Abstract
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.
Highlights
Evapotranspiration (ET) is a critical component of the water cycle and water balance because it links a number of ecological and hydrological processes [1]
land surface temperature (LST)-based ET models are confronted with difficulties in estimating temporally continuous daily actual ET
We developed a gap-filling algorithm using deep neural network (DNN) for the temperature-vegetation index (Ts-VI) triangle model to obtain temporally continuous daily actual ET in an arid area of China
Summary
Evapotranspiration (ET) is a critical component of the water cycle and water balance because it links a number of ecological and hydrological processes [1]. As a result of increased irrigation, drinking water demands, and urban water usage, groundwater levels have decreased significantly through over-pumping [5,6,7] To improve both water use efficiency and the level of water resource management to balance these different water demands, it is necessary to obtain temporally continuous daily actual ET data to more accurately calculate total water consumption. The surface temperature-vegetation index (Ts-VI) model is a typical remotely sensed land surface temperature (LST)-based ET model [8]. It depends primarily on LST and is presently most applicable for providing direct, accurate estimates of ET in arid and semi-arid areas o regional scales. Previous studies have shown that the accuracy of the Ts-VI triangle model is approximately 1 mm d−1 or less [15,16]
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