Integrating two layers of soil moisture parameters into the MOD16 algorithm to improve evapotranspiration estimations

Abstract

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplier R_sm1 was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30 mm day^–1 and from 1.14 to 0.97 mm day^–1, respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed.

Acknowledgements

The measurements at the nine flux-tower sites were downloaded from the AmeriFlux FTP servers. We are grateful to the researchers at these sites for their effort in making these data available. Information on the LAI and VFC was downloaded from the MODIS collection.

Additional information

Funding

This work was partly supported by the National Natural Science Foundation of China under Grant 41201366, the Institute of Geographic Sciences and Natural Resources Research funding for the outstanding young talent project under grant 2013RC201, the High-resolution Earth-observing Water Application Demonstration under grant 08-Y30B07-9001-13/15-01, the National Natural Science Foundation of China under Grant 41101332, and the International Science & Technology Cooperation Program of China under Grant 2014DFE10220.