Remote estimation of terrestrial evapotranspiration without using meteorological data

Yuting Yang, Di Long, Songhao Shang

    Research output: Contribution to journalArticle

    55 Citations (Scopus)

    Abstract

    We developed a new method to estimate terrestrial evapotranspiration (ET) from satellite data without using meteorological inputs. By analyzing observations from 20 eddy covariance tower sites across continental North America, we found a strong relationship between monthly gross primary production (GPP) and ET (R2 = 0.72-0.97), implying the potential of using the remotely sensed GPP to invert ET. We therefore adopted the Temperature-Greenness model which calculates 16 day GPP using MODIS EVI and LST products to estimate GPP and then to calculate ET by dividing GPP with ecosystem water use efficiency (the ratio of GPP to ET). The proposed method estimated 16 day ET very well by comparison with tower-based measurements (R2 = 0.84, p < 0.001, n = 1290) and provided better ET estimates than the MODIS ET product. This suggests that routine estimation of ET from satellite remote sensing without using fine-resolution meteorological fields is possible and can be very useful for studying water and carbon cycles. Key Points We proposed a remote sensing ET algorithm without using weather data The proposed method estimates ET well across a wide range of bio-climates The proposed method outperforms MODIS ET product in all validation sites

    Original languageEnglish
    Pages (from-to)3026-3030
    Number of pages5
    JournalGeophysical Research Letters
    Volume40
    Issue number12
    DOIs
    Publication statusPublished - 2013

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