Abstract
To date, little attention has been given to remote sensing-based algorithms for inferring urban surface evapotranspiration. A multi-source parallel model based on ASTER data was one of the first examples, but its accuracy can be improved. We therefore present a modified multi-source parallel model in this study, which has made improvements in parameterization and model accuracy. The new features of our modified model are: (1) a characterization of spectrally heterogeneous urban impervious surfaces using two endmembers (high- and low-albedo urban impervious surface), instead of a single endmember, in linear spectral mixture analysis; (2) inclusion of an algorithm for deriving roughness length for each land surface component in order to better approximate to the actual land surface characteristic; and (3) a novel algorithm for calculating the component net radiant flux with a full consideration of the fraction and the characteristics of each land surface component. HJ-1 and ASTER data from the Chinese city of Hefei were used to test our model’s result with the China–ASEAN ET product. The sensitivity of the model to vegetation and soil fractions was analyzed and the applicability of the model was tested in another built-up area in the central Chinese city of Wuhan. We conclude that our modified model outperforms the initial multi-source parallel model in accuracy. It can obtain the highest accuracy when applied to vegetation-dominated (vegetation proportion > 50%) areas. Sensitivity analysis shows that vegetation and soil fractions are two important parameters that can affect the ET estimation. Our model is applicable to estimate evapotranspiration in other urban areas.
Highlights
Land surface evapotranspiration (ET) is the largest component of the land surface water and energy balance [1]
In order to solve the problem, Zheng [5] first developed a multi-source parallel model in which evapotranspiration of each pixel in a satellite image of urban land surface was inferred based on an energy residual algorithm through combining the transpiration and evaporation of the vegetation component and the soil component that are extracted by linear spectral mixture analysis
A total of 100 validation samples, each consisting of 3 × 3 pixels (90 × 90 m2) of the HJ-1A image, were randomly generated on the Google Earth image, and four surface component fractions of each validation sample were extracted by manual interpretation
Summary
Land surface evapotranspiration (ET) is the largest component of the land surface water and energy balance [1]. As urban land surface energy balance has four main components, i.e., vegetation, soil, impervious surfaces, and water [30], dual-source models only take vegetation and soil into account, and the proportions of vegetation and soil in a mixed pixel are always ignored, and are not suitable for urban areas. In order to solve the problem, Zheng [5] first developed a multi-source parallel model in which evapotranspiration of each pixel in a satellite image of urban land surface was inferred based on an energy residual algorithm through combining the transpiration and evaporation of the vegetation component and the soil component that are extracted by linear spectral mixture analysis. BByy tthhee eennddooff22001155, ,ththeeuurbrabnanpoppouplualtaiotinonofoHf eHfeeifereiarcehaecdhe5d.4584.4m84ilmlioilnli,owni,thwaitnhuarnbaunribzaantiioznatriaotne roaft7e0o.4f %70..4T%hi.sTshuigsgseusgtsgethstast tHhaefteHi hefaesibheacsobmeecoamheigahhlyiguhrlbyaunrizbeadniczietyd. cInityo.rIdnerortodefor ctousfoocnuusrobnanurlbanand lsaunrdfacseurefvaacpeoetvraanpsoptriraantsiopnir,awtieone,xtwraecteexdtrtahceteudrbtahne buurbilat-nupbuairlet-aufproamreHa efrfeoim’s HHJe-f1eAi’ssaHteJl-l1itAe ismataeglleirtey i(mpraegseernyte(dprinesSeenctteidonin.S2e)catsiotnhe2.s2t)uadsytahreeast(uFdigyuarree1a).(FTihgeurseel1e)c.teTdhearseealehcitgehdliagrhetashthigehfloiguhrtms tahine lfaonudr mcoavienrltaynpdescoovfeHr etyfepi,esi.eo.f, iHmepfeeir,vii.eo.u, simsuprefravcieosu, svesguerftaatcieosn, ,vbeagreetastoiioln, a, nbdarwe saoteilr,. and water
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