scholarly journals Ground Heat Flux Determination according to Land Skin Temperature Observations from In Situ Stations and Satellites

2005 ◽  
Vol 6 (4) ◽  
pp. 371-390 ◽  
Author(s):  
Ben-Jei Tsuang
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Skin Temperature ◽  
Land Surface ◽  
Rapid Progress ◽  
Ground Heat Flux ◽  
Global Coverage ◽  
Temperature Observations ◽  
Remote Sensing Techniques

Abstract Due to rapid progress in the development of remote sensing techniques, skin temperature can now be observed with global coverage from satellites. This study derives an equation for utilizing skin temperature measurements to determine ground heat flux. This equation is verified at the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) campaign site and four Fluxnet sites. A preliminary monthly global dataset of ground heat flux with 1° resolution, covering the years 1984–95, is derived based on skin temperature observations obtained via satellite. It shows that the seasonal variation of ground heat flux over land can be determined at 1.5 months ahead by observing the increasing rate of skin temperature, and that the variation increases from ±2 W m−2 at the equator to ±20 W m−2 at the poles. Over land, the resulting ground heat flux can be compared with that of the ERA-15 reanalysis, showing rms differences of about 4 W m−2.

scholarly journals Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 277 ◽  
Author(s):  
Ali Nadir Arslan ◽  
Zuhal Akyürek
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Land Surface ◽  
Climate Models ◽  
Weather Forecasting ◽  
Special Issue ◽  
Carbon Cycles ◽  
Wide Range ◽  
Remote Sensing Techniques

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, “Remote Sensing of Snow and Its Applications” in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above.

scholarly journals Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

2010 ◽  
Vol 11 (5) ◽  
pp. 1103-1122 ◽  
Author(s):  
Rolf H. Reichle ◽  
Sujay V. Kumar ◽  
Sarith P. P. Mahanama ◽  
Randal D. Koster ◽  
Q. Liu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Skin Temperature ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Ground Heat Flux ◽  
Assimilation System

Abstract Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

scholarly journals A new parameterisation scheme of ground heat flux for land surface flux retrieval from remote sensing information

2012 ◽  
Vol 454-455 ◽  
pp. 113-122 ◽  
Author(s):  
M. Tanguy ◽  
A. Baille ◽  
M.M. González-Real ◽  
C. Lloyd ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Land Surface ◽  
Surface Flux ◽  
Ground Heat Flux

scholarly journals Estimating Surface Soil Heat Flux in Permafrost Regions Using Remote Sensing-Based Models on the Northern Qinghai-Tibetan Plateau under Clear-Sky Conditions

2019 ◽  
Vol 11 (4) ◽  
pp. 416 ◽  
Author(s):  
Cheng Yang ◽  
Tonghua Wu ◽  
Jiemin Wang ◽  
Jimin Yao ◽  
Ren Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Soil ◽  
Ground Surface ◽  
Soil Heat Flux ◽  
Parameterization Scheme ◽  
Clear Sky ◽  
Sky Conditions ◽  
Permafrost Regions

The ground surface soil heat flux (G0) quantifies the energy transfer between the atmosphere and the ground through the land surface. However; it is difficult to obtain the spatial distribution of G0 in permafrost regions because of the limitation of in situ observation and complication of ground surface conditions. This study aims at developing an improved G0 parameterization scheme applicable to permafrost regions of the Qinghai-Tibet Plateau under clear-sky conditions. We validated several existing remote sensing-based models to estimate G0 by analyzing in situ measurement data. Based on the validation of previous models on G0; we added the solar time angle to the G0 parameterization scheme; which considered the phase difference problem. The maximum values of RMSE and MAE between “measured G0” and simulated G0 using the improved parameterization scheme and in situ data were calculated to be 6.102 W/m2 and 5.382 W/m2; respectively. When the error of the remotely sensed land surface temperature is less than 1 K and the surface albedo measured is less than 0.02; the accuracy of estimates based on remote sensing data for G0 will be less than 5%. MODIS data (surface reflectance; land surface temperature; and emissivity) were used to calculate G0 in a 10 x 10 km region around Tanggula site; which is located in the continuous permafrost region with long-term records of meteorological and permafrost parameters. The results obtained by the improved scheme and MODIS data were consistent with the observation. This study enhances our understanding of the impacts of climate change on the ground thermal regime of permafrost and the land surface processes between atmosphere and ground surface in cold regions.

Geoacoustic and Geological Characterization of Surficial Marine Sediments by In Situ Probe and Remote Sensing Techniques

2019 ◽  
pp. 295-350
Author(s):  
Richard H. Bennett ◽  
Huon Li ◽  
Michael D. Richardson ◽  
Peter Fleischer ◽  
Douglas N. Lambert ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Marine Sediments ◽  
Remote Sensing Techniques

Hydrological functioning of irrigated maize crops in southwest France using Eddy Covariance measurements and a land surface model

2021 ◽  
Author(s):  
Oluwakemi Dare-Idowu ◽  
Lionel Jarlan ◽  
Aurore Brut ◽  
Valerie Le-Dantec ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Sap Flow ◽  
Heat Fluxes ◽  
Added Value ◽  
Surface Model ◽  
Data Set ◽  
Flow Measurements ◽  
Ground Heat Flux

<p>This study aims to analyze the main components of the energy and hydric budgets of irrigated maize in southwestern France. To this objective, the ISBA-A-gs (<span>Interactions between Soil, Biosphere, and Atmosphere) </span>is run over six maize growing seasons. As a preliminary step, the ability of the ISBA-A-gs model to predict the different terms of the energy and water budgets is assessed thanks to a large database of <em>in situ</em> measurements by comparing the single budget version of the model with the new Multiple Energy Balance version solving an energy budget separately for the soil and the vegetation. The <em>in situ</em> data set acquired at the Lamasquere site (43.48<sup>o</sup> N, 1.249<sup>o</sup> E) includes half-hourly measurements of sensible (H) and latent heat fluxes (LE) estimated by an Eddy Covariance system. Measurements also include net radiation (Rn), ground heat flux (G), plant transpiration with sap flow sensors, meteorological variables, and 15-days measurements of vegetation characteristics. The seasonal dynamics of the turbulent fluxes were properly reproduced by both configurations of the model with an R² ranging from 0.66 to 0.89, and a root mean square error lower than 48 W m<sup>-2</sup>. Statistical metrics showed that H was better predicted by MEB with R² of 0.80 in comparison to ISBA-Ags (0.73). However, the difference between the RMSE of ISBA-Ags and MEB during the well-developed stage of the plants for both H and LE does not exceed 8 W m<sup>-2</sup>. This implies that MEB only has a significant added value over ISBA-Ags when the soil and the canopy are not fully coupled, and over a heterogeneous field. Furthermore, this study made a comparison between the sap flow measurements and the transpiration simulated by ISBA-A-gs and MEB. A good dynamics was reproduced by ISBA-A-gs and MEB, although, MEB (R²= 0.91) provided a slightly more realistic estimation of the vegetation transpiration. Consequently, this study investigated the dynamics of the water budget during the growing maize seasons. Results indicated that drainage is almost null on the site, while the observed values of cumulative evapotranspiration that was higher than the water inputs are related to a shallow ground table that provides supplement water to the crop. This work provides insight into the modeling of water and energy exchanges over maize crops and opens perspectives for better water management of the crop in the future.</p>

scholarly journals Extension of the Hapke bidirectional reflectance model to retrieve soil water content

2011 ◽  
Vol 15 (7) ◽  
pp. 2317-2326 ◽  
Author(s):  
G.-J. Yang ◽  
C.-J. Zhao ◽  
W.-J. Huang ◽  
J.-H. Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Ant Colony Algorithm ◽  
Heat Fluxes ◽  
Spatial And Temporal Variation ◽  
Bidirectional Reflectance ◽  
Equivalent Water Thickness

Abstract. Soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes. An accurate assessment of the spatial and temporal variation of soil moisture is important to the study of surface biogeophysical processes. Although remote sensing has proven to be one of the most powerful tools for obtaining land surface parameters, no effective methodology yet exists for in situ soil moisture measurement based on a Bidirectional Reflectance Distribution Function (BRDF) model, such as the Hapke model. To retrieve and analyze soil moisture, this study applied the soil water parametric (SWAP)-Hapke model, which introduced the equivalent water thickness of soil, to ground multi-angular and hyperspectral observations coupled with, Powell-Ant Colony Algorithm methods. The inverted soil moisture data resulting from our method coincided with in situ measurements (R2 = 0.867, RMSE = 0.813) based on three selected bands (672 nm, 866 nm, 2209 nm). It proved that the extended Hapke model can be used to estimate soil moisture with high accuracy based on the field multi-angle and multispectral remote sensing data.

scholarly journals In-situ measurements of the ice flow motion at Eqip Sermia Glacier using a remotely controlled UAV

2019 ◽  
Author(s):  
Guillaume Jouvet ◽  
Eef van Dongen ◽  
Martin P. Lüthi ◽  
Andreas Vieli
Keyword(s):  
Remote Sensing ◽  
Template Matching ◽  
In Situ Measurements ◽  
In Situ Monitoring ◽  
High Temporal Resolution ◽  
Ice Flow ◽  
Flow Motion ◽  
Remote Sensing Techniques ◽  
Matching Techniques

Abstract. Measuring the ice flow motion accurately is essential to better understand the time evolution of glaciers and ice sheets, and therefore to better anticipate the future consequence of climate change in terms of sea-level rise. Although there exist a variety of remote sensing methods to fill this task, in-situ measurements are always needed for validation or to capture high temporal resolution movements. Yet glaciers are in general hostile environments where the installation of instruments might be tedious and risky when not impossible. Here we report the first-ever in-situ measurements of ice flow motion using a remotely controlled Unmanned Aerial Vehicle (UAV). We used a multicopter UAV to land on a highly crevassed area of Eqip Sermia Glacier, West Greenland, to measure the displacement of the glacial surface with the aid of an on-board differential GNSS receiver. Despite the unfortunate loss of the UAV, we measured approximately 70 cm of displacement over 4.36 hours without setting foot onto the glacier – a result validated by applying UAV photogrammetry and template matching techniques. Our study demonstrates that UAVs are promising instruments for in-situ monitoring, and have a great potential for capturing short-term ice flow variations in inaccessible glaciers – a task that remote sensing techniques can hardly achieve.

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