A temperature prediction-correction method for estimating surface soil heat flux from soil temperature and moisture data

Yang, Kun; Wang, Jiemin

doi:10.1007/s11430-008-0036-1

Cited by 136 publications

(87 citation statements)

References 16 publications

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“…The retrieval methods of land surface variables were validated in other areas. For example, the albedo algorithm was previously applied to retrieve Global Land Surface Satellite (GLASS) products (Liang et al, 2014), the LST retrieval algorithm was validated in the Haihe River basin in northern China (H. , and the soil heat flux correction algorithm was validated in the GAME-Tibet campaign (Yang and Wang, 2008). Since the surface of the Heihe River basin is very heterogeneous, additional comparisons of our algorithm in other areas would be helpful.…”

Section: Discussionmentioning

confidence: 99%

“…EC outputs are available every 30 min. G was measured by using three soil heat plates at a depth of 6 cm at each site, and the surface G was calculated using the method proposed by Yang and Wang (2008) based on the soil temperature and moisture above the plates. Surface meteorological variables, such as wind speed, wind direction, relative humidity, and air pressure, were used to interpolate images using the inverse distance weighting.…”

Section: Hiwater Experiments Data Setmentioning

confidence: 99%

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Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Peng

Xin

Jiao

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration (ET) plays an important role in surface-atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR) band. A temperature-sharpening and flux aggregation scheme (TSFA) was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling) and TRFA (temperature resampling and flux aggregation) methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research) were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R 2 , MBE, and RMSE values of the sensible heat flux (H ) were 0.61, 0.90, and 50.99 W m −2 , respectively, and those for the latent heat flux (LE) were 0.82, −20.54, and 71.24 W m −2 , respectively. IPUS yielded the largest errors in ET estimation. The RMSE of LE between the TSFA and IPUS methods was 51.30 W m −2 , and the RMSE of LE between the TSFA and TRFA methods was 16.48 W m −2 . Furthermore, additional analysis showed that the TSFA method can capture the subpixel variations of land surface temperature and the influences of various landscapes within mixed pixels.

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Section: Discussionmentioning

confidence: 99%

Section: Hiwater Experiments Data Setmentioning

confidence: 99%

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Peng

Xin

Jiao

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Because the soil heat flux plates were buried at depths of 0.05 m and 0.15 m in this study (Table 1), the soil surface heat flux was estimated using the method proposed by Yang and Wang (2008), which is a temperature prediction-correction method based on the thermal exchange equation using the profile of soil temperature and moisture observations, as follows:…”

Section: Soil Surface Heat Fluxmentioning

confidence: 99%

A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem

Liu

Wang

et al. 2011

Hydrol. Earth Syst. Sci.

480

275

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Abstract.We analyzed the seasonal variations of energy balance components over three different surfaces: irrigated cropland (Yingke, YK), alpine meadow (A'rou, AR), and spruce forest (Guantan, GT). The energy balance components were measured using eddy covariance (EC) systems and a large aperture scintillometer (LAS) in the Heihe River Basin, China, in 2008 and 2009. We also determined the source areas of the EC and LAS measurements with a footprint model for each site and discussed the differences between the sensible heat fluxes measured with EC and LAS at AR. The results show that the main EC source areas were within a radius of 250 m at all of the sites. The main source area for the LAS (with a path length of 2390 m) stretched along a path line approximately 2000 m long and 700 m wide. The surface characteristics in the source areas changed with the season at each site, and there were characteristic seasonal variations in the energy balance components at all of the sites. The sensible heat flux was the main term of the energy budget during the dormant season. During the growing season, however, the latent heat flux dominated the energy budget, and an obvious "oasis effect" was observed at YK. The sensible heat fluxes measured by LAS at AR were larger than those measured by EC at the same site. This difference seems to be caused by the so-called energy imbalance phenomenon, the heterogeneity of the underlying surfaces, and the difference between the source areas of the LAS and EC measurements.

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“…Soil surface temperature was not measured. A different approach involving numerical solution of the onedimensional thermal diffusion equation was used by Yang and Wang [11]. The method used soil temperature measurements at the soil surface and several soil depths.…”

Section: Introductionmentioning

confidence: 99%

Soil profile method for soil thermal diffusivity, conductivity and heat flux: Comparison to soil heat flux plates

Evett

Agam

Kustas

et al. 2012

Advances in Water Resources

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A temperature prediction-correction method for estimating surface soil heat flux from soil temperature and moisture data

Cited by 136 publications

References 16 publications

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem

Soil profile method for soil thermal diffusivity, conductivity and heat flux: Comparison to soil heat flux plates

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