Jianwu Tang

Coastal blue carbon: Concept, study method, and the application to ecological restoration

Coastal blue carbon refers to the carbon taken from atmospheric CO2; fixed by advanced plants (including salt marsh, mangrove, and seagrass), phytoplankton, macroalgae, and marine calcifiers via the interaction of plants and microbes; and stored in nearshore sediments and soils; as well as the carbon transported from the coast to the ocean and ocean floor. The carbon sequestration capacity per unit area of coastal blue carbon is far greater than that of the terrestrial carbon pool. The mechanisms and controls of the carbon sink from salt marshes, mangroves, seagrasses, the aquaculture of shellfish and macroalgae, and the microbial carbon pump need to be further studied. The methods to quantify coastal blue carbon include carbon flux measurements, carbon pool measurements, manipulative experiments, and modeling. Restoring, conserving, and enhancing blue carbon will increase carbon sinks and produce carbon credits, which could be traded on the carbon market. The need to tackle climate change and implement China’s commitment to cut carbon emissions requires us to improve studies on coastal blue carbon science and policy. The knowledge learned from coastal blue carbon improves the conservation and restoration of salt marshes, mangroves, and seagrasses; enhances the function of the microbial carbon pump; and promotes sustainable aquaculture, such as ocean ranching.

Performance of Solar-Induced Chlorophyll Fluorescence in Estimating Water-Use Efficiency in a Temperate Forest

Water-use efficiency (WUE) is a critical variable describing the interrelationship between carbon uptake and water loss in land ecosystems. Different WUE formulations (WUEs) including intrinsic water use efficiency (WUEi), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE) have been proposed. Based on continuous measurements of carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) at a temperate forest, we analyze the correlations between SIF emission and the different WUEs at the canopy level by using linear regression (LR) and Gaussian processes regression (GPR) models. Overall, we find that SIF emission has a good potential to estimate IWUE and uWUE, especially when a combination of different SIF bands and a GPR model is used. At an hourly time step, canopy-level SIF emission can explain as high as 65% and 61% of the variances in IWUE and uWUE. Specifically, we find that (1) a daily time step by averaging hourly values during daytime can enhance the SIF-IWUE correlations, (2) the SIF-IWUE correlations decrease when photosynthetically active radiation and air temperature exceed their optimal biological thresholds, (3) a low Leaf Area Index (LAI) has a negative effect on the SIF-IWUE correlations due to large evaporation fluxes, (4) a high LAI in summer also reduces the SIF-IWUE correlations most likely due to increasing scattering and (re)absorption of the SIF signal, and (5) the observation time during the day has a strong impact on the SIF-IWUE correlations and SIF measurements in the early morning have the lowest power to estimate IWUE due to the large evaporation of dew. This study provides a new way to evaluate the stomatal regulation of plant-gas exchange without complex parameterizations.

Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison

Characterized by the noticeable seasonal patterns of canopy photosynthesis, mid-to-high latitude forests are sensitive to climate change and crucial for understanding the global carbon cycle. To monitor the seasonal cycle of the canopy photosynthesis from space, several remotely sensed indexes, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) have been implemented within the past decades. Recently, satellite-derived sun-induced fluorescence (SIF) has shown great potential of providing retrievals that are more related to photosynthesis process. However, the potentials of different canopy measurements have not been thoroughly assessed in the context of recent advances of new satellites and proposals of improved indexes. At 15 forested sites, we present a cross-platform intercomparison of one emerging remote sensing based index of phenology index (PI) and two SIF datasets against the conventional indexes such as NDVI, EVI, and LAI to capture the seasonal cycles of canopy photosynthesis. NDVI, EVI, LAI, and PI were calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, while SIF were evaluated from Global Ozone Monitoring Experiment-2 (GOME-2) and Orbiting Carbon Observatory-2 (OCO-2) observations. Results indicated that GOME-2 SIF was highly correlated with gross primary production (GPP) and absorbed photosynthetically active radiation during the growing seasons. The SIF-GPP relationship can generally be considered linear at the 16-day scale. Key phenological metrics such as start of the seasons and end of the seasons captured by SIF from GOME-2 and OCO-2 matched closely with photosynthesis phenology as inferred by GPP. However, the applications of OCO-2 SIF for phenological studies may be limited only for a small range of sites (at site-level) due to a limited spatial sampling. Among the MODIS estimations, PI and NDVI provided most reliable predictions of start of growing seasons, while no indexes accurately captured the end of growing seasons.

Comparison of Phenology Estimated from Reflectance-Based Indices and Solar-Induced Chlorophyll Fluorescence (SIF) Observations in a Temperate Forest Using GPP-Based Phenology as the Standard

We assessed the performance of reflectance-based vegetation indices and solar-induced chlorophyll fluorescence (SIF) datasets with various spatial and temporal resolutions in monitoring the Gross Primary Production (GPP)-based phenology in a temperate deciduous forest. The reflectance-based indices include the green chromatic coordinate (GCC), field measured and satellite remotely sensed Normalized Difference Vegetation Index (NDVI); and the SIF datasets include ground-based measurement and satellite-based products. We found that, if negative impacts due to coarse spatial and temporal resolutions are effectively reduced, all these data can serve as good indicators of phenological metrics for spring. However, the autumn phenological metrics derived from all reflectance-based datasets are later than the those derived from ground-based GPP estimates (flux sites). This is because the reflectance-based observations estimate phenology by tracking physiological properties including leaf area index (LAI) and leaf chlorophyll content (Chl), which does not reflect instantaneous changes in phenophase transitions, and thus the estimated fall phenological events may be later than GPP-based phenology. In contrast, we found that SIF has a good potential to track seasonal transition of photosynthetic activities in both spring and fall seasons. The advantage of SIF in estimating the GPP-based phenology lies in its inherent link to photosynthesis activities such that SIF can respond quickly to all factors regulating phenological events. Despite uncertainties in phenological metrics estimated from current spaceborne SIF observations due to their coarse spatial and temporal resolutions, dates in middle spring and autumn—the two most important metrics—can still be reasonably estimated from satellite SIF. Our study reveals that SIF provides a better way to monitor GPP-based phenological metrics.

Defining System Boundaries of an Institution Nitrogen Footprint

Abstract A nitrogen (N) footprint quantifies the amount of reactive nitrogen released to the environment and can be measured at different scales. The N footprint of a university includes activities and consumption within its geographic boundaries as well as activities that support the institution. Determining system bounds of an N footprint depends on the institutions mission and provides a common baseline for comparison. A comparison of three scopes of the N footprint, which describe how emissions are directly related to an institutions activities, was conducted for seven institutions. Scopes follow the established definition for the carbon footprint. In this article, the authors propose a new system bounds definition (core campus versus adjunct). Two case studies were explored: how the N footprint of Dickinson College changed with air travel, and how the N footprint of the Marine Biological Laboratory changed with scientific research. Of the three scopes, scope 3 was consistently the largest proportion of the N footprint for all seven institutions. The core campus activities of Dickinson College made up 99 percent of its N footprint, with air travel making up the remaining 1 percent. The Marine Biological Laboratorys core campus activities made up 51 percent of its N footprint and the scientific research made up the remaining 49 percent. Institutions should define system bounds based on their mission and stay consistent with their boundaries following the baseline year. The core campus footprint could be used to compare institution footprints using consistent system bounds. How institutions define their boundaries will impact the recorded amount of nitrogen as well as how the institution will set reduction strategies.