Changes in the Shadow: The Shifting Role of Shaded Leaves in Global Carbon and Water Cycles Under Climate Change

He, Li; Chen, Jing M.; Gonsamo, Alemu; Luo, Xiangzhong; Wang, Rong; Yang, Liu; Liu, Ronggao

doi:10.1029/2018gl077560

Cited by 60 publications

(44 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Boreal Ecosystems Productivity Simulator (BEPS), hourly version, is a process-based ecosystem model to simulate water, energy, and carbon budgets [47][48][49][50][51][52]; it was used in this study to simulate cotton GPP. BEPS is in development for the last years [53].…”

Section: Ecosystem Model For Gpp Simulationmentioning

confidence: 99%

“…BEPS is in development for the last years [53]. Although BEPS was initially developed for boreal ecosystems, it was expanded and used for temperate and tropical ecosystems in regional and global scales [48,51,52,[54][55][56][57][58][59] and for the estimation of winter wheat yield in China [18], as C3 plants share the same photosynthesis theory at the leaf level, i.e., Farquhar's leaf-level biochemical model [60], and BEPS has a "two-leaf" approach to upscale the ecosystem GPP to canopy level for various ecosystems including crops [49,61].…”

Section: Ecosystem Model For Gpp Simulationmentioning

confidence: 99%

“…The V cmax at 25 • C varies with leaf nitrogen and phosphorus [65,66]. Since such nutrition information is not reliably available from Sentinel-2, the cotton V cmax at 25 • C was set to 120 µmol•m −2 •s −1 to be consistent with our previous modeling for crops [52]. For BEPS simulation, the CO 2 concentration data were obtained from https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html.…”

Section: Cotton-specific Photosynthetic Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

Mostovoy

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton lint yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.

show abstract

Section: Ecosystem Model For Gpp Simulationmentioning

confidence: 99%

Section: Ecosystem Model For Gpp Simulationmentioning

confidence: 99%

Section: Cotton-specific Photosynthetic Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

Mostovoy

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The four component proportions directly determine the forest canopy spectral characteristics [9] and further affect the retrieval accuracy of forest leaf area index (LAI) [10,11] and chlorophyll content [12]. Then characterizing the sunlit and shaded components in a forest stand will be beneficial to the calculation of C assimilation and transpiration using advanced ecosystem models [13].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar

et al. 2020

View full text Add to dashboard Cite

Forest three-dimensional (3-D) structure, in the vertical dimension, consists of at least two components, including overstory and a forest background matrix (i.e., shrubs, grass, and bare earth). Quantitatively characterizing the proportions of forest sunlit (i.e., sunlit overstory and forest background) and shaded (i.e., shaded overstory and forest background) components is a crucial step in simulating the spatial variations of bidirectional reflectance distribution function (BRDF) of a forest canopy. By developing a Voxel-based sorest sunlit and shaded (VFSS) approach driven by aerial laser scanning data (ALS), we investigated the spatial variations of the forest sunlit and shaded components in a heterogeneous urban forest park (Washington Park Arboretum) with abundant tree species and a homogeneous natural forest area (Panther Creek). Meanwhile, we validated the forest canopy directional reflectance at both solar principal and perpendicular planes at the plot level. Moreover, we explored the effects of ALS data characteristics and forest stand conditions on the estimation accuracy of forest sunlit and shaded components. Our results show that (1) ALS data effectively stratify overstory and forest background with the accuracy decreasing from 87% to 65% as forest densities increase; (2) the root mean square errors (RMSEs) between the modeled- and ALS-based proportions of forest sunlit and shaded components range from 5.8% to 11.1% affected by forest densities; and (3) the scan angles and flight directions have apparent effects on the estimation accuracy of forest sunlit and shaded components. This work provides a solid foundation to investigate the spatial variations of directional forest canopy reflectance with a high spatial resolution of 1 m.

show abstract

“…Some studies have shown that understory vegetation contributes 32% of the total GPP in tropical forests [22,23], but this contribution may be 25% in temperate forests [24]. A recent study showed that the shaded GPP has increased by 1.1% over the past three decades [25]. This variability is due to variations in the light, temperature, and humidity conditions in the understory and overstory.…”

Section: Introductionmentioning

confidence: 99%

Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Lin

Liu

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

show abstract

Changes in the Shadow: The Shifting Role of Shaded Leaves in Global Carbon and Water Cycles Under Climate Change

Cited by 60 publications

References 105 publications

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US

Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar

Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Contact Info

Product

Resources

About