.

Acknowledgement

We thank the GOSAT Project of Japan, NASA, and WDCGG for the use of their data in this study.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [41401478], [41201159], [41171072]; the Fundamental Research Funds for the Central Universities [2412015KJ021].