DIAGNOSTIC ANALYSIS OF TERRESTRIAL GROSS PRIMARY PRODUCTIVITY USING REMOTE SENSING AND IN SITU OBSERVATIONS

Vegetation play a critical role in the interactions between atmosphere and biosphere. CO2 fixed by plants through photosynthesis process at ecosystem scale is termed as gross primary production (GPP). It is also the first step CO2 entering the biosphere from the atmosphere. It not only fuels the ecosystem functioning, but also drives the global carbon cycle. Accurate estimation of the ecosystem photosynthetic carbon uptake at a global scale can help us better understand the global carbon budget, and the ecosystem sensitivity to the global climate change. Satellite observations have the advantage of global coverage and high revisit cycle, hence, are ideal for global GPP estimation. The simple production efficiency model that utilize the remote sensing imagery and climate data can provide reasonably well estimates of GPP at a global scale. With the solar induced chlorophyll fluorescence (SIF) being retrieved from satellite observations, new opportunities emerge in directly estimating photosynthesis from the energy absorption and partitioning perspective. In this thesis, by combining observations from both in situ and remotely acquired, I tried to (1) investigate the GPP SIF relationship using data from observations and model simulations; (2) improve a production efficiency model (vegetation photosynthesis model, VPM) and apply it to the regional and global scale; (3) investigate the GPP and SIF sensitivity to drought at different ecosystems; (4) explore the global interannual variation of GPP and its contributing factors. Chapter 2 uses site level observations of both SIF and GPP to explore their linkage at both leaf and canopy/ecosystem scale throughout a growing season. Two drought events happened during this growing season also highlight the advantage of SIF in early drought warning and its close linkage to photosynthetic activity. Chapter 3 compares the GPP and SIF relationships using both instantaneous and daily integrated observations, the daily GPP and satellite retrieved SIF are latitudinal dependent and time-of-overpass dependent. Daily integrated SIF estimation shows better correlation with daily GPP observations. Chapter 4 compares different vegetation indices with SIF to get an empirical estimation of fraction of photosynthetically active radiation by chlorophyll (fPARchl). By comparing this fPARchl estimation with ecosystem light use efficiency retrieved from eddy covariance flux towers, the light use efficiency based on light absorption by chlorophyll shows narrower range of variation that can be used for improving production efficiency models. Chapter 5 investigates the drought impact on GPP through the change of vegetation canopy optical properties and physiological processes. Forest and non-forest ecosystems shows very different responses in terms of these two limitation and need to be treated differently in GPP modelling. Chapter 6 applies the improved VPM to North America and compared with SIF retrieval from GOME-2 instrument. The comparison shows good consistency between GPP and SIF in both spatial and seasonal variation. Chapter 7 uses an ensemble of GPP product to explore the cause of hot spots of GPP interannual variability. GPP in semiarid regions are strongly coupled with evapotranspiration and show high sensitivity to interannual variation of precipitation. The results demonstrate the importance of precipitation in regional carbon flux variability

Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations

Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions. In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate

Observed water and light limitation across global ecosystems

peer reviewedAbstract. With a changing climate, it is becoming increasingly critical to understand vegetation responses to limiting environmental factors. Here, we investigate the spatial and temporal patterns of light and water limitation on photosynthesis using an observational framework. Our study is unique in characterizing the nonlinear relationships between photosynthesis and water and light, acknowledging approximately two regime behaviours (no limitation and varying degrees of limitation). It is also unique in using an observational framework instead of using model-derived photosynthesis properties. We combine data from three different satellite sensors, i.e., sun-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), surface soil moisture from the Soil Moisture Active Passive (SMAP) microwave radiometer, and vegetation greenness from the Moderate Resolution Imaging Spectroradiometer (MODIS). We find both single-regime and two-regime models describe SIF sensitivity to soil moisture and photosynthetically active radiation (PAR) across the globe. The distribution and strength of soil moisture limitation on SIF are mapped in the water-limited environments, while the distribution and strength of PAR limitations are mapped in the energy-limited environments. A two-regime behaviour is detected in 73 % of the cases for water limitation on photosynthesis, while two-regime detection is much lower at 41 % for light limitation on photosynthesis. SIF sensitivity to PAR strongly increases along moisture gradients, reflecting mesic vegetation's adaptation to making rapid usage of incoming light availability on the weekly timescales. The transition point detected between the two regimes is connected to soil type and mean annual precipitation for the SIF–soil moisture relationship and for the SIF–PAR relationship. These thresholds therefore have an explicit relation to properties of the landscape, although they may also be related to finer details of the vegetation and soil interactions not resolved by the spatial scales here. The simple functions and thresholds are emergent behaviours capturing the interaction of many processes. The observational thresholds and strength of coupling can be used as benchmark information for Earth system models, especially those that characterize gross primary production mechanisms and vegetation dynamics

Evaluating solar-induced fluorescence across spatial and temporal scales to monitor primary productivity

Solar-induced chlorophyll fluorescence (SIF) has been widely cited in carbon cycling studies as a proxy for photosynthesis, and SIF data are commonly incorporated into terrestrial primary productivity models. Though satellite-based SIF products show close relationships with gross primary productivity (GPP), this is not universally true at intermediate scales. A meta-analysis of the tower-based and airborne SIF literature revealed that mean SIF retrievals from unstressed vegetation span three orders of magnitude. While reporting on spectrometer calibration procedures, hardware characterizations, and associated corrections is inconsistent, laboratory and field experiments show that these factors may contribute to significant uncertainty in SIF retrievals. Additionally, there remain ongoing questions regarding the interpretation of SIF data made across spatial scales and the link between satellite SIF retrievals and primary productivity on the ground. Chlorophyll fluorescence originates from dynamic energy partitioning at the leaf level and does not exhibit a uniformly linear relationship with photosynthesis at finer scales. As a standalone metric, SIF measured at the tower scale was not found to track changes in carbon assimilation following stomatal closure induced in deciduous woody tree branches. This lack of relationship may be explained by alternative energy partitioning pathways, such as thermal energy dissipation mediated by xanthophyll cycle pigments; the activity of these pigments can be tracked using the photochemical reflectance index (PRI). Gradual, phenological changes in energy partitioning are observed as changes in the slope of the SIF-PRI relationship over the course of a season. Along with high frequency effects such as wind-mediated changes in leaf orientation and reflectance, and rapid changes in sky condition due to clouds, PRI offers crucial insights needed to link SIF to leaf physiology. While SIF offers tremendous promise for improving the characterization of terrestrial carbon exchange, and a fuller understanding of the boundaries on its utility and interpretation as a biophysical phenomenon will help to create more reliable models of global productivity

Measuring and modelling fAPAR for satellite product validation

This thesis presents a comprehensive approach to satellite Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) product validation. This draws on 3D radiative transfer modelling and metrology to characterise the biases associated with a satellite fAPAR algorithm and the uncertainty associated with fAPAR estimates. This extends existing approaches which tend to assume that the in situ measurement technique produces the same fAPAR quantity as the satellite product. The validation procedure involves creating a closure experiment where every aspect of the satellite product definition and its associated assumptions can be tested from the perspective of the in situ and satellite sensors. The intrinsic differences created by the satellite product assumptions are also assessed, where a new reference is created. This is known as the “true” fAPAR since it is perfectly knowable within the context of the radiative transfer model used. Correction factors between the in situ and satellite-derived fAPAR are created to correct data collected over Wytham Woods. The results indicate that the corrections reduce differences of >10% to near zero. However, the uncertainty estimates for the satellite-derived fAPAR show that it does not meet the requirements given by Global Climate Observing System (GCOS) (≤(10% or 0.05)). The wider implications of the retrieved uncertainties are also presented showing that it is unlikely that the GCOS requirements associated with downstream applications that use satellite fAPAR can be met currently. This work represents an important step forward in the validation of satellitederived fAPAR because it is the first time that the absence of satellite and in situ data uncertainty and traceability, and satellite product definition differences have been addressed. This paves the way for the improvement of satellite fAPAR products because their uncertainties can now be quantified effectively and their validation conducted fairly, meaning there is now a benchmark to base improvements on

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( ε / S I F yield ), the one-leaf light-use efficiency (SL-LUE) model ( ε max ), and the two-leaf LUE (TL-LUE) model ( ε msu and ε msh ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the ε / S I F yield and ε max were similarly robust and considerably more stable than ε msu and ε msh for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( C V ) of ε / S I F yield was relatively small (15.0%) and was obviously lower than ε max ( C V = 27.0%), ε msu ( C V = 43.2%), and ε msh ( C V = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types