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

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.

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Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica

The Cryosphere Discussions ◽

10.5194/tcd-8-55-2014 ◽

2014 ◽

Vol 8 (1) ◽

pp. 55-84 ◽

Cited By ~ 3

Author(s):

H. Fréville ◽

E. Brun ◽

G. Picard ◽

N. Tatarinova ◽

L. Arnaud ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Data Set ◽

Warm Bias ◽

Clear Sky ◽

Surface Temperatures ◽

Stable Conditions ◽

Land Surface Temperatures ◽

Sky Conditions

Abstract. MODIS land surface temperatures in Antarctica were processed in order to produce a gridded data set at 25 km resolution, spanning the period 2000–2011 at an hourly time-step. The AQUA and TERRA orbits and MODIS swath width, combined with frequent clear-sky conditions, lead to very high availability of quality-controlled observations: on average, hourly data are available 14 h per day at the grid points around the South Pole and more than 9 over a large area of the Antarctic Plateau. Processed MODIS land surface temperatures, referred to hereinafter as MODIS Ts, were compared with in situ hourly measurements of surface temperature collected over the entire year 2009 by 7 stations from the BSRN and AWS networks. In spite of an occasional failure in the detection of clouds, MODIS Ts exhibit a good performance, with a bias ranging from −1.8 to 0.1 °C and errors ranging from 2.2 to 4.8 °C root mean square at the 5 stations located on the plateau. These results show that MODIS Ts can be used as a precise and accurate reference to test other surface temperature data sets. Here, we evaluate the performance of surface temperature in the ERA-Interim reanalysis. During conditions detected as cloud-free by MODIS, ERA-Interim shows a widespread warm bias in Antarctica in every season, ranging from +3 to +6 °C on the plateau. This confirms a recent study which showed that the largest discrepancies in 2 m air temperature between ERA-Interim and the HadCRUT4 data set occur in Antarctica. A comparison with in situ surface temperature shows that this bias is not strictly limited to clear-sky conditions. A detailed comparison with stand-alone simulations by the Crocus snowpack model, forced by ERA-Interim, and with the ERA-Interim/land simulations, shows that the warm bias may be due primarily to an overestimation of the surface turbulent fluxes in very stable conditions. Numerical experiments with Crocus show this is likely due to an overestimation of the surface exchange coefficients under very stable conditions.

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Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

10.5194/hess-2017-597 ◽

2017 ◽

Author(s):

Sibo Zhang ◽

Jean-Christophe Calvet ◽

José Darrozes ◽

Nicolas Roussel ◽

Frédéric Frappart ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Ground Surface ◽

Satellite System ◽

Surface Model ◽

Global Navigation Satellite ◽

Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

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Earth Observations-Based Evapotranspiration in Northeastern Thailand

Remote Sensing ◽

10.3390/rs11020138 ◽

2019 ◽

Vol 11 (2) ◽

pp. 138 ◽

Cited By ~ 4

Author(s):

Chaolei Zheng ◽

Li Jia ◽

Guangcheng Hu ◽

Jing Lu

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Microwave Remote Sensing ◽

Spatiotemporal Pattern ◽

Monsoon Climate ◽

Thermal Remote Sensing ◽

Clear Sky

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

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Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

Remote Sensing ◽

10.3390/rs11030216 ◽

2019 ◽

Vol 11 (3) ◽

pp. 216 ◽

Cited By ~ 6

Author(s):

Martha Anderson ◽

George Diak ◽

Feng Gao ◽

Kyle Knipper ◽

Christopher Hain ◽

...

Keyword(s):

Remote Sensing ◽

Real Time ◽

Land Surface ◽

Water Resource Management ◽

Radiant Energy ◽

Data Sources ◽

Delta Region ◽

Spatiotemporal Resolution ◽

Sky Conditions ◽

Mean Square Errors

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento–San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts.

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Towards an operative land surface temperature in-situ measurements system for remote sensing models validations

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. ◽

10.1109/igarss.2005.1525487 ◽

2005 ◽

Cited By ~ 1

Author(s):

A. Chahboun ◽

N. Raissouni ◽

M. Essaaid

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

In Situ Measurements

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

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2317-2011 ◽

2011 ◽

Vol 15 (7) ◽

pp. 2317-2326 ◽

Cited By ~ 13

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.

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Verification of a Clear-Sky Mesoscale Simulation Using Satellite-Derived Surface Temperatures

Monthly Weather Review ◽

10.1175/2008mwr2461.1 ◽

2008 ◽

Vol 136 (12) ◽

pp. 5148-5161 ◽

Cited By ~ 19

Author(s):

M. A. Jiménez ◽

A. Mira ◽

J. Cuxart ◽

A. Luque ◽

S. Alonso ◽

...

Keyword(s):

High Temporal Resolution ◽

Local Cooling ◽

Satellite Measurements ◽

Mesoscale Simulation ◽

Clear Sky ◽

Surface Temperatures ◽

Satellite Sensors ◽

Sky Conditions ◽

Mesoscale Simulations

Abstract A mesoscale simulation for Majorca Island is made using the Méso-NH model for a spring night, under a slack synoptic pressure gradient with weak general winds and clear skies. The circulations over and around the island are driven mostly by the locally generated flows, due to the topography and the land–sea thermal contrast. The verification of mesoscale simulations in clear-sky conditions is difficult, especially if the network of stations is not very dense. The main objective of this work is to try to verify the mesoscale simulation using measurements from automatic weather stations and satellite measurements. The model outputs are compared with the available instrumental data and the representativeness of the stations is discussed. Furthermore, complete two-dimensional comparisons are made between the radiative surface temperatures produced by the model and those processed from the National Oceanic and Atmospheric Administration and Meteosat Second Generation (MSG) satellite sensors. The high temporal resolution of the MSG images also allows comparison of the temporal evolutions of the surface temperature between satellite pixels and model grid cells. The procedure permits assessment of the closeness of the simulation to in situ and remote sensing observations. The results of the comparison show that the model is able to reproduce most of the observed patterns, such as intense local cooling or persistent outflows at the largest basins.

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Special Issue on Remote Sensing of Snow and Its Applications

Geosciences ◽

10.3390/geosciences9060277 ◽

2019 ◽

Vol 9 (6) ◽

pp. 277 ◽

Cited By ~ 1

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.

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Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5693-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5693-5708 ◽

Cited By ~ 3

Author(s):

Jordi Etchanchu ◽

Vincent Rivalland ◽

Simon Gascoin ◽

Jérôme Cros ◽

Tiphaine Tallec ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Agricultural Landscapes ◽

Surface Model ◽

Spatial And Temporal Variability ◽

Area Index ◽

The Impact ◽

Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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Global Land Surface Temperature from historical NOAA AVHRR datasets (1981-2000)

10.5194/egusphere-egu2020-3102 ◽

2020 ◽

Author(s):

Jin Ma ◽

Ji Zhou ◽

Frank-Michael Göttsche ◽

Shaofei Wang

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Surface Temperature ◽

Spatial Resolution ◽

Land Surface Temperature ◽

Land Surface ◽

Microwave Remote Sensing ◽

Community Land Model ◽

Noaa Avhrr

As one of the most important indicators in the energy exchange between land and atmosphere, Land Surface Temperature (LST) plays an important role in the research of climate change and various land surface processes. In contrast to in-situ measurements, satellite remote sensing provides a practical approach to measure global and local land surface parameters. Although passive microwave remote sensing offers all-weather observation capability, retrieving LST from thermal infra-red data is still the most common approach. To date, a variety of global LST products have been published by the scientific community, e.g. MODIS and (A)ASTR /SLSTR LST products, and used in a broad range of research fields. Several global and regional satellite retrieved LSTs are available since 1995. However, the temporal-spatial resolution before 2000 is generally considerably lower than that after 2000. According to the latest IPCC report, 1983 &#8211; 2012 are the warmest 30 years for nearly 1400 years. Therefore, for global climate change research, it is meaningful to extend the time series of global LST products with a relatively higher temporal-spatial resolution to before 2000, e.g. that of NOAA AVHRR. In this study, global daily NOAA AVHRR LST products with 5-km spatial resolution were generated for 1981-2000. The LST was retrieved using an ensemble of RF-SWAs (Random Forest and Split-Window Algorithm). For a maximum uncertainty in emissivity and water vapor content of 0.04 and 1.0 g/cm2, respectively, the training and testing with simulated datasets showed a retrieval accuracy with MBE of less than 0.1 K and STD of 1.1 K. The generated RF-SWA LST product was also evaluated against in-situ measurements: for water sites of the National Data Buoy Center (NDBC) between 1981 and 2000, it showed an accuracy similar to that for the simulated data, with a small MBE of less than 0.1 K and a STD between 0.79 K and 1.02 K. For SURFRAD data collected between 1995 and 2000, the MBE is -0.03 K with a range of -1.20 K &#8211; 0.54 K and a STD with a mean of 2.55 K and a range of 2.08 K &#8211; 3.0 K (site dependent). As a new global historical dataset, the RF-SWA LST product can help to close the gap in long-term LST data available to climate research. Furthermore, the data can be used as input to land surface process models, e.g. the Community Land Model (CLM). In support of the scientific research community, the RF-SWA LST product will be freely available at the National Earth System Science Data Center of China (http://www.geodata.cn/).

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