Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation.

[1]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[2]  G. Senay,et al.  Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin , 2015 .

[3]  G. Senay,et al.  A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET , 2013 .

[4]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[5]  Soe W. Myint,et al.  Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area, Arizona , 2016 .

[6]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[7]  Habib N. Najm,et al.  Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics , 2009 .

[8]  Masoud Karbasi.  Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model , 2017, Water Resources Management.

[9]  Keith Beven,et al.  A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates , 1979 .

[10]  X. Lee,et al.  A mechanistic investigation of the oasis effect in the Zhangye cropland in semiarid western China , 2020, Journal of Arid Environments.

[11]  T. Sauer,et al.  Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard , 2019, Agronomy.

[12]  Stefano Tarantola,et al.  A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output , 1999, Technometrics.

[13]  Liangxu Wang,et al.  The Heihe Integrated Observatory Network: A Basin‐Scale Land Surface Processes Observatory in China , 2018 .

[14]  Jiemin Wang,et al.  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 , 2016 .

[15]  A. Langousis,et al.  A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources , 2019, Water.

[16]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[17]  Du Zheng,et al.  Radiation calibration of FAO56 Penman-Monteith model to estimate reference crop evapotranspiration in China , 2008 .

[18]  Y. Ge,et al.  Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces , 2016 .

[19]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[20]  Ziwei Xu,et al.  Micrometeorological Methods to Determine Evapotranspiration , 2019, Observation and Measurement of Ecohydrological Processes.

[21]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[22]  Jiemin Wang,et al.  Aggregation of area-averaged evapotranspiration over the Ejina Oasis based on a flux matrix and footprint analysis , 2019, Journal of Hydrology.

[23]  Li Jia,et al.  Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations , 2015, Remote. Sens..

[24]  Xin Li,et al.  Upscaling research in HiWATER: Progress and prospects , 2016 .

[25]  L. Rui,et al.  Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin , 2020 .

[26]  Qing Xiao,et al.  Advances in quantitative remote sensing product validation: Overview and current status , 2019, Earth-Science Reviews.

[27]  Lisheng Song,et al.  Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data , 2018, Remote Sensing of Environment.

[28]  Qianyi Zhao,et al.  Upscaling Sensible Heat Fluxes With Area-to-Area Regression Kriging , 2015, IEEE Geoscience and Remote Sensing Letters.

[29]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[30]  Chunlin Huang,et al.  High resolution surface radiation products for studies of regional energy, hydrologic and ecological processes over Heihe river basin, northwest China , 2016 .

[31]  Markus Reichstein,et al.  Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product , 2018, Earth System Science Data.

[32]  Ji Zhou,et al.  Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network , 2020 .

[33]  Bo Zhong,et al.  Land cover mapping using time series HJ-1/CCD data , 2014, Science China Earth Sciences.

[34]  Yan Jin,et al.  Principles and methods of scaling geospatial Earth science data , 2019, Earth-Science Reviews.

[35]  Yong Ge,et al.  Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging , 2015 .

[36]  W. Kustas,et al.  Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale , 2018, Remote Sensing of Environment.

[37]  Günther Heinemann,et al.  Comparison of methods for area‐averaging surface energy fluxes over heterogeneous land surfaces using high‐resolution non‐hydrostatic simulations , 2005 .

[38]  Chunlin Huang,et al.  Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter , 2008 .

[39]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms , 2015 .

[40]  Mohammad Ali Gholami Sefidkouhi,et al.  Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments , 2017, Applied Water Science.

[41]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[42]  Simon J. Hook,et al.  Validation of Land Surface Temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements , 2014 .

[43]  N. S. Raghuwanshi,et al.  Artificial neural networks approach in evapotranspiration modeling: a review , 2010, Irrigation Science.

[44]  Anita Simic Milas,et al.  Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices , 2015 .

[45]  Ali Rahimikhoob,et al.  Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment , 2014, Water Resources Management.

[46]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[47]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[48]  Youngryel Ryu,et al.  Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS) , 2016 .

[49]  S. Liang,et al.  A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes , 2015 .

[50]  Gorka Landeras,et al.  Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain) , 2008 .

[51]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[52]  S. M. Bateni,et al.  Mapping regional evapotranspiration in cloudy skies via variational assimilation of all-weather land surface temperature observations , 2020 .

[53]  Shunlin Liang,et al.  Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Nathaniel A. Brunsell,et al.  Validating remotely sensed land surface fluxes in heterogeneous terrain with large aperture scintillometry , 2011 .

[55]  Thomas Foken,et al.  Area-Averaged Surface Fluxes Over the Litfass Region Based on Eddy-Covariance Measurements , 2006 .

[56]  Zhixia Guo,et al.  Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States , 2019, Journal of Hydrology.