Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Remote Sensing

Shihao Ma¹  Zengliang Luo²  Zhaoyuan Yao²  Xi Chen²  Yang Hong²  Wenjie Fan²  Yaokui Cui² 

[1] Carey Business School, Johns Hopkins University, Washington, DC 20036, USA;Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China;

关键词: evapotranspiration;    remote sensing;    LST;    Ts-VI triangle model;    DNN;    arid area;   

DOI  :  10.3390/rs12071121

来源: DOAJ

【 摘 要 】

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d⁻¹, −0.16 mm d⁻¹, and 0.65 mm d⁻¹, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.

【 授权许可】

Unknown