Remote Sensing Observations for the Monitoring of Land-Surface Fluxes and Water Budgets

1991 ◽  
pp. 337-347 ◽  
Author(s):  
Thomas J. Schmugge ◽  
F. Becker
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Fluxes ◽  
Water Budgets

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 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.

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.

New results of the Land Atmosphere Feedback Experiment (LAFE)

2021 ◽  
Author(s):  
Volker Wulfmeyer ◽  
David D. Turner
Keyword(s):  
Remote Sensing ◽  
Latent Heat ◽  
Land Surface ◽  
Transport Processes ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Model Verification ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
The Impact

<p>The Land-Atmosphere Feedback Experiment (LAFE) deployed several state-of-the-art scanning lidar and remote sensing systems to the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SPG) site during August 2017. A novel synergy of remote sensing systems was applied for simultaneous measurements of land-surface fluxes and horizontal and vertical transport processes in the atmospheric boundary layer (ABL). The impact of spatial inhomogeneities of the soil-vegetation continuum on L-A feedback was studied using the scanning capability of the instrumentation as well as soil, vegetation, and surface flux measurements. Thus, both the variability of surface fluxes and ABL dynamics and thermodynamics over the SGP site was studied for the first time. The objectives of LAFE are as follows:</p><p>I. Determine turbulence profiles and investigate new relationships among  gradients, variances, and fluxes<br>II. Map surface momentum, sensible heat, and latent heat fluxes using a synergy of scanning wind, humidity, and temperature lidar systems<br>III. Characterize land-atmosphere feedback and the moisture budget at the SGP site via the new LAFE sensor synergy<br>IV: Verify large-eddy simulation model runs and improve turbulence representations in mesoscale models.</p><p>In this presentation, the status of LAFE research and recent achievements of the science objectives are presented and discussed. Concerning I., long-term profiling capabilities of turbulent properties have been developed and will be presented such as continuous measurements of latent heat flux profiles for a duration of one month. Concerning II., we present a combination of tower and remote sensing measurements to study surface layer profiles of wind, temperature, and humidity. A first evaluation of the results demonstrates significant deviations from Monin-Obukhov similarity theory. Concerning III., Convective Triggering Potential (CTP)-Humidity Index (HIlow) metrics are presented at the SGP site to characterize L-A feedback and a new technique for determination of water-vapor advection, as important part of its budget. Last but not least, concerning IV., we present an advanced ensemble model design with turbulence permitting resolution for case studies and model verification from the convection-permitting to the turbulent scales in a realistic mesoscale environment. Using this framework, we introduce a strategy to apply the observations for the test and development of turbulence parameterizations. These results confirm that LAFE will make significant contributions to process understanding and the parameterization of the next generation of high-resolution weather forecast, climate, and earth system models.</p>

scholarly journals Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system

2020 ◽  
Vol 24 (7) ◽  
pp. 3643-3661
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 (θ), 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 an instantaneous to a daily timescale 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” (SVEN) model, which combines the snapshot version of the remote sensing Priestley–Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, now incorporates a dynamic component for the ground heat flux based on the “force-restore” method and a water balance “bucket” model to estimate θ and canopy wetness at a half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an unmanned aerial system at 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 UAS-based snapshots to continuous records of Ts, Rn, θ, ET, and GPP for the 2016 growing season with forcing from continuous climatic data and the normalized difference vegetation index (NDVI). Validation with eddy covariance and other in situ observations indicates that SVEN can estimate daily land surface fluxes between remote sensing acquisitions with normalized root mean square deviations of the simulated daily Ts, Rn, θ, LE, and GPP of 11.77 %, 6.65 %, 19.53 %, 14.77 %, and 12.97 % respectively. In this deciduous tree plantation, this study demonstrates that temporally sparse optical and thermal remote sensing observations can be used 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 observations.

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.

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