The CO2 record at the Amazon Tall Tower Observatory : A new opportunity to study processes on seasonal and inter-annual scales

High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014-2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1 degrees S, 58.9 degrees W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (Delta CO2obs) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between Delta CO2obs and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of Delta CO2obs. In addition, we present how the 2015-2016 El Nino-induced drought was captured by our atmospheric record as a positive 2 sigma anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of Delta CO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.Peer reviewe

Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management

Integrating in-situ measurements, land surface models and satellite remote sensing to understand impacts of environmental changes on terrestrial ecosystem processes at multiple scales

How terrestrial ecosystems respond to environmental changes affects the well-being of human society. Thus, extreme climate events, increasing the atmospheric concentration of CO₂, and drastic changes in temperature are sources of major concern. However, our current capacity to understand and predict these responses is still limited because a myriad of physical, chemical, and biological processes are involved. While many mechanistic-based land surface models have been developed, their performances remain relatively poor and require continuous improvement. While ground-based and space-based observational datasets of the surface of the Earth have been available for a long time, their linkages to the functional aspects of the processes in terrestrial ecosystems often are weak. In this study, I used the approach of integrating in-situ measurements, land surface models, and remote sensing by satellites. I hypothesized that, through such integration, the impacts of environmental changes on terrestrial processes at multiple scales could be better understood and even predicted, especially the impacts related to the functions of important ecosystems. I tested this hypothesis at the local, regional, and global scales. At the local scale, i.e., at a Midwest forest site known as the isoprene volcano of the world, I examined the effects of droughts on the emissions of isoprene, which is the most abundant, non-methane, biogenic volatile organic compound. I compared flux tower observations with simulations performed by a state-of-the-art land model (CLM) coupled with the model of emissions of gases and aerosols from Nature version 2.1 (MEGAN2.1), and I used these observations to develop an understanding of how the amount of moisture in the soil and the ambient temperature affect the prediction of isoprene emissions during droughts. I found that temperature had a delaying effect on isoprene emissions, which are sensitive to variations in the moisture content of the soil. Thus, during drought conditions, both the delaying effect and the sensitivity to moisture are overlooked by the model. A better model that does not have these two shortcomings is required for realistic predictions of isoprene emissions. At the regional scale, I investigated the potential of using sun-induced chlorophyll fluorescence (SIF) retrieved from a satellite to monitor vegetation activities in an arid region and a semi-arid region in Australia. I chose these two types of regions for this investigation because the ecosystems in such regions have important effects on the global carbon cycle, while their contributions are poorly constrained in global carbon budgets. I found that SIF was synchronized better with the activity of vegetation than other indices that are commonly used for this purpose. I quantified the relationships between the various activities of plants and the amount and frequency of precipitation, and I was able to demonstrate that, over the region being studied, SIF represented the activity of vegetation in response to the availability of water better than other, remotely-sensed variables. At the global scale, I used multiple model ensembles to determine the climatic and anthropogenic controls on the terrestrial evapotranspiration trends from 1982 to 2010. After climatic influences, increases in CO₂ were found to be the second-most dominant factor that affected the trend of ET. CO₂ causes a decreasing trend in the canopy’s transpiration and ET, and this is especially of concern for tropical forests and high-latitude shrub lands. The increased deposition of nitrogen amplifies the global ET slightly due to enhanced growth of plants. On a global scale, land-use-induced ET responses are minor, but they can be significant locally, particularly over regions with intensive changes in the land-cover. The results of my studies demonstrated that integrating in-situ measurements, models of the surface on the land, and remote sensing using satellites can provide insights regarding the impacts of environmental changes on terrestrial processes at multiple scales. This approach is particularly important when models are imperfect and observations are lacking. My findings indicated ways that future models can be improved and identified key observational needs for the functions of terrestrial ecosystems.Geological Science

A Multi-Scale Assessment of Solar-Induced Chlorophyll Fluorescence and Its Relation to Northern Hemisphere Forest Productivity

Photosynthesis, or gross primary productivity (GPP), plays a critical role in the global carbon cycle, since it is the sole pathway for carbon fixation by the biosphere. Quantifying GPP across multiple spatial scales is needed to improve our understanding of current and future behavior of biosphere-atmosphere carbon exchange and subsequent feedbacks on the climate system. Remote sensing represents one method to observe vegetation properties and processes, and solar-induced chlorophyll fluorescence (SIF), a light signal originating from leaves, has been shown to be proportional to GPP on diurnal and seasonal timescales. Recently, new techniques to retrieve SIF from satellite observations have provided an unprecedented opportunity to study GPP on a global scale. The relationship between SIF and GPP, however, is subject to significant uncertainty as it is influenced by a number of ecosystem traits (e.g. plant species, canopy structure, leaf age). In this dissertation, I evaluate SIF signals and their relation to GPP over Northern Hemisphere forest ecosystems. First, I compare climate-driven variations in satellite-based SIF to both longstanding satellite vegetation indices derived from reflected sunlight and tower-based estimates of GPP. Even when aggregated regionally, interannual variability (IAV) of SIF is found to be subject to low signal-to-noise performance, particularly during summer. However, through a statistical analysis, I show that increases in springtime temperature driven by warmer temperatures are offset by drier, less productive conditions later in the growing season. Summer productivity, however, is more strongly correlated with moisture than with temperature, suggesting that moisture exerts a greater influence on growing season-integrated signals. While these results demonstrate that satellite observations can be used to reveal meaningful carbon-climate interactions, they also show that currently available satellite observations of SIF do not allow for robust studies of IAV at scales comparable to surface-based observations. To investigate how SIF signals are related to ecosystem function at a local scale, I built and deployed a PhotoSpec spectrometer system to the AmeriFlux tower at the University of Michigan Biological Station (US-UMB) above a temperate deciduous forest. These observations show a strong correlation between SIF and GPP at diurnal and seasonal timescales, but SIF is more closely tied to solar radiation and exhibits a delayed response to water stress-induced losses in summer GPP. This decoupling during drought highlights the challenges in using SIF to detect changes in summertime productivity. However, an increased ratio between red and far-red SIF during drought indicates that the combination of SIF at multiple wavelengths may improve the detection of water stress. Lastly, I explore diurnal and directional aspects of the SIF signal. Observations of SIF are sensitive to sun-sensor geometry, with smaller incident angles (between solar and viewing angles) leading to stronger signals. However, afternoon SIF is typically lower than morning values at equivalent light levels due to ecosystem downregulation, which obfuscates angular dependencies in the afternoon. While satellite observations typically rely on a clear-sky sunlight proxy to scale instantaneous observations of SIF to daily values, these results demonstrate the need to account for sounding geometry and diurnal hysteresis in SIF signals in order to advance the interpretation of satellite observations. Overall, my results provide a multiscale assessment of SIF over Northern Hemisphere forests and emphasize that careful attention must be given to the spatial and temporal scales at which SIF can be used to make inferences about GPP.PHDAtmospheric, Oceanic & Space ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168011/1/zbutterf_1.pd

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales

Abstract High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014?2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal () that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of . In addition, we present how the 2015?2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales

A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)

Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., NDVI, NIRv, and kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution while many processes influencing GPP occur at fine spatial scales. Here, we develop a Convolutional Neural Network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m&minus;2 sr&minus;1 nm&minus;1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (OCO-2/3). SIFnet performs systematically better than the downscaling approach (r = 0.78 for SIFnet, r = 0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions these parameters matter. Namely, the CNN finds low resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high spatial resolution SIF data.</p

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio