The Comparison Between GOME-2 SIF & TROPOMI SIF Free Essay Example

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Abstract

Understanding the global carbon cycle and the effect of photosynthesis on gross primary production (GPP) is important for understanding the health status of vegetation. Photosynthetically active vegetation re-emits light in the form of an electromagnetic signal called solar induced chlorophyll fluorescence (SIF). Previous satellite studies measured SIF from space and found a correlation between SIF and GPP. However, these satellites (GOME-2, SCIAMACHY, GOSAT and OCO-2) have low temporal and spatial resolution. In this study, the first SIF data is presented from the recently launched satellite TROPOMI and this data is compared with GOME-2 SIF.

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First, it is demonstrated that TROPOMI shows a substantial improvement in monitoring SIF compared to GOME-2. Next, TROPOMI is able to distinguish different vegetation types when comparing SIF data with land-cover maps. These results enable a better understanding of the SIF signal.

Introduction

Life on Earth depends on photosynthesis, the fundamental mechanism underlying plant growth and productivity. During this process, chlorophyll-a molecules in vegetation absorb a fraction of sunlight within 400-700 nm wavelengths called photosynthetically active radiation (PAR) and the leaf pores absorb atmospheric CO2.

Plants use PAR to convert CO2 into complex energy-rich molecules (Frankenberg et al., 2014). The amount of atmospheric CO2 absorbed by plants is called gross primary production (GPP), the largest component in the global carbon cycle (Wu et al., 2018).

Knowing how, when and where CO2 is absorbed and released provides insight into the effect of climate change on ecosystems. Donohue et al. (2013) showed that enhanced levels of CO2 led to an increase in vegetated cover.

Also, increased photosynthetic levels in already densely vegetated areas were found when levels of CO2 increased (Norby et al., 2005). However, the impact of a changing climate and human intervention on photosynthesis and the global carbon cycle is not fully understood. Currently, there are different approaches to measure GPP: laboratory, ground-based and satellite remote-sensing systems (Frankenberg et al., 2011).

The disadvantage of laboratory and ground-based techniques is that they cannot be used for large areas, owing to the limited data sources available and places on Earth that are hard to reach (Thum et al., 2017; Wu et al., 2018). Also, these techniques measure ‘potential photosynthesis’ by detecting chlorophyll levels and spectral reflectance. These chlorophyll levels are linked to indices such as the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the normalized difference indices (NDVI) and the enhanced vegetation indices (EVI) (Zhao et al., 2005). These indices can be misleading, for example, green vegetated areas that are not photosynthetically active but have high chlorophyll levels (Erickson, 2014). To measure actual photosynthetic performance and better understand the global carbon cycle, sun-induced chlorophyll fluorescence (SIF) can be measured. SIF is the electromagnetic signal re-emitted by photosynthetically active vegetation at longer wavelengths (660-800 nm) (Köhler et al., 2015).

Satellites, designed for atmospheric research, can measure SIF levels from space. They include GOSAT, GOME-2, SCIAMACHY (Frankenberg et al., 2011; Joiner et al., 2012; Guanter et al., 2012) and OCO-2 (Frankenberg et al., 2014). These satellites can detect narrow absorption features in the solar spectrum, called Fraunhofer lines (660-800 nm). By measuring the fractional depth of these lines, fluorescence can be estimated (Frankenberg et al., 2012). Investigating SIF levels is important because the amount of SIF emitted from vegetation into the atmosphere is directly linked to photosynthetic efficiency and is regulated by short-term variations in weather. SIF levels respond to variation of light, water and temperature and have a strong linear correlation with GPP (Zuromski et al., 2018). Therefore, SIF levels can be used as a direct reporter for the health status of vegetation and for analysing the sensitivity of different ecosystems to short-term variations in weather (Gitelson et al., 2015; Frankenberg et al., 2011).

Previous research showed that SIF datasets retrieved from satellites had low spatial and temporal resolution, which can be a consequence of a large footprint size (GOME-2 and SCIAMACHY) or discontinuous global coverage and sparse measurements (GOSAT and OCO-2) (Frankenberg et al., 2011; Joiner et al., 2012; Guanter et al., 2012; Frankenberg et al., 2014). Most ecosystems are heterogeneous and large footprint sizes and high spatial resolution are needed for providing information for small agricultural and forestry land units (Guanter et al., 2015). It is expected that the new spaceborne mission launched on 13 October 2017: the NASA TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor, will improve the precision of spatiotemporal aggregates to SIF retrieval (Veefkind et al., 2012). Although there are several papers addressing the improvements of TROPOMI compared to its predecessors, not much research has been done on mapping global TROPOMI SIF observations.

One of the complicated factors in the TROPOMI VIS-NIR channel is stray light, unwanted light that reaches the detector. It is not fully understood how this stray light affects calibration and SIF retrieval (Kleipool et al., 2014). Therefore, this study investigates the sensitivity of TROPOMI for measuring SIF retrieval and presents the first TROPOMI SIF results. Next, it is investigated if TROPOMI enables a substantial spatial improvement in SIF retrieval with respect to GOME-2. GOME-2 is used as a reference to evaluate TROPOMI’s performance given that it provides the most similar spectral and spatial sampling (Guanter et al., 2015). Last, the potential of TROPOMI to make a distinction between different types of vegetation is analysed.

It is expected that TROPOMI will improve the precision of spatiotemporal aggregates to SIF retrieval with respect to GOME-2, as a result of the higher sensitivity to SIF, higher spatial resolution and greater number of clear-sky observations. It is further expected that TROPOMI has the potential to make a distinction between different types of vegetation as a result of the wide swath width of approximately 2600 km and the spatial resolution of 7 × 7 km2 (Guanter et al., 2015). The paper is structured as follows: section 2 describes the instruments GOME-2 and TROPOMI. Section 3 describes the SIF retrieval approach. Section 4 presents the global SIF results of TROPOMI, the comparison between GOME-2 SIF and TROPOMI SIF and discusses the comparison of TROPOMI with SPOT-4 VEGETATION land-cover classes.