气候变化领域集成服务门户(CCPortal): Temporal interpolation of land surface fluxes derived from remote sensing

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DOI	10.5194/hess-24-3643-2020
	Temporal interpolation of land surface fluxes derived from remote sensing - Results with an unmanned aerial system
	Wang S.; Garcia M.; Ibrom A.; Bauer-Gottwein P.
发表日期	2020
ISSN	1027-5606
起始页码	3643
结束页码	3661
卷号	24 期号:7
英文摘要	Remote sensing imagery can provide snapshots of rapidly changing land surface variables, e.g. evapotranspiration (ET), land surface temperature (Ts), net radiation (Rn), soil moisture (-), and gross primary productivity (GPP), for the time of sensor overpass. However, discontinuous data acquisitions limit the applicability of remote sensing for water resources and ecosystem management. Methods to interpolate between remote sensing snapshot data and to upscale them from an instantaneous to a daily timescale are needed. We developed a dynamic soil-vegetation-atmosphere transfer model to interpolate land surface state variables that change rapidly between remote sensing observations. The "Soil-Vegetation, Energy, water, and CO2 traNsfer" (SVEN) model, which combines the snapshot version of the remote sensing Priestley-Taylor Jet Propulsion Laboratory ET model and light use efficiency GPP models, now incorporates a dynamic component for the ground heat flux based on the "force-restore" method and a water balance "bucket" model to estimate and canopy wetness at a half-hourly time step. A case study was conducted to demonstrate the method using optical and thermal data from an unmanned aerial system at a willow plantation flux site (Risoe, Denmark). Based on model parameter calibration with the snapshots of land surface variables at the time of flight, SVEN interpolated UAS-based snapshots to continuous records of Ts, Rn , ET, and GPP for the 2016 growing season with forcing from continuous climatic data and the normalized difference vegetation index (NDVI). Validation with eddy covariance and other in situ observations indicates that SVEN can estimate daily land surface fluxes between remote sensing acquisitions with normalized root mean square deviations of the simulated daily Ts, Rn , LE, and GPP of 11.77 %, 6.65 %, 19.53 %, 14.77 %, and 12.97% respectively. In this deciduous tree plantation, this study demonstrates that temporally sparse optical and thermal remote sensing observations can be used to calibrate soil and vegetation parameters of a simple land surface modelling scheme to estimate "lowpersistence" or rapidly changing land surface variables with the use of few forcing variables. This approach can also be applied with remotely-sensed data from other platforms to fill temporal gaps, e.g. cloud-induced data gaps in satellite observations. © 2020 Copernicus GmbH. All rights reserved.
语种	英语
scopus关键词	Antennas; Forestry; Heat flux; Information management; Land surface temperature; Soil moisture; Surface measurement; Vegetation; Water resources; Gross primary productivity; Jet Propulsion Laboratory; Land surface modelling; Normalized difference vegetation index; Root mean square deviations; Soil-vegetation-atmosphere transfer models; Temporal interpolation; Unmanned aerial systems; Remote sensing; deciduous tree; eddy covariance; growing season; heat flux; interpolation; land surface; NDVI; remote sensing; satellite data; satellite imagery; soil-vegetation interaction; unmanned vehicle; Denmark; Salix arbusculoides
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159346
作者单位	Wang, S., Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; Garcia, M., Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; Ibrom, A., Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; Bauer-Gottwein, P., Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark
推荐引用方式 GB/T 7714	Wang S.,Garcia M.,Ibrom A.,et al. Temporal interpolation of land surface fluxes derived from remote sensing - Results with an unmanned aerial system[J],2020,24(7).
APA	Wang S.,Garcia M.,Ibrom A.,&Bauer-Gottwein P..(2020).Temporal interpolation of land surface fluxes derived from remote sensing - Results with an unmanned aerial system.Hydrology and Earth System Sciences,24(7).
MLA	Wang S.,et al."Temporal interpolation of land surface fluxes derived from remote sensing - Results with an unmanned aerial system".Hydrology and Earth System Sciences 24.7(2020).