Estimation of crop evapotranspiration from MODIS data by combining random forest and trapezoidal models

Pengyu Hao, Liping Di and Liying Guo

Agricultural Water Management, 2022, vol. 259, issue C

Abstract: Evapotranspiration (ET) is an important parameter for crop growth monitoring and land surface modeling. This paper proposed a new workflow, namely ESVEP-RF, to calculate ET during the crop growing season using MODIS data by combining the advantages of the trapezoidal model and Random Forest (RF) algorithm. In ESVEP-RF, the endmember-based soil and vegetation energy partitioning (ESVEP) model was first used to calculate a series of parameters from MODIS and meteorological inputs, and then all parameters derived from remote sensing data, meteorological data and ESVEP models were used as inputs to the RF algorithm for latent heat flux (LE) calculation. In-situ data of 12 years (2003–2012, 2018 and 2019) from five flux towers located in Nebraska (NE) and Michigan (MI) were used to test the performance of ESVEP-RF, and results showed that ESVEP-RF had great potential to accurately calculate ET when the number of training samples was sufficient and representative. In 2010 and 2011, R2 of LE were around 0.8 and RMSE were around 70 W/m2, which outperformed original ESVEP model results. This indicated that the RF algorithm could better describe the non-linear correlation between in LST/FVC space endmembers and LE. Among all parameters, LAI, PLEv and R-vw had high contribution with percentage importance of 18.49%, 15.71% and 13.57%, respectively. Furthermore, all samples between 2003 and 2012 collected from the three NE sites were used to train RF models and then calculate LE for both NE and MI sites in 2018 and 2019. In NE sites, RMSE was around 65 W/m2 and R2 was around 0.8. In MI sites, it was noted that no samples from these sites were included in the training data set, and RMSE was around 70 W/m2 and R2 was higher than 0.7. These results showed the potential of ESVEP-RF for providing up-to-date ET information.

Keywords: ET; Remote sensing; ESVEP; Machine learning; Nebraska (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:259:y:2022:i:c:s0378377421005266

DOI: 10.1016/j.agwat.2021.107249

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