Früher|Nächster

Estimating instantaneous evapotranspiration based on dual-angle thermal infrared observations from Sentinel-3 satellite

  • role： First author第一作者
  • Affiliation：

    School of Geography Science, Southwest University, Chongqing 400715, China

  • Email：xkj971121@email.swu.edu.cn
  • Introduction：薛可嘉，1997年生，男，硕士研究生，研究方向为水文气象遥感。E-mail：xkj971121@email.swu.edu.cn
XUE Kejia1,  
  • Affiliation：

    School of Geography Science, Southwest University, Chongqing 400715, China

HE Miao1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute of Chinese Academy of Sciences,Beijing 100101, China

BIAN Zunjian2,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography Science, Southwest University, Chongqing 400715, China

  • Email：songls@swu.edu.cn
  • Introduction：宋立生，1987年生，男，教授，研究方向为水文气象遥感。E-mail：songls@swu.edu.cn
SONG Lisheng1*,  
  • Affiliation：

    School of Geography Science, Southwest University, Chongqing 400715, China

XU Yanhao1,  
  • Affiliation：

    School of Geography Science, Southwest University, Chongqing 400715, China

JIANG Hao1

Resümee

The Two-Source Energy Balance (TSEB) model is often used in the estimation of surface evapotranspiration which is important for the water resources regulation and utilization especially in arid and semi-arid areas. Using the different model inputs, such as land surface temperature and surface component temperatures (soil and vegetation) which are the key boundary of the TSEB model can vary the model performance in the prediction of evapotranspiration. In this paper, the Land Surface Temperature (LST) at nadir angle and Land Surface Component Temperatures (LSCT) retrieved from dual-angle observations of Sentinel-3 were used to drive the TSEB model in Heihe River Basin. At each site, the ground measurements data including net radiation, soil heat flux, sensible and latent heat fluxes were measured by radiometer, heat-plates, eddy covariance, respectively, and with the representativeness ranges from meters to hundreds of meters. The energy balance closure was enforced in the EC system observations using the Bowen ratio approach. What’s more, the large aperture scintillometers were used to measure the sensible heat flux over several kilometers areas to partly solve the mismatch between the model’s output and ground measurement.Then, the outputs including net radiation, soil heat flux, sensible and latent heat fluxes are evaluated using the ground measurements from EC and LAS which have larger source areas over the grassland, cropland and riparian forest landcover types in the Heihe River Basin, respectively. The results showed that both models overestimate the net radiation and latent heat flux with values of mean bias range from 50 to 150 W/m2 and from 60 to 130 W/m2 when compared with the ground measurements. However, the model performances of the TSEB-PT and TSEB-2T varied over different landcover types. In order to further explore the overestimation in latent heat flux from the two models, we intercompared the spatial pattern of plant transpiration estimated by the two models along with the moderate-resolution imaging spectroradiometer (MODIS) leaf area index data and the canopy temperature separated by the two models. It informed that the difference of model separated component temperatures mainly lead to the difference outputs of latent heat fluxes between TSEB-PT model and TSEB-2T model. Additionally, the TSEB-PT model mainly overestimated the canopy transpiration especially over the areas with high vegetation fraction coverage in the upstream of Heihe River Basin. This may due to the canopy temperature separated by the TSEB-PT model have lower values when compared with the inputs of canopy temperature in the TSEB-2T model. The lower canopy temperature could lead to lower sensible heat flux values and result in higher latent heat flux which is calculated as a residual in the land surface energy balance equation.The results showed that the TSEB-PT model has a better performance in the areas with low vegetation fraction coverage such as alpine meadows and riparian forest land cover types. However, the TSEB-2T model performed better in the areas with high vegetation fraction coverage, such as farmland, forest and so on. What’s more, as the fraction of vegetation coverage increase, the advantages of the TSEB-2T model are more obvious. The research results can provide water resources managers with more accurate estimates of land surface water consume over difference ecosystem.

Schlüsselwort

land surface temperature (TSEB-PT);component temperature (TSEB-2T);surface flux;validation

References

  1. 1.
    Anderson M C, Allen R G, Morse A and Kustas W P. 2012. Use of landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, 122: 50-65
  2. 2.
    Bian Z J, Li H, Göttsche F M, Li R B, Du Y M, Ren H Z, Cao B, Xiao Q and Liu Q H. 2020. Retrieving soil and vegetation temperatures from dual-angle and multipixel satellite observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5536-5549
  3. 3.
    Cammalleri C, Anderson M C, Ciraolo G, D'Urso G, Kustas W P, Loggia L and Minacapilli M. 2012. Applications of a remote sensing-based two-source energy balance algorithm for mapping surface fluxes without in situ air temperature observations. Remote Sensing of Environment, 124: 502-515
  4. 4.
    Che T, Li X, Liu S M, Li H Y, Xu Z W, Tan J L, Zhang Y, Ren Z G, Xiao L, Deng J, Jin R, Ma M G, Wang J and Yang X F. 2019. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China. Earth System Science Data, 11(3): 1483-1499
  5. 5.
    Chehbouni A, Nouvellon Y, Lhomme J P, Watts C, Boulet G, Kerr Y H, Moran M S and Goodrich D C. 2001. Estimation of surface sensible heat flux using dual angle observations of radiative surface temperature. Agricultural and Forest Meteorology, 108(1): 55-65
  6. 6.
    Colaizzi P D, Kustas W P, Anderson M C, Agam N, Tolk J A, Evett S R, Howell T A, Gowda P H and O’Shaughnessy S A. 2012. Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures. Advances in Water Resources, 50: 134-151
  7. 7.
    François C. 2002. The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status. Remote Sensing of Environment, 80(1): 122-133
  8. 8.
    French A N, Hunsaker D J and Thorp K R. 2015. Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sensing of Environment, 158: 281-294
  9. 9.
    Hu T, Renzullo L J, Van Dijk A I J M, He J, Tian S Y, Xu Z H, Zhou J, Liu T J and Liu Q H. 2020. Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals. Remote Sensing of Environment, 236: 111419
  10. 10.
    Jönsson P and Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845
  11. 11.
    Kalma J D, McVicar T R and McCabe M F. 2008. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4): 421-469
  12. 12.
    Kustas W and Anderson M. 2009. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology, 149(12): 2071-2081
  13. 13.
    Kustas W P and Daughtry C S T. 1990. Estimation of the soil heat flux/net radiation ratio from spectral data. Agricultural and Forest Meteorology, 49(3): 205-223
  14. 14.
    Kustas W P and Norman J M. 1999. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology, 94(1): 13-29
  15. 15.
    Li X, Cheng G D, Liu S M, Xiao Q, Ma M G, Jin R, Che T, Liu H H, Wang W Z, Qi Y, Wen J G, Li H Y, Zhu G F, Guo J W, Ran Y H, Wang S G, Zhu Z L, Zhou J, Hu X L and Xu Z W. 2013. Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8): 1145-1160
  16. 16.
    Li X, Lu L, Cheng G D and Xiao X L. 2001. Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS. Journal of Arid Environments, 48(4): 521-535
  17. 17.
    Liu M, Tang R L, Li Z L, Gao M F and Yao Y J. 2021. Progress of data-driven remotely sensed retrieval methods? and products on land surface evapotranspiration. National Remote Sensing Bulletin, 25(8): 1517-1537
  18. 18.
    Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The heihe integrated observatory network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 1-21
  19. 19.
    Liu S M, Xu Z W, Wang W Z, Jia Z Z, Zhu M J, Bai J and Wang J M. 2011. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15(4): 1291-1306
  20. 20.
    Norman J M, Kustas W P and Humes K S. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3/4): 263-293
  21. 21.
    Pan X D and Li X. 2011. Validation of WRF model on simulating forcing data for Heihe River Basin. Sciences in Cold and Arid Regions, 3(4): 344-357
  22. 22.
    Pan X D and Ma H Q. 2019. Near-surface hourly atmospheric driving data at 0.05×0.05° based on WRF model simulation over 2000-2016 years for the Heihe River Basin. Plateau Meteorology, 38(1): 206-216
  23. 23.
    Pan X D, Li X, Shi X K, Han X J, Luo L H and Wang L X. 2012. Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China. Frontiers of Earth Science, 6(3): 314-323
  24. 24.
    Qi S Z and Luo F. 2007. Environmental degradation problems in the Heihe River Basin, northwest China. Water and Environment Journal, 21(2): 142-148
  25. 25.
    Song L S, Bian Z J, Kustas W P, Liu S M, Xiao Q, Nieto H, Xu Z W, Yang Y, Xu T R and Han X J. 2020. Estimation of surface heat fluxes using multi-angular observations of radiative surface temperature. Remote Sensing of Environment, 239: 111674
  26. 26.
    Song L S, Kustas W P, Liu S M, Colaizzi P D, Nieto H, Xu Z W, Ma Y F, Li M S, Xu T R, Agam N, Tolk J A and Evett S R. 2016a. Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology, 540: 574-587
  27. 27.
    Song L S, Liu S M, Kustas W P, Nieto H, Sun L, Xu Z W, Skaggs T H, Yang Y, Ma M G, Xu T R, Tang X G and Li Q P. 2018. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale. Remote Sensing of Environment, 219: 72-88
  28. 28.
    Song L S, Liu S M, Kustas W P, Zhou J, Xu Z W, Xia T and Li M S. 2016b. Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures. Agricultural and Forest Meteorology, 230-231: 8-19
  29. 29.
    Song L S, Liu S M, Xu T R, Xu Z W and Ma Y F. 2017. Soil evaporation and vegetation transpiration: Remotely sensed estimation and validation.Journal of Remote sensing, 21(6): 966-981
  30. 30.
    Twine T E, Kustas W P, Norman J M, Cook D R, Houser P R, Meyers T P, Prueger J H, Starks P J and Wesely M L. 2000. Correcting eddy-covariance flux underestimates over a grassland. Agricultural and Forest Meteorology, 103(3): 279-300
  31. 31.
    Wan Z M. 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140: 36-45
  32. 32.
    Wohlfahrt G, Haslwanter A, Hörtnagl L, Jasoni R L, Fenstermaker L F, Arnone III J A and Hammerle A. 2009. On the consequences of the energy imbalance for calculating surface conductance to water vapour. Agricultural and Forest Meteorology, 149(9): 1556-1559
  33. 33.
    Wu B F, Xing Q, Yan N N, Zhu W W and Zhuang Q F. 2015. A linear relationship between temporal multiband MODIS BRDF and aerodynamic roughness in HiWATER wind gradient data. IEEE Geoscience and Remote Sensing Letters, 12(3): 507-511
  34. 34.
    Xu F N, Wang W Z, Wang J M, Huang C L, Qi Y, Li Y and Ren Z G. 2019. Aggregation of area-averaged evapotranspiration over the Ejina Oasis based on a flux matrix and footprint analysis. Journal of Hydrology, 575: 17-30
  35. 35.
    Zhan W F, Chen Y H, Zhou J and Li J. 2011. An algorithm for separating soil and vegetation temperatures with sensors featuring a single thermal channel. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1796-1809
  36. 36.
    Zhang Y，Jia Z Z，Liu S M，Xu Z W，Xu T R，Yao Y J，Ma Y F，Song L S，Li X，Hu X，Wang Z Y，Guo Z X and Zhou J. 2020. Advances in validation of remotely sensed land surface evapotranspiration. Journal of Remote Sensing(Chinese)，24(8): 975-999
  37. 37.
    Zhou S, Yu B F, Zhang Y, Huang Y F and Wang G Q. 2018. Water use efficiency and evapotranspiration partitioning for three typical ecosystems in the Heihe River Basin, northwestern China. Agricultural and Forest Meteorology, 253-254: 261-273
  38. 38.
    Zhu J S, Ren H Z, Ye X, Zeng H, Nie J, Jiang C C and Guo J X. 2021. Ground validation of land surface temperature and surface emissivity from thermal infrared remote sensing data:A review. National Remote Sensing Bulletin, 25(8): 1538-1566

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website