scholarly journals SCOPE 2.0: A model to simulate vegetated land surface fluxes and satellite signals

2020 ◽  
Author(s):  
Peiqi Yang ◽  
Egor Prikaziuk ◽  
Wout Verhoef ◽  
Christiaan van der Tol
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Heat Dissipation ◽  
Chlorophyll Concentration ◽  
Vertical Gradient ◽  
Surface Fluxes ◽  
Canopy Reflectance ◽  
Physically Based ◽  
Vegetation Canopies ◽  
Definition Of

Abstract. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared and thermal domains with land surface processes in a physically based manner, and quantifying the micro-climate in the canopy. It simulates radiative transfer in the soil, leaves and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 11 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP) and directional thermal signals. Here we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) It enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understory or with a vertical gradient in leaf chlorophyll concentration; (2) It enables the simulation of soil reflectance; (3) It includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) The computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in the computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.

scholarly journals SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals

2021 ◽  
Vol 14 (7) ◽  
pp. 4697-4712
Author(s):  
Peiqi Yang ◽  
Egor Prikaziuk ◽  
Wout Verhoef ◽  
Christiaan van der Tol
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Heat Dissipation ◽  
Gross Primary Production ◽  
Chlorophyll Concentration ◽  
Vertical Gradient ◽  
Surface Fluxes ◽  
Canopy Reflectance ◽  
Physically Based ◽  
Vegetation Canopies

Abstract. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared, and thermal domains with land surface processes in a physically based manner, and quantifying the microclimate in vegetation canopies. It simulates radiative transfer in the soil, leaves, and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 12 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary production (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) it enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understorey or with a vertical gradient in leaf chlorophyll concentration; (2) it enables the simulation of soil reflectance; (3) it includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) the computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.

scholarly journals A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation

2005 ◽  
Vol 6 (6) ◽  
pp. 1063-1072 ◽  
Author(s):  
Steven A. Margulis ◽  
Jongyoun Kim ◽  
Terri Hogue
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface ◽  
Measurement Errors ◽  
Structural Model ◽  
Turbulent Fluxes ◽  
Surface Fluxes ◽  
Variational Data Assimilation ◽  
Model Errors

Abstract Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature observations. Results from a set of synthetic experiments and an application using real data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site indicate that the assimilation approach performs slightly better than the triangle method because of the robustness of the estimation to measurement errors and parsimony of the system model, which leads to fewer sources of structural model errors. Future comparison work using retrieval and data assimilation algorithms will provide more insight into the optimal approach for diagnosis of land surface fluxes using remote sensing observations.

Assessment of the uncertainty related to irrigation modeling by a land surface model across India

2020 ◽  
Author(s):  
Dazhi Li ◽  
Xujun Han ◽  
Dhanya C.t. ◽  
Stefan Siebert ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Monsoon Season ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Spatiotemporal Pattern ◽  
Surface Model ◽  
Irrigation Requirement ◽  
Community Land Model ◽  
Surface Models

<p>Irrigation is very important for maintaining the agricultural production and sustaining the increasing population of India. The irrigation requirement can be estimated with land surface models by modeling water storage changes but the estimates are affected by various uncertainties such as regarding the spatiotemporal distribution of areas where and when irrigation is potentially applied. In the present work, this uncertainty is analyzed for the whole Indian domain. The irrigation requirements and hydrological fluxes over India were reconstructed by multiple simulation experiments with the Community Land Model (CLM) version 4.5 for the year of 2010.</p><p>These multiple simulation scenarios showed that the modeled irrigation requirement and the land surface fluxes differed between the scenarios, representing the spatiotemporal uncertainty of the irrigation maps. Using a season-specific irrigation map resulted in a higher transpiration-evapotranspiration ratio (T/ET) in the pre-monsoon season compared to the application of a static irrigation map, which implies a higher irrigation efficiency. The remote sensing based evapotranspiration products GLEAM and MODIS ET were used for comparison, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced land surface modeling. The correspondence is better if the seasonal irrigation map is used as basis for simulations with CLM. We conclude that more accurate temporal information on irrigation results in modeled evapotranspiration closer to the spatiotemporal pattern of evapotranspiration deduced from remote sensing. Another conclusion is that irrigation modeling should consider the sub-grid heterogeneity to improve the estimation of soil water deficit and irrigation requirement.</p>

scholarly journals Building a field- and model-based climatology of local water and energy cycles in the cultivated Sahel – annual budgets and seasonality

2014 ◽  
Vol 11 (5) ◽  
pp. 4753-4808 ◽  
Author(s):  
C. Velluet ◽  
J. Demarty ◽  
B. Cappelaere ◽  
I. Braud ◽  
H. B.-A. Issoufou ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Surface Fluxes ◽  
Process Understanding ◽  
Land Use Types ◽  
Physically Based ◽  
Data Imprecision ◽  
Local Water ◽  
Physical Laws ◽  
And Storage

Abstract. In the African Sahel, energy and water cycling at the land surface is pivotal for regional climate, water resources and land productivity, yet it is still extremely poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt, rainfed millet crop and fallow bush. These estimates build on the combination of a 7 year field dataset from two typical plots in southwestern Niger with detailed physically-based soil-plant-atmosphere modelling, yielding a continuous, comprehensive set of water and energy flux and storage variables over the 7 year period. In this study case in particular, blending field data with mechanistic modelling is considered as making best use of available data and knowledge for such purpose. It extends observations by reconstructing missing data and extrapolating to unobserved variables or periods. Furthermore, model constraining with observations compromises between extraction of observational information content and integration of process understanding, hence accounting for data imprecision and departure from physical laws. Climatological averages of all water and energy variables, with associated sampling uncertainty, are derived at annual to subseasonal scales from the 7 year series produced. Similarities and differences in the two ecosystems behaviors are highlighted. Mean annual evapotranspiration is found to represent ~82–85% of rainfall for both systems, but with different soil evaporation/plant transpiration partitioning and different seasonal distribution. The remainder consists entirely of runoff for the fallow, whereas drainage and runoff stand in a 40–60% proportion for the millet field. These results should provide a robust reference for the surface energy- and water-related studies needed in this region. The model developed in this context has the potential for reliable simulations outside the reported conditions, including changing climate and land cover.

scholarly journals Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems

2021 ◽  
Vol 21 (12) ◽  
pp. 9609-9628
Author(s):  
Brad Weir ◽  
Lesley E. Ott ◽  
George J. Collatz ◽  
Stephan R. Kawa ◽  
Benjamin Poulter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Growth Rates ◽  
Land Surface ◽  
Surface Fluxes ◽  
Short Latency ◽  
Satellite Observations ◽  
Seasonal Forecasts ◽  
Spatial Coverage

Abstract. The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide (CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets; for example, inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal forecasts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

scholarly journals Temporal interpolation of land surface fluxes derived from remote sensing – results with an Unmanned Aerial System

2019 ◽  
Author(s):  
Sheng Wang ◽  
Monica Garcia ◽  
Andreas Ibrom ◽  
Peter Bauer-Gottwein
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Fluxes ◽  
Deciduous Tree ◽  
Unmanned Aerial System ◽  
Transfer Model ◽  
Climatic Data ◽  
Time Step ◽  
Dynamic Component ◽  
Ground Heat Flux

Abstract. Remote sensing imagery can provide snapshots of rapidly changing land surface variables, e.g. evapotranspiration (ET), land surface temperature (Ts), net radiation (Rn), soil moisture (SM) and gross primary productivity (GPP), for the time of sensor overpass. However, discontinuous data acquisitions limit the applicability of remote sensing for water resources and ecosystem management. Methods to interpolate between remote sensing snapshot data and to upscale them from instantaneous to daily time scale are needed. We developed a dynamic Soil Vegetation Atmosphere Transfer model to interpolate land surface state variables that change rapidly between remote sensing observations. The Soil-Vegetation, Energy, water and CO2 traNsfer model (SVEN), which combines the snapshot version of the remote sensing Priestley Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, incorporates now a dynamic component for the ground heat flux based on the force-restore method and a water balance bucket model to estimate SM and canopy wetness at half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an Unmanned Aerial System in a willow plantation flux site (Risoe, Denmark). Based on model parameter calibration with the snapshots of land surface variables at the time of flight, SVEN interpolated the snapshot Ts, Rn, SM, ET and GPP to continuous records for the growing season of 2016 with forcing from continuous climatic data and NDVI. Validation with eddy covariance and other in-situ observations indicates that SVEN can well estimate daily land surface fluxes between remote sensing acquisitions with root mean square deviations of the simulated daily Ts, Rn, SM, LE and GPP equal to 2.35 °C, 14.49 W m−2, 1.98 % m3 m−3, 16.62 W m−2 and 3.01 g C m−2 d−1, respectively. This study demonstrates that, in this deciduous tree planation, temporally sparse optical and thermal remote sensing observations can be used as ground truth to calibrate soil and vegetation parameters of a simple land surface modelling scheme to estimate low persistence or rapidly changing land surface variables with the use of few forcing variables. This approach can also be applied with remotely sensed data from other platforms to fill temporal gaps, e.g. cloud induced data gaps in satellite observation.

scholarly journals Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1924 ◽  
Author(s):  
Matthias Wocher ◽  
Katja Berger ◽  
Martin Danner ◽  
Wolfram Mauser ◽  
Tobias Hank
Keyword(s):  
Water Content ◽  
Optical Sensors ◽  
Land Surface ◽  
Hyperspectral Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Equivalent Water Thickness ◽  
Crop Type ◽  
Physically Based ◽  
Leaf Level

Quantitative equivalent water thickness on canopy level (EWTcanopy) is an important land surface variable and retrieving EWTcanopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWTcanopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWTcanopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWTcanopy in the scope of future hyperspectral satellite missions.

Modeling and Scaling Coupled Energy, Water, and Carbon Fluxes Based on Remote Sensing: An Application to Canada’s Landmass

2007 ◽  
Vol 8 (2) ◽  
pp. 123-143 ◽  
Author(s):  
Baozhang Chen ◽  
Jing M. Chen ◽  
Gang Mo ◽  
Chiu-Wai Yuen ◽  
Hank Margolis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
General Circulation ◽  
Carbon Fluxes ◽  
General Circulation Models ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Sensible Heat ◽  
Area Index ◽  
Up Scaling

Abstract Land surface models (LSMs) need to be coupled with atmospheric general circulation models (GCMs) to adequately simulate the exchanges of energy, water, and carbon between the atmosphere and terrestrial surfaces. The heterogeneity of the land surface and its interaction with temporally and spatially varying meteorological conditions result in nonlinear effects on fluxes of energy, water, and carbon, making it challenging to scale these fluxes accurately. The issue of up-scaling remains one of the critical unsolved problems in the parameterization of subgrid-scale fluxes in coupled LSM and GCM models. A new distributed LSM, the Ecosystem–Atmosphere Simulation Scheme (EASS) was developed and coupled with the atmospheric Global Environmental Multiscale model (GEM) to simulate energy, water, and carbon fluxes over Canada’s landmass through the use of remote sensing and ancillary data. Two approaches (lumped case and distributed case) for handling subgrid heterogeneity were used to evaluate the effect of land-cover heterogeneity on regional flux simulations based on remote sensing. Online runs for a week in August 2003 provided an opportunity to investigate model performance and spatial scaling issues. Comparisons of simulated results with available tower observations (five sites) across an east–west transect over Canada’s southern forest regions indicate that the model is reasonably successful in capturing both the spatial and temporal variations in carbon and energy fluxes, although there were still some biases in estimates of latent and sensible heat fluxes between the simulations and the tower observations. Moreover, the latent and sensible heat fluxes were found to be better modeled in the coupled EASS–GEM system than in the uncoupled GEM. There are marked spatial variations in simulated fluxes over Canada’s landmass. These patterns of spatial variation closely follow vegetation-cover types as well as leaf area index, both of which are highly correlated with the underlying soil types, soil moisture conditions, and soil carbon pools. The surface fluxes modeled by the two up-scaling approaches (lumped and distributed cases) differ by 5%–15% on average and by up to 15%–25% in highly heterogeneous regions. This suggests that different ways of treating subgrid land surface heterogeneities could lead to noticeable biases in model output.

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