浏览全部资源
扫码关注微信
Published： 07 August 2021 ，
扫 描 看 全 文
薛可嘉，何苗，卞尊健，宋立生，徐艳豪，蒋浩.2021.Sentinel-3卫星双角度热红外数据的流域尺度地表蒸散发估算与验证.遥感学报，25（8）： 1683-1699
Xue K J，Bian Z J，He M，Song L S，Xu Y H and Jiang H. 2021. Estimating instantaneous evapotranspiration based on dual-angle thermal infrared observations from Sentinel-3 satellite. National Remote Sensing Bulletin， 25（8）：1683-1699
双层能量平衡模型TSEB（Two Source Energy Balance）常用于地表蒸散发的估算研究，对于干旱半干旱流域的水资源调控以及利用有着十分重要的意义。本文在流域尺度上分别利用Sentinel-3双角度观测热红外和近红外数据反演的地表温度LST（Land Surfacer Temperature）和地表组分温度LSCT（Land Surface Component Temperatures）分别驱动TSEB模型，估算了黑河流域不同土地覆盖类型下的瞬时地表通量，并利用流域内上、中、下游通量塔地面观测数据验证了两种模型估算的地表通量。结果表明：两种模型都在一定程度上高估了净辐射和潜热通量，在植被稀疏的高寒草甸以及河岸林土地覆被类型下，TSEB-PT模型表现更好，随着植被覆盖度的上升，TSEB-2T模型的优势更加明显。研究结果为黑河流域水资源合理利用提供一定的保障以及为水资源管理者提供流域尺度上更加准确的植被水分利用的估计值，并且为后期模型的优化提供了理论基础。
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/m
2
and from 60 to 130 W/m
2
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.
陆表温度（TSEB-PT）组分温度（TSEB-2T）地表通量真实性检验
land surface temperature (TSEB-PT)component temperature (TSEB-2T)surface fluxvalidation
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 [DOI: 10.1016/j.rse.2011.08.025http://dx.doi.org/10.1016/j.rse.2011.08.025]
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 [DOI: 10.1109/jstars.2020.3024190http://dx.doi.org/10.1109/jstars.2020.3024190]
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 [DOI: 10.1016/j.rse.2012.06.009http://dx.doi.org/10.1016/j.rse.2012.06.009]
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 [DOI: 10.5194/essd-11-1483-2019http://dx.doi.org/10.5194/essd-11-1483-2019]
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 [DOI: 10.1016/S0168-1923(01)00221-0http://dx.doi.org/10.1016/S0168-1923(01)00221-0]
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 [DOI: 10.1016/j.advwatres.2012.06.004http://dx.doi.org/10.1016/j.advwatres.2012.06.004]
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 [DOI: 10.1016/S0034-4257(01)00293-0http://dx.doi.org/10.1016/S0034-4257(01)00293-0]
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 [DOI: 10.1016/j.rse.2014.11.003http://dx.doi.org/10.1016/j.rse.2014.11.003]
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 [DOI: 10.1016/j.rse.2019.111419http://dx.doi.org/10.1016/j.rse.2019.111419]
Jönsson P and Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845 [DOI: 10.1016/j.cageo.2004.05.006http://dx.doi.org/10.1016/j.cageo.2004.05.006]
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 [DOI: 10.1007/s10712-008-9037-zhttp://dx.doi.org/10.1007/s10712-008-9037-z]
Kustas W and Anderson M. 2009. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology, 149(12): 2071-2081 [DOI: 10.1016/j.agrformet.2009.05.016http://dx.doi.org/10.1016/j.agrformet.2009.05.016]
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 [DOI: 10.1016/0168-1923(90)90033-3http://dx.doi.org/10.1016/0168-1923(90)90033-3]
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 [DOI:10.1016/S0168-1923(99)00005-2http://dx.doi.org/10.1016/S0168-1923(99)00005-2]
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 [DOI: 10.1175/bams-d-12-00154.1http://dx.doi.org/10.1175/bams-d-12-00154.1]
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 [DOI: 10.1006/jare.2000.0715http://dx.doi.org/10.1006/jare.2000.0715]
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
刘萌, 唐荣林,李 召良, 高懋芳, 姚云军. 2021. 数据驱动的蒸散发遥感反演方法及产品研究进展. 遥感学报, 25(8): 1517-1537 [DOI: 10.11834/jrs.20211310http://dx.doi.org/10.11834/jrs.20211310]
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 [DOI: 10.2136/vzj2018.04.0072http://dx.doi.org/10.2136/vzj2018.04.0072]
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 [DOI: 10.5194/hess-15-1291-2011http://dx.doi.org/10.5194/hess-15-1291-2011]
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 [DOI: 10.1016/0168-1923(95)02265-Yhttp://dx.doi.org/10.1016/0168-1923(95)02265-Y]
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 [DOI: 10.3724/SP.J.1226.2011.00344http://dx.doi.org/10.3724/SP.J.1226.2011.00344]
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
潘小多, 马瀚青. 2019. 2000-2016年基于WRF模式的0.05°×0.05°黑河流域近地表大气驱动数据. 高原气象, 38(1): 206-216 [DOI: 10.7522/j.issn.1000-0534.2018.00062http://dx.doi.org/10.7522/j.issn.1000-0534.2018.00062]
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 [DOI: 10.1007/s11707-012-0306-2http://dx.doi.org/10.1007/s11707-012-0306-2]
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 [DOI: 10.1111/j.1747-6593.2006.00059.xhttp://dx.doi.org/10.1111/j.1747-6593.2006.00059.x]
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 [DOI: 10.1016/j.rse.2020.111674http://dx.doi.org/10.1016/j.rse.2020.111674]
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 [DOI: 10.1016/j.jhydrol.2016.06.034http://dx.doi.org/10.1016/j.jhydrol.2016.06.034]
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 [DOI: 10.1016/j.rse.2018.10.002http://dx.doi.org/10.1016/j.rse.2018.10.002]
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 [DOI: 10.1016/j.agrformet.2016.01.005http://dx.doi.org/10.1016/j.agrformet.2016.01.005]
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
宋立生, 刘绍民, 徐同仁, 徐自为, 马燕飞. 2017. 土壤蒸发和植被蒸腾遥感估算与验证.遥感学报, 21(6): 966-981
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 [DOI: 10.1016/S0168-1923(00)00123-4http://dx.doi.org/10.1016/S0168-1923(00)00123-4]
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 [DOI: 10.1016/j.rse.2013.08.027http://dx.doi.org/10.1016/j.rse.2013.08.027]
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 [DOI: 10.1016/j.agrformet.2009.03.015http://dx.doi.org/10.1016/j.agrformet.2009.03.015]
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 [DOI: 10.1109/lgrs.2014.2348074http://dx.doi.org/10.1109/lgrs.2014.2348074]
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 [DOI: 10.1016/j.jhydrol.2019.05.011http://dx.doi.org/10.1016/j.jhydrol.2019.05.011]
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 [DOI: 10.1109/tgrs.2010.2082555http://dx.doi.org/10.1109/tgrs.2010.2082555]
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
张圆,贾贞贞,刘绍民,徐自为,徐同仁,姚云军,马燕飞,宋立生,李相,胡骁,王泽宇， 郭枝虾， 周纪.2020.遥感估算地表蒸散发真实性检验研究进展.遥感学报,24(8): 975-999 [DOI：10.11834/jrs.20209099http://dx.doi.org/10.11834/jrs.20209099]
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 [DOI: 10.1016/j.agrformet.2018.02.002http://dx.doi.org/10.1016/j.agrformet.2018.02.002]
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
朱金顺,任华忠,叶昕, 曾晖, 聂婧, 蒋晨琛, 郭金鑫. 2021. 热红外遥感地表温度与发射率地面验证进展. 遥感学报, 25(8): 1538-1566 [DOI: 10.11834/jrs.20211299http://dx.doi.org/10.11834/jrs.20211299]
相关文章
相关作者
相关机构
京公网安备11010802024621