Estimating CO2 concentration during the growing season from MODIS and GOSAT in East Asia
Estimating CO2 concentration during the growing season from MODIS and GOSAT in East Asia
复制标题
利用 MODIS 和 GOSAT 估算东亚生长季节的二氧化碳浓度
DOI：
10.1080/01431161.2015.1081305
复制
发表时间：
2015-01-01
影响因子：
3.4
通讯作者：
Wu, Li
中科院分区：
文献类型：
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作者：
Guo, Meng;Xu, Jiawei;Wu, Li
由于观测站数量有限，轨道碳卫星数据的时间序列短，很难长时间监测大空间尺度的CO2浓度（XCO2）。因此，我们在准确预测XCO 2变化方面受到限制。基于使用卫星传感器获得的数据作为自变量来模拟CO2交换的方法的研究表明，对封闭林的结果是有希望的。有必要将这一方法推广到其他土地覆盖类型，以便在大的空间尺度上监测XCO2。在这项研究中，中分辨率成像光谱仪（MODIS）衍生的指数被用来模拟XCO2。选择2010年和2011年的温室气体观测卫星数据和MODIS衍生指数构建生长季节（5月至10月）的XCO2模型。我们选择了三个地面站来评估2011年至2013年每个月模拟XCO2的准确性。结果的准确性表明，三个地面站的平均偏差分别为2.25、4.53和4.43 ppm，尽管最大偏差为10.03 ppm（2013年6月在上店子站）。我们还使用2012年和2013年的GOSAT热红外和近红外碳观测传感器（TANSO）点数据作为观测数据来评估XCO2模型的准确性，除6月外，每个月都取得了略好的结果。本研究的总体结论是，在区域范围内获得XCO2的新方法需要在未来完善。
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.