Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be extended to the water balance study of the whole Heihe River basin.

Area-averaged evapotranspiration over a heterogeneous land surface: Aggregation of multi-point EC flux measurements with high-resolution land-cover map and footprint analysis

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models or general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. Based on HiWATER flux matrix datasets and a high-resolution land cover map derived from aircraft remote sensing, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. Firstly, the representativeness of multi-point eddy covariance (EC) flux measurements was quantitatively evaluated. The results show, the model estimated flux values cannot be directly validated with the flux tower measurements because the latent- and sensible heat fluxes measured by EC are determined by the upwind surface flux emanating from separate land cover classes, and a method in retrieving area-averaged fluxes should be applied. Secondly, a flux aggregation method was established combining footprint analysis and multiple regression analysis. The area-averaged sensible heat fluxes were obtained using the method and validated by the large aperture scintillometer (LAS) measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated through the flux aggregation schemes. The aggregated results were then regarded as ground truth for the remotely-sensed ET products. These findings demonstrate that the refined flux integration technique is a better method to determine the heterogeneous surface fluxes.

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

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 R2, 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.

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

Evapotranspiration (ET) plays an important role in surface-atmosphere interactions. Remote sensing has long been identified as a technology that is capable of monitoring ET. However, spatial problems greatly affect the accuracy of ET retrievals by satellite. The objective of this paper is to reduce the spatial-scale uncertainty produced by surface heterogeneity using Chinese HJ-1B data. Two upscaling schemes with area-weighting aggregation for different steps and variables were applied. One scheme is input parameter upscaling (IPUS), which refers to parameter aggregation, and the other is temperature sharpening and flux aggregation (TSFA). Footprint validation results show that TSFA is more accurate and less uncertain than IPUS, and additional analysis shows that TSFA can capture land surface heterogeneities and integrate the effect of overlooked land types in the mixed pixel.

Estimating regional greenhouse gas fluxes: an uncertainty analysis of planetary boundary layer techniques and bottom-up inventories

Quantification of regional greenhouse gas (GHG) fluxes is essential for establishing mitigation strategies and evaluating their effectiveness. Here, we used multiple top-down approaches and multiple trace gas observations at a tall tower to estimate regional-scale GHG fluxes and evaluate the GHG fluxes derived from bottom-up approaches. We first applied the eddy covariance, equilibrium, inverse modeling (CarbonTracker), and flux aggregation methods using 3 years of carbon dioxide (CO2) measurements on a 244 m tall tower in the upper Midwest, USA. We then applied the equilibrium method for estimating CH4 and N2O fluxes with 1-month high-frequency CH4 and N2O gradient measurements on the tall tower and 1-year concentration measurements on a nearby tall tower, and evaluated the uncertainties of this application. The results indicate that (1) the flux aggregation, eddy covariance, the equilibrium method, and the CarbonTracker product all gave similar seasonal patterns of the regional CO2 flux (105−106 km2, but that the equilibrium method underestimated the July CO2 flux by 52–69%. (2) The annual budget varied among these methods from −54 to −131 g C–CO2 m−2 yr−1, indicating a large uncertainty in the annual CO2 flux estimation. (3) The regional CH4 and N2O emissions according to a top-down method were at least 6 and 2 times higher than the emissions from a bottom-up inventory (Emission Database for Global Atmospheric Research), respectively. (4) The global warming potentials of the CH4 and N2O emissions were equal in magnitude to the cooling benefit of the regional CO2 uptake. The regional GHG budget, including both biological and anthropogenic origins, is estimated at 7 ± 160 g CO2 equivalent m−2 yr−1.

Estimating greenhouse gas fluxes from an agriculture-dominated landscape using multiple planetary boundary layer methods

Quantification of regional greenhouse gas (GHG) fluxes is essential for establishing mitigation strategies and evaluating their effectiveness. Here, we used multiple top-down approaches and multiple trace gas observations at a tall tower to estimate GHG regional fluxes and evaluate the GHG fluxes derived from bottom-up approaches. We first applied the eddy covariance, equilibrium, inverse modeling (CarbonTracker), and flux aggregation methods using three years of carbon dioxide (CO2) measurements on a 244 m tall tower in the Upper Midwest, USA. We then applied the equilibrium method for estimating methane (CH4) and nitrous oxide (N2O) fluxes with one-month high-frequency CH4 and N2O gradient measurements on the tall tower and one-year concentration measurements on a nearby tall tower, and evaluated the uncertainties of this application. The results indicate that: (1) the flux aggregation, eddy covariance, the equilibrium method, and the CarbonTracker product all gave similar seasonal patterns of the regional CO2 flux (105–106 km2), but that the equilibrium method underestimated the July CO2 flux by 52–69%. (2) The annual budget varied among these methods from 74 to −131 g C-CO2 m−2 yr−1, indicating a large uncertainty in the annual CO2 flux estimation. (3) The regional CH4 and N2O emissions according to a top-down method were at least six and two times higher than the emissions from a bottom-up inventory (Emission Database for Global Atmospheric Research), respectively. (4) The global warming potentials of the CH4 and N2O emissions were equal in magnitude to the cooling benefit of the regional CO2 uptake. The regional GHG budget, including both biological and anthropogenic origins, is estimated at 7 ± 160 g CO2 eq m−2 yr−1.

Area-Averaged Surface Fluxes in a Semiarid Region with Partly Irrigated Land: Lessons Learned from EFEDA

The European Field Experiment in a Desertification-Threatened Area (EFEDA) provides a comprehensive land surface dataset for a semiarid Mediterranean environment with natural vegetation and cultivated dry and irrigated land. This paper discusses the methods and practical aspects of deriving area-averaged fluxes for a range of areas from the whole EFEDA region to several numerical weather prediction model grid cells (on 10–100-km scales). A time series of grid-scale surface fluxes for the entire observational period of 1 month was obtained from weighted surface averages, using a crop phenology–based land use classification together with a homogenized set of surface observations representative of the four major vegetation classes. The flux-aggregated surface observations were compared with two other approaches to obtain grid-scale fluxes (airborne flux observations and radiosondes in conjunction with a simple mixed-layer model). The area-aggregated fluxes (in particular of latent heat) depend strongly on the location of the area boundaries whenever a significant fraction of irrigated land is present. This result confirms clearly the importance of adequately accounting for tiles of irrigated land in surface schemes and corresponding physiographic databases of large-scale models. A simple way to accommodate for minimum information on the canopy water status is proposed in terms of the distinction of at least two seasonal classes of irrigated crops—one of spring and one of summer growing cycles. The main lesson from this aggregation exercise concerns the role of irrigation. First, this study quantifies the uncertainties in the space–time pattern and its effects on aggregated surface fluxes for the first time on the grounds of observational data. Second, it demonstrates practical ways to accomplish the parameterization of irrigation in flux aggregation schemes, by identifying the key data along with their possible sources and by defining a practical implementation procedure.