Solar-induced Fluorescence Data Research Articles

Assessment of waterlogging hazard during maize growth stage in the Songliao plain based on daily scale SPEI and SMAI

Waterlogging is one of the major disasters affecting crop yield and food security. The Songliao Plain, located in the mid-latitude region and known as the "Golden Maize Belt," is severely impacted by waterlogging, which significantly affects maize yield. Therefore, it is essential to conduct a detailed assessment of the waterlogging hazard for maize in the Songliao Plain and to apply the results to agricultural meteorological disaster prevention and mitigation measures. In this study, a comprehensive waterlogging hazard assessment index was constructed by combining environmental factors conducive to disaster and disaster-causing factors. Environmental factors included terrain slope, distance from rivers, and soil clay content, while disaster-causing factors included daily SPEI and SMAI during the maize growing season in the Songliao Plain from 1982 to 2020. The results indicate that: (1) The spatial distribution of waterlogging hazard in the Songliao Plain ranges from extremely high to extremely low, showing a gradual decrease from west to east. The western and southern parts of the Songliao Plain, such as Baicheng, Songyuan, Changchun, and Fuxin, are more prone to waterlogging disasters. (2) During different maize growth stages, the spatial distribution of high and extremely high levels of waterlogging hazard exhibited significant heterogeneity. There were notable differences in the duration of waterlogging around the year 2000, with a reduction in the duration of extremely high and high levels of waterlogging after 2000. (3) A Pearson correlation analysis was conducted between the comprehensive waterlogging hazard index and SIF (Solar-Induced Fluorescence) data during different maize growth stages. The results showed a strong correlation between the comprehensive waterlogging hazard index and SIF data, with the highest correlation coefficient reaching −0.9 and a p-value less than 0.05. The comprehensive maize waterlogging hazard index can be used for precise and timely assessment of waterlogging hazard during different growth stages of maize, and it has a positive impact on improving the ability to prevent and mitigate waterlogging risks.

Novel methods facilitating the mechanistic interpretation of multiscale optical remote sensing measurements

Plant physiology concentrates on the study of plant internal processes, such as growth, nutrient uptake and photosynthesis. The quantification of photosynthesis regulation is significant in understanding how plants react to the changing climate. Spectral remote sensing methods, using both reflected light in the visible and near infrared wavelengths, as well as chlorophyll fluorescence, are used to gather information about plant physiological variables. These methods have developed rapidly, prompted by the advances in remote sensing platforms and sensors. However, interpretation of remote sensing signals can be challenging. Due to canopy heterogeneity, the signal is affected by various elements, such as scattering, soil background and canopy structural effects. Additionally, open questions remain linked to the underlying mechanistic processes in the leaf modulating the optical signal, such as nutrient contents and leaf photochemistry, and how these processes and the optical signals diverge in response to temporal variation. Through multi-scale measurements, this thesis aims to advance the interpretation of optical remote sensing signals as they are affected by spatial and temporal variation, while promoting the use of novel methods and devices. Results indicate that diurnal and long-term variation of solar induced fluorescence (SIF) is driven by photosynthetic and structural factors, causing possible misinterpretations in SIF data. Additionally, depending on the scale of observation, results show that the capacity of remote sensing to detect changes in foliar nutrients depends on the covariation of nutrients, pigments and canopy structure, underlining the need for both leaf and canopy level measurements. Finally, we advocate for the implementation of a novel miniaturized fluorometer, demonstrating the ability to track the seasonal regulation of photosynthesis using integrated measurements of chlorophyll fluorescence and gas exchange. The results from this thesis underline the need for simultaneous multi-scale measurements of leaf and canopy physiological factors to further our understanding of photosynthesis regulation.

Variability of Remotely Sensed Solar-Induced Chlorophyll Fluorescence in Relation to Climate Indices

Global remote sensing of solar-induced fluorescence (SIF), a proxy for plant photosynthetic activity, represents a breakthrough in the systematic observation of global-scale gross primary production and other ecosystem functions. Here, we hypothesize that all earth ecosystem variabilities, including SIF, are affected by climate variations. The main contribution of this study is to apply a global empirical orthogonal function (EOF) analysis of SIF to quantify the relations between the large-scale GPP variability and climate variations. We used 2007–2019 SIF data derived from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor observations and a rotated empirical orthogonal function (EOF) analysis to explore global SIF variability over years and decades. The first leading EOF mode captures the well-known ENSO pattern, with most of the variance over continents in the tropical Pacific and Indian Oceans. The second and third leading EOF modes in SIF variability are significantly related to the NAO and PDO climate indices, respectively. Our analysis also shows that the 2011 La Niña (2015 El Niño) elevated (decreased) global SIF.

Anthropogenic CO2 emissions assessment of Nile Delta using XCO2 and SIF data from OCO-2 satellite

We estimate CO2 emissions from the Nile Delta region of Egypt, using over five years of column-averaged CO2 dry air mole fraction (XCO2) data from the NASA’s OCO-2 satellite. The Nile Delta has significant anthropogenic emissions of CO2 from urban areas and irrigated farming. It is surrounded by the Sahara desert and the Mediterranean Sea, minimizing the confounding influence of CO2 sources in surrounding areas. We compiled the observed spatial and temporal variations of XCO2 in the Nile Delta region (XCO2,del), and found that values for XCO2,del were on average 1.1 ppm higher than XCO2,des (mean XCO2 in desert area). We modelled the expected enhancements of XCO2 over the Nile Delta based on two global CO2 emission inventories, EDGAR and ODIAC. Modelled XCO2 enhancements were much lower, indicating underestimation of CO2 emissions in the Nile Delta region by mean factors of 4.5 and 3.4 for EDGAR and ODIAC, respectively. Furthermore, we captured a seasonal pattern of XCO2 enhancement (ΔXCO2), with significantly lower ΔXCO2 during the summer agriculture season in comparison to other seasons. Additionally, we used solar-induced fluorescence (SIF) measurement from OCO-2 to understand how the CO2 emissions are related to agricultural activities. Finally, we estimated an average emission of CO2 from the Nile Delta from 2014–2019 of 470 Mt CO2/year, about 1% of global anthropogenic emissions, which is significantly more than estimated hitherto.

Scientific Communities Striving for a Common Cause: Innovations in Carbon Cycle Science

AbstractWhere does the carbon released by burning fossil fuels go? Currently, ocean and land systems remove about half of the CO2 emitted by human activities; the remainder stays in the atmosphere. These removal processes are sensitive to feedbacks in the energy, carbon, and water cycles that will change in the future. Observing how much carbon is taken up on land through photosynthesis is complicated because carbon is simultaneously respired by plants, animals, and microbes. Global observations from satellites and air samples suggest that natural ecosystems take up about as much CO2 as they emit. To match the data, our land models generate imaginary Earths where carbon uptake and respiration are roughly balanced, but the absolute quantities of carbon being exchanged vary widely. Getting the magnitude of the flux is essential to make sure our models are capturing the right pattern for the right reasons. Combining two cutting-edge tools, carbonyl sulfide (OCS) and solar-induced fluorescence (SIF), will help develop an independent answer of how much carbon is being taken up by global ecosystems. Photosynthesis requires CO2, light, and water. OCS provides a spatially and temporally integrated picture of the “front door” of photosynthesis, proportional to CO2 uptake and water loss through plant stomata. SIF provides a high-resolution snapshot of the “side door,” scaling with the light captured by leaves. These two independent pieces of information help us understand plant water and carbon exchange. A coordinated effort to generate SIF and OCS data through satellite, airborne, and ground observations will improve our process-based models to predict how these cycles will change in the future.

Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures

Recent satellite retrieval of solar-induced fluorescence (SIF) has provided promising information for constraining carbon flux simulation with terrestrial biosphere models (TBMs). For this purpose, it is necessary to implement a SIF model in TBMs. SIF emerging from leaves generally undergoes multiple scattering processes within a canopy. However, it remains unclear how canopy scattering impacts observed SIF, which is used to constrain regional and global GPP modeling. Here, we developed an efficient scheme to account for the canopy scattering in a SIF model. Then we coupled this mechanistic representation of SIF to Boreal Ecosystem Productivity Simulator (BEPS) and evaluated the new model (BEPS-SIF) simulations with ground and satellite-based SIF measurements. The results showed that BEPS-SIF reproduced global patterns of SIF observed by a satellite sensor and captured the seasonality of SIF reasonably well over different regions. The canopy scattering effects on the relationship between SIF and gross primary productivity (GPP) were also examined at the global scale using the BEPS-SIF model. We confirmed that SIF emitted from leaves was more directly linked to GPP globally than the observed canopy-leaving SIF, with higher improvements of the coefficient of determination (R2) in dense vegetated regions and lower improvements of R2 in sparse vegetated regions. This study highlights the importance of implementing the canopy scattering effect in a fluorescence model of TBMs and the need to account for this effect in the use of satellite SIF data for constraining global GPP modeling.

Spatiotemporal Patterns and Phenology of Tropical Vegetation Solar-Induced Chlorophyll Fluorescence across Brazilian Biomes Using Satellite Observations

Solar-induced fluorescence (SIF) has been empirically linked to gross primary productivity (GPP) in multiple ecosystems and is thus a promising tool to address the current uncertainties in carbon fluxes at ecosystem to continental scales. However, studies utilizing satellite-measured SIF in South America have concentrated on the Amazonian tropical forest, while SIF in other regions and vegetation classes remain uninvestigated. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data for vegetation classes within and across the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does satellite-measured SIF differ? (2) What is the relationship (strength and direction) of satellite-measured SIF with canopy temperature (Tcan), air temperature (Tair), and vapor pressure deficit (VPD)? (3) How does the phenology of satellite-measured SIF (duration and amplitude of seasonal integrated SIF) compare? Our analysis shows that OCO-2 captures a significantly higher mean SIF with lower variability in the Amazon and lower mean SIF with higher variability in the Caatinga compared to other biomes. OCO-2 also distinguishes the mean SIF of vegetation types within biomes, showing that evergreen broadleaf (EBF) mean SIF is significantly higher than other vegetation classes (deciduous broadleaf (DBF), grassland (GRA), savannas (SAV), and woody savannas (WSAV)) in all biomes. We show that the strengths and directions of correlations of OCO-2 mean SIF to Tcan, Tair, and VPD largely cluster by biome: negative in the Caatinga and Cerrado, positive in the Pampa, and no correlations were found in the Pantanal, while results were mixed for the Amazon and Atlantic Forest. We found mean SIF most strongly correlated with VPD in most vegetation classes in most biomes, followed by Tcan. Seasonality from time series analysis reveals that OCO-2 SIF measurements capture important differences in the seasonal timing of SIF for different classes, details masked when only examining mean SIF differences. We found that OCO-2 captured the highest base integrated SIF and lowest seasonal pulse integrated SIF in the Amazon for all vegetation classes, indicating continuous photosynthetic activity in the Amazon exceeds other biomes, but with small seasonal increases. Surprisingly, Pantanal EBF SIF had the highest total integrated SIF of all classes in all biomes due to a large seasonal pulse. Additionally, the length of seasons only accounts for about 30% of variability in total integrated SIF; thus, integrated SIF is likely captures differences in photosynthetic activity separate from structural differences. Our results show that satellite measurements of SIF can distinguish important functioning and phenological differences in vegetation classes and thus has the potential to improve our understanding of productivity and seasonality in the tropics.

Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence

A general greening trend in the Arctic tundra biome has been indicated by satellite remote sensing data over recent decades. However, since 2011, there have been signs of browning trends in many parts of the region. Previous research on tundra greenness across the Arctic region has relied on the satellite-derived normalized difference vegetation index (NDVI). In this research, we initially used spatially downscaled solar-induced fluorescence (SIF) data to analyze the spatiotemporal variation of Arctic tundra greenness (2007–2013). The results derived from the SIF data were also compared with those from two NDVIs (the Global Inventory Modeling and Mapping Studies NDVI3g and MOD13Q1 NDVI), and the eddy-covariance (EC) observed gross primary production (GPP). It was found that most parts of the Arctic tundra below 75° N were browning (–0.0098 mW/m2/sr/nm/year, where sr is steradian and nm is nanometer) using SIF, whereas spatially and temporally heterogeneous trends (greening or browning) were obtained based on the two NDVI products. This research has further demonstrated that SIF data can provide an alternative direct proxy for Arctic tundra greenness.

Passive remote sensing of solar-induced fluorescence spectra of crude oil

The solar-induced fluorescence (SIF) of a simulated oil slick is remotely retrieved from the infilling of the solar Fraunhofer lines of its radiance spectrum acquired by means of a sub-nanometric spectral resolution fluorescence lidar operated in passive mode (i.e. as a spectroradiometer). SIF data, retrieved from the infilling of several Fraunhofer lines selected in the 390–660 nm range, were exploited to reconstruct the oil fluorescence spectrum. This spectrum was finally compared with laser-induced fluorescence spectra acquired on the same oil slick by using the fluorescence lidar. The results open new prospects for the remote sensing of oils and their classification.