scholarly journals How does leaf functional diversity affect the light environment in forest canopies? An in-silico biodiversity experiment

2021 ◽  
Author(s):  
Elena Plekhanova ◽  
Pascal A. Niklaus ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
Gabriela Schaepman-Strub
Keyword(s):  
Functional Diversity ◽  
In Silico ◽  
Species Interactions ◽  
Shortwave Radiation ◽  
Ecosystem Functions ◽  
Radiative Transfer Model ◽  
Leaf Angle ◽  
Transfer Model ◽  
Angle Distribution ◽  
Area Index

<p><span>Global change is affecting biodiversity, with consequences on carbon, water, and energy fluxes between the atmosphere, vegetation, and soil. The interaction of shortwave radiation with vegetation drives basic processes of the biosphere, such as primary productivity or species interactions through light competition. </span></p><p><span>In our study, we aim to understand the effects of biodiversity on canopy light absorption. We focus on the diversity of three key functional traits that influence the light-canopy interaction: leaf area index, leaf angle distribution and leaf optical properties. Our study is based on the in-silico combination of process-based modelling of radiation (3D radiative transfer model DART) with the well-established design of biodiversity experiments. We used this novel method to study the effects of leaf functional diversity on a light proxy for productivity (the fraction of absorbed photosynthetically active radiation (FAPAR)) and net radiation (shortwave albedo). We found that diverse canopies had lower albedo and higher FAPAR than the average of the corresponding monoculture values. In mixtures, FAPAR was unequally re-distributed between trees with distinct leaf traits and the net biodiversity effect on absorptance was greater when combining plant functional types with more distinct traits. Our results support the mechanistic understanding of overyielding effects in functionally diverse canopies and may partially explain some of the growth-promoting mechanisms in biodiversity-ecosystem functioning experiments. They can further help to account for biodiversity effects in dynamic vegetation and climate models.</span></p><p><span>We would like to present this study in the BG3.22 session, because it 1) contributes to the understanding of fundamental ecosystem functions related to light interaction 2) describes a novel in-silico combination of process-based modelling of radiation with the well-established design of biodiversity experiments.</span></p>

scholarly journals Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study

2020 ◽  
Vol 88 (5) ◽  
pp. 379-390
Author(s):  
M. Schwieder ◽  
M. Buddeberg ◽  
K. Kowalski ◽  
K. Pfoch ◽  
J. Bartsch ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Empirical Model ◽  
Aboveground Biomass ◽  
Ecosystem Functions ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Validation Data ◽  
Area Index ◽  
Dry Biomass ◽  
Sentinel 2

Abstract Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.

scholarly journals Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

2018 ◽  
Vol 10 (10) ◽  
pp. 1508 ◽  
Author(s):  
Yelu Zeng ◽  
Baodong Xu ◽  
Gaofei Yin ◽  
Shengbiao Wu ◽  
Guoqing Hu ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Hot Spot ◽  
Canopy Structure ◽  
Radiative Transfer Model ◽  
Leaf Angle ◽  
Transfer Model ◽  
Angle Distribution ◽  
Bidirectional Reflectance Factor ◽  
Spectral Invariant

This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.

scholarly journals Simulation of Thermodynamic Features of a Thunderstorm Event over Dhaka using WRF-ARW Model

2018 ◽  
Vol 37 ◽  
pp. 131-145
Author(s):  
Md Mijanur Rahman ◽  
Md Abdus Samad ◽  
SM Quamrul Hassan
Keyword(s):  
Convective Available Potential Energy ◽  
Model Performance ◽  
Vertical Wind Shear ◽  
Longwave Radiation ◽  
Final Analysis ◽  
Horizontal Resolution ◽  
Cumulus Parameterization ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model

An attempt has been made to simulate the thermodynamic features of the thunderstorm (TS) event over Dhaka (23.81°N, 90.41°E) occurred from 1300 UTC to 1320 UTC of 4 April 2015 using Advanced Research dynamics solver of Weather Research and Forecasting model (WRF-ARW). The model was run to conduct a simulation for 48 hours on a single domain of 5 km horizontal resolution utilizing six hourly Global Final Analysis (FNL) datasets from 0600 UTC of 3 April 2015 to 0600 UTC of 5 April 2015 as initial and lateral boundary conditions. Kessler schemes for microphysics, Yonsei University (YSU) scheme for planetary boundary layer (PBL) parametrization, Revised MM5 scheme for surface layer physics, Rapid Radiative Transfer Model (RRTM) for longwave radiation, Dudhia scheme for shortwave radiation and Kain–Fritsch (KF) scheme for cumulus parameterization were used. Hourly outputs produced by the model have been analyzed numerically and graphically using Grid Analysis and Display System (GrADS). Deep analyses were carried out by examining several thermodynamic parameters such as mean sea level pressure (MSLP), wind pattern, vertical wind shear, vorticity, temperature, convective available potential energy (CAPE), relative humidity (RH) and rainfall. To validate the model performance, simulated values of MSLP, maximum and minimum temperature and RH were compared with observational data obtained from Bangladesh Meteorological Department (BMD). Rainfall values were compared with that of BMD and Tropical Rainfall Measuring Mission (TRMM) of National Aeronautics and Space Administration (NASA). Based on the comparisons and validations, the present study advocates that the model captured the TS event reasonably well.GANIT J. Bangladesh Math. Soc.Vol. 37 (2017) 131-145

Evaluation of shortwave radiation fluxes in the multi-layer SPARTACUS-Urban scheme using DART

2021 ◽  
Author(s):  
Megan Stretton ◽  
William Morrison ◽  
Robin Hogan ◽  
Sue Grimmond
Keyword(s):  
Radiative Transfer ◽  
Urban Form ◽  
Climate Models ◽  
Weather Prediction ◽  
Computational Cost ◽  
Three Dimensional ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Urban Structure

<p>The heterogenous structure of cities impacts radiative exchanges (e.g. albedo and heat storage). Numerical weather prediction (NWP) models often characterise the urban structure with an infinite street canyon – but this does not capture the three-dimensional urban form. SPARTACUS-Urban (SU) - a fast, multi-layer radiative transfer model designed for NWP - is evaluated using the explicit Discrete Anisotropic Radiative Transfer (DART) model for shortwave fluxes across several model domains – from a regular array of cubes to real cities .</p><p>SU agrees with DART (errors < 5.5% for all variables) when the SU assumptions of building distribution are fulfilled (e.g. randomly distribution). For real-world areas with pitched roofs, SU underestimates the albedo (< 10%) and shortwave transmission to the surface (< 15%), and overestimates wall-plus-roof absorption (9-27%), with errors increasing with solar zenith angle. SU should be beneficial to weather and climate models, as it allows more realistic urban form (cf. most schemes) without large increases in computational cost.</p>

Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data

2021 ◽  
Author(s):  
Richard Fernandes ◽  
Fred Baret ◽  
Luke Brown ◽  
Francis Canisius ◽  
Jadu Dash ◽  
...  
Keyword(s):  
North America ◽  
Google Earth ◽  
Model Complexity ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Wide Field ◽  
Area Index ◽  
Product Generation ◽  
Level 2 ◽  
Sentinel 2

<p>The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.  The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.  Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.  Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?  </p><p><br><br></p><p>To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).  This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.  SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.  Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.  Results for other sites will also be presented.  To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.  The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.</p>

scholarly journals Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2460 ◽  
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Xiuguo Liu ◽  
Lin Du ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Gaussian Process Regression ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Look Up Table ◽  
Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

scholarly journals VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

2020 ◽  
Vol 12 (14) ◽  
pp. 2254 ◽  
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
M. Pilar Martín ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sharp Change ◽  
Full Potential ◽  
Area Index ◽  
Red Edge ◽  
Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

scholarly journals Analysis of the decrease in the tropical mean outgoing shortwave radiation at the top of atmosphere for the period 1984-2000

2005 ◽  
Vol 5 (6) ◽  
pp. 1721-1730 ◽  
Author(s):  
A. Fotiadi ◽  
N. Hatzianastassiou ◽  
C. Matsoukas ◽  
K. G. Pavlakis ◽  
E. Drakakis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Shortwave Radiation ◽  
Radiative Energy ◽  
Radiative Transfer Model ◽  
Physical Parameters ◽  
Transfer Model ◽  
Monthly Basis ◽  
Decadal Scale

Abstract. A decadal-scale trend in the tropical radiative energy budget has been observed recently by satellites, which however is not reproduced by climate models. In the present study, we have computed the outgoing shortwave radiation (OSR) at the top of atmosphere (TOA) at 2.5° longitude-latitude resolution and on a mean monthly basis for the 17-year period 1984-2000, by using a deterministic solar radiative transfer model and cloud climatological data from the International Satellite Cloud Climatology Project (ISCCP) D2 database. Anomaly time series for the mean monthly pixel-level OSR fluxes, as well as for the key physical parameters, were constructed. A significant decreasing trend in OSR anomalies, starting mainly from the late 1980s, was found in tropical and subtropical regions (30° S-30° N), indicating a decadal increase in solar planetary heating equal to 1.9±0.3Wm-2/decade, reproducing well the features recorded by satellite observations, in contrast to climate model results. This increase in solar planetary heating, however, is accompanied by a similar increase in planetary cooling, due to increased outgoing longwave radiation, so that there is no change in net radiation. The model computed OSR trend is in good agreement with the corresponding linear decadal decrease of 2.5±0.4Wm-2/decade in tropical mean OSR anomalies derived from ERBE S-10N non-scanner data (edition 2). An attempt was made to identify the physical processes responsible for the decreasing trend in tropical mean OSR. A detailed correlation analysis using pixel-level anomalies of model computed OSR flux and ISCCP cloud cover over the entire tropical and subtropical region (30° S-30° N), gave a correlation coefficient of 0.79, indicating that decreasing cloud cover is the main reason for the tropical OSR trend. According to the ISCCP-D2 data derived from the combined visible/infrared (VIS/IR) analysis, the tropical cloud cover has decreased by 6.6±0.2% per decade, in relative terms. A detailed analysis of the inter-annual and long-term variability of the various parameters determining the OSR at TOA, has shown that the most important contribution to the observed OSR trend comes from a decrease in low-level cloud cover over the period 1984-2000, followed by decreases in middle and high-level cloud cover. Note, however, that there still remain some uncertainties associated with the existence and magnitude of trends in ISCCP-D2 cloud amounts. Opposite but small trends are introduced by increases in cloud scattering optical depth of low and middle clouds.

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