Xiangzhong Luo

Leaf chlorophyll content as a proxy for leaf photosynthetic capacity

Abstract Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO2 levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013–2015), approximately every 10 days for leaf chlorophyll content (ChlLeaf) and canopy structure. Leaf nitrogen (NArea) was also measured during 2014. Leaf photosynthesis was measured during 2014–2015 using a Li‐6400 gas‐exchange system, with A‐Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and NArea (R2 = 0.62, P < 0.001), whereas ChlLeaf demonstrated a much stronger correlation with Vcmax25 (R2 = 0.78, P < 0.001). The relationship between ChlLeaf and NArea was also weak (R2 = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive ChlLeaf. TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple NArea–Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that ChlLeaf provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China

Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding.

Assessment of SMAP soil moisture for global simulation of gross primary production

In this study, high quality soil moisture data derived from the Soil Moisture Active Passive (SMAP) satellite measurements are evaluated from a perspective of improving the estimation of the global Gross Primary Production (GPP) using a process-based ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS). The SMAP soil moisture data are assimilated into BEPS using an Ensemble Kalman Filter. The correlation coefficient (r) between simulated GPP from the sunlit leaves and Sun-Induced chlorophyll Fluorescence (SIF) measured by Global Ozone Monitoring Experiment–2 (GOME-2) is used as an indicator to evaluate the performance of the GPP simulation. Areas with SMAP data in low quality (i. e. forests), or with SIF in low magnitude (e. g. deserts) or both are excluded from the analysis. With the assimilated SMAP data, the r value is enhanced for Africa, Asia, and North America by 0.016, 0.013, and 0.013, respectively (p<0.05). Significant improvement in r appears in single-cropping agricultural land where the irrigation is not considered in the model but well captured by SMAP (e. g. 0.09 in North America, p<0.05). With the assimilation of SMAP, areas with weak model performances are identified in double or triple cropping cropland (e. g. part of North China Plain) and/or mountainous area (e. g. Spain and Turkey). The correlation coefficient is enhanced by 0.01 in global average for shrub, grass, and cropland. This enhancement is small and insignificant because non-water-stressed areas are included.

Comparison of spatial patterns of satellite-derived and ground-based phenology for the deciduous broadleaf forest of China

Using spatial phenology model-based Ulmus pumila leaf unfolding and leaf fall data at 8 km × 8 km grids, we analysed spatial relationships between ground-based and satellite-derived growing season beginning and end dates during the period 2001–2005 and examined climatic controls on spatial correlations between ground-based and satellite-derived growing seasons. The results show that the regional mean satellite-derived growing season clearly started earlier and terminated slightly later than the regional mean ground-based growing season. Meanwhile, spatial patterns of satellite-derived growing season beginning and end dates correlate positively with spatial patterns of ground-based growing season beginning and end dates in each year (p < 0.001). Interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season beginning date and February–April temperature controls interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season beginning date. In contrast, interannual variation of the spatial correlation coefficient between ground-based and satellite-derived growing season end date is not associated with interannual variation in the difference of the slope of the spatial regression between ground-based/satellite-derived growing season end date and September–November temperature.

Comparison of Big‐Leaf, Two‐Big‐Leaf, and Two‐Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon‐Water Modeling

Author(s): Luo, X; Chen, JM; Liu, J; Black, TA; Croft, H; Staebler, R; He, L; Arain, MA; Chen, B; Mo, G; Gonsamo, A; McCaughey, H | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles

Author(s): He, L; Chen, JM; Croft, H; Gonsamo, A; Luo, X; Liu, J; Zheng, T; Liu, R; Liu, Y | Abstract: ©2017. American Geophysical Union. All Rights Reserved. The magnitude and variability of the terrestrial CO2 sink remain uncertain, partly due to limited global information on ecosystem nitrogen (N) and its cycle. Without N constraint in ecosystem models, the simulated benefits from CO2 fertilization and CO2-induced increases in water use efficiency (WUE) may be overestimated. In this study, satellite observations of a relative measure of chlorophyll content are used as a proxy for leaf photosynthetic N content globally for 2003–2011. Global gross primary productivity (GPP) and evapotranspiration are estimated under elevated CO2 and N-constrained model scenarios. Results suggest that the rate of global GPP increase is overestimated by 85% during 2000–2015 without N limitation. This limitation is found to occur in many tropical and boreal forests, where a negative leaf N trend indicates a reduction in photosynthetic capacity, thereby suppressing the positive vegetation response to enhanced CO2 fertilization. Based on our carbon-water coupled simulations, enhanced CO2 concentration decreased stomatal conductance and hence increased WUE by 10% globally over the 1982 to 2015 time frame. Due to increased anthropogenic N application, GPP in croplands continues to grow and offset the weak negative trend in forests due to N limitation. Our results also show that the improved WUE is unlikely to ease regional droughts in croplands because of increases in evapotranspiration, which are associated with the enhanced GPP. Although the N limitation on GPP increase is large, its associated confidence interval is still wide, suggesting an urgent need for better understanding and quantification of N limitation from satellite observations.

Leaf chlorophyll content estimation from sentinel-2 MSI data

The Sentinel-2A (S2A) Multi-Spectral Imager (MSI) is a new remote sensor launched on 23 June 2015 that provides unprecedented Earth observation with high spatial, spectral and temporal resolutions. It has high potential for chlorophyll content estimation. Chlorophyll content plays a crucial role in plant photosynthesis affecting the terrestrial carbon cycle. In this research, a physical retrieval algorithm is proposed for leaf chlorophyll content from the S2A MSI data based on 4-Scale and PROSPECT models. Satellite and ground data were collected and processed in a mixed temperate forest near Borden, Ontario, Canada from May to October 2016. Preliminary validation shows an agreement between the inverted and ground measured leaf chlorophyll contents, with r = 0.77 and RMSE = 8.82 μg/cm2, which is an improvement over those generated by the Sentinel Application Platform (SNAP). Further research is ongoing, and the algorithm will be improved in the future.