A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data
A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data
复制标题
DOI：
10.5194/amt-8-2589-2015
复制
发表时间：
2015-01-01
影响因子：
3.8
通讯作者：
Joiner, J.
中科院分区：
文献类型：
--
作者：
Koehler, P.;Guanter, L.;Joiner, J.
近红外太阳诱导叶绿素荧光（SIF）的全球检索已在过去几年中实现了一些空间大气光谱仪的手段。在这里，我们提出了一种新的反演方法，中等光谱分辨率的仪器，如全球臭氧监测实验-2（GOME-2）和扫描成像吸收光谱仪大气CHartographY（SCIAMACHY）。在Guanter et al.（2013）和Joiner et al.（2013）之前工作的基础上，我们的方法提供了一种选择自由参数数量的解决方案。特别地，应用向后消除算法来优化要拟合的系数的数量，这也减少了检索噪声并自动选择状态向量元素的数量。模拟光谱的灵敏度分析已被用来评估我们的检索方法的性能。该方法也已被应用于估计在740 nm处的SIF从真实的光谱从GOME-2和第一次，从SCIAMACHY。我们发现一个很好的对应关系的绝对应力强度因子值和空间模式从两个传感器，这表明所提出的检索方法的鲁棒性。此外，我们将我们的结果与现有的SIF数据集进行比较，检查不确定性，并使用我们的GOME-2检索经验显示相对较低的敏感性的SIF检索云污染。
Global retrievals of near-infrared sun-induced chlorophyll fluorescence (SIF) have been achieved in the last few years by means of a number of space-borne atmospheric spectrometers. Here, we present a new retrieval method for medium spectral resolution instruments such as the Global Ozone Monitoring Experiment-2 (GOME-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY). Building upon the previous work by Guanter et al. (2013) and Joiner et al. (2013), our approach provides a solution for the selection of the number of free parameters. In particular, a backward elimination algorithm is applied to optimize the number of coefficients to fit, which reduces also the retrieval noise and selects the number of state vector elements automatically. A sensitivity analysis with simulated spectra has been utilized to evaluate the performance of our retrieval approach. The method has also been applied to estimate SIF at 740 nm from real spectra from GOME-2 and for the first time, from SCIAMACHY. We find a good correspondence of the absolute SIF values and the spatial patterns from the two sensors, which suggests the robustness of the proposed retrieval method. In addition, we compare our results to existing SIF data sets, examine uncertainties and use our GOME-2 retrievals to show empirically the relatively low sensitivity of the SIF retrieval to cloud contamination.