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 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 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.

Long-Term Climate and Derived Surface Hydrology and Energy Flux Data for Mexico: 1925–2004

2007 ◽  
Vol 20 (9) ◽  
pp. 1936-1946 ◽  
Author(s):  
Chunmei Zhu ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
North American ◽  
Land Surface ◽  
Surface Fluxes ◽  
The Other ◽  
Shortwave Radiation ◽  
Infiltration Capacity ◽  
Time Step ◽  
Downward Longwave Radiation

Abstract Studying the role of land surface conditions in the Mexican portion of the North American monsoon system (NAMS) region has been a challenge due to the paucity of long-term observations. A long-term gridded observation-based climate dataset suitable for forcing land surface models, as well as model-derived land surface states and fluxes for a domain consisting of all of Mexico, is described. The datasets span the period of January 1925–October 2004 at 1/8° spatial resolution at a subdaily (3 h) time step. The simulated runoff matches the observations plausibly over most of the 14 small river basins spanning all of Mexico, which suggests that long-term mean evapotranspiration is realistically reproduced. On this basis, and given the physically based model parameterizations of soil moisture and energy fluxes, the other surface fluxes and state variables such as soil moisture should be represented reasonably. In addition, a comparison of the surface fluxes from this study is performed with North American Regional Reanalysis (NARR) data on a seasonal mean basis. The results indicate that downward shortwave radiation is generally smaller than in the NARR data, especially in summer. Net radiation, on the other hand, is somewhat larger in the Variable Infiltration Capacity (VIC) hydrological model than in the NARR data for much of the year over much of the domain. The differences in radiative and turbulent fluxes are attributed to (i) the parameterization used in the VIC forcings for solar and downward longwave radiation, which links them to the daily temperature and temperature range, and (ii) differences in the land surface parameterizations used in VIC and the NCEP–Oregon State University–U.S. Air Force–NWS/Hydrologic Research Lab (Noah) land scheme used in NARR.

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>

A LONG TERM FIELD EXPERIMENT FOR RADIATIVE TRANSFER MODEL DEVELOPMENT AND LAND SURFACE PROCESSES REMOTE SENSING

2008 ◽  
Vol 52 ◽  
pp. 13-18
Author(s):  
Hui LU ◽  
Toshio KOIKE ◽  
Hiroyuki TSUTSUI ◽  
David Ndegwa KURIA ◽  
Tobias GRAF ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Field Experiment ◽  
Land Surface ◽  
Model Development ◽  
Surface Processes ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Term Field

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 REMOTE SENSING OF GLOBAL MONTHLY EVAPOTRANSPIRATION WITH AN ENERGY BALANCE (EB) MODEL

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1729-1733
Author(s):  
X. Chen ◽  
Z. Su ◽  
Y. Ma
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Land Surface ◽  
Roughness Length ◽  
Meteorological Data ◽  
Global Scale ◽  
Balance Model ◽  
Time Step ◽  
Available Energy ◽  
Hydrological Cycles

<p><strong>Abstract.</strong> A global monthly evapotranspiration (ET) product without spatial-temporal gaps for 2000&amp;ndash;2017 is delivered by using an energy balance (EB) algorithm and MODIS satellite data. It provides us with a moderate resolution estimate of ET without spatial-temporal gaps on a global scale. The model is driven by monthly remote sensing land surface temperature and ERA-Interim meteorological data. A global turbulent exchange parameterization scheme was developed for global momentum and heat roughness length calculation with remote sensing information. The global roughness length was used in the energy balance model, which uses monthly land-air temperature gradient to estimate the turbulent sensible heat, and take the latent heat flux as a residual of the available energy. This study produced an ET product for global landmass, at a monthly time step and 0.05-degree spatial resolution. The performance of ET data has been evaluated in comparison to hundreds flux sites measurements representing a broad range of land covers and climates. The ET product has a mean bias of 3.3&amp;thinsp;mm/month, RMSE value of 36.9&amp;thinsp;mm/month. The monthly ET product can be used to study the global energy and hydrological cycles at either seasonal or inter-annual temporal resolution.</p>

Measuring and modelling thermal erosion patterns of peat plateaus in northern Norway

2020 ◽  
Author(s):  
Sebastian Westermann ◽  
Leo Martin ◽  
Jan Nitzbon ◽  
Kjetil Aas ◽  
Johanna Scheer ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Subsidence ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Major Type ◽  
Surface Model ◽  
Climatic Data ◽  
Climate Signal ◽  
Northern Norway ◽  
Peat Plateaus

&lt;p&gt;Peat plateaus are a major type of permafrost landscape in Arctic and Siberian lowlands. They represent a substantial pool of several hundreds of petagrams of organic carbon that has the potential to contribute to the Permafrost Carbon Feedback. The thermal response of these soils to the climate signal is complex and implies the interaction of various surface and subsurface processes operating at a very small spatial scale involving water, snow and heat fluxes and surface subsidence. As these processes have the ability to generate feedbacks between each other and trigger non-linear evolutions of the landscape, they challenge our abilities to measure and model them.&lt;/p&gt;&lt;p&gt;Peat plateaus in Northern Norway have been actively degrading over at least the last 60 years. They thus offer a precious opportunity to measure and model the degradation patterns they exhibit. We present new topographical observations derived from drone-based photogrammetry that we acquired for one site in Northern Norway. Over a period of 3 years, these Digital Elevation Models allows quantifying precisely the surface subsidence and resulting lateral degradation of the peat plateaus. In a second time, we use the land surface model CryoGrid to model the observed patterns. The model is able to (i) simulate the snow fluxes and the water and heat sub-surface fluxes within the plateau and between the plateau and the surrounding wet mire and to (ii) represent the soil surface subsidence due to excess ice melt in the soil. We implement a set up that discretize the interface between the peat plateaus and the wet mire and force the Surface Energy Balance module of the model with climatic data derived from regional atmospheric modelling.&lt;/p&gt;&lt;p&gt;Our simulations manage to reproduce the degradation speed we observe in our topographical data. We also present a sensitivity analysis of the degradation speed to snow cover and to the geometry of the peat plateaus and show how the feedbacks between the dynamical topography and the lateral fluxes of snow and water can trigger rapid permafrost thawing and fast degradation of permafrost landscapes.&lt;/p&gt;

scholarly journals An Evaluation of Satellite Remote Sensing Data Products for Land Surface Hydrology: Atmospheric Infrared Sounder*

2010 ◽  
Vol 11 (6) ◽  
pp. 1234-1262 ◽  
Author(s):  
Craig R. Ferguson ◽  
Eric F. Wood
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Specific Humidity ◽  
Retrieval Algorithm ◽  
Climatic Data ◽  
Atmospheric Infrared Sounder ◽  
Retrieval Models ◽  
Near Surface

Abstract The skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (Ts), air temperature (Ta), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (Psurf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-based water cycle monitoring in areas without readily available in situ data. The study is conducted over the continental United States and Africa for a period of more than 6 years (2002–08). For both regions, it provides for the first time the geographic distribution of AIRS retrieval performance. Through conditional sampling, attribution of retrieval errors to scene atmospheric and surface conditions is performed. The findings support previous assertions that performance degrades with cloud fraction and that (positive) bias enhances with altitude. In general AIRS is biased warm and dry. In certain regions, strong AIRS–NCDC correlation suggests that bias-driven errors, which can be substantial, are correctable. The utility of the error characteristics for prescribing the input-induced uncertainty of RS retrieval models is demonstrated through two applications: a microwave soil moisture retrieval algorithm and the Penman–Monteith evapotranspiration model. An important side benefit of this study is the verification of NLDAS forcing.

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