Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems

Dou, Xiankang; Yang, Yongguo

doi:10.1016/j.compag.2018.03.010

Cited by 132 publications

(74 citation statements)

References 82 publications

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“…At present, the majority of regression models are based on vegetation indices (Glenn et al, 2010), such as NDVI and enhanced vegetation index (EVI), because of their simplicity, resilience in the presence of data gaps, utility under a wide range of conditions and connection with vegetation transpiration capacity (Maselli et al, 2014;Nagler et al, 2005;Yuan et al, 2010). As an alternative to statistical regression methods, machine learning algorithms have been gaining increased attention for ET estimation for their ability to capture the complex nonlinear relationships between ET and its controlling factors (Dou and Yang, 2018). Many conventional machine learning algorithms, such as artificial neural networks, random forest, and support vector machine based algorithms have been applied in various ecosystems (Antonopoulos et al, 2016;Chen et al, 2014;Feng et al, 2017;Shrestha and Shukla, 2015) and have proved to be more accurate in estimating ET than simple regression models (Antonopoulos et al, 2016;Chen et al, 2014;Kisi et al, 2015;Shrestha and Shukla, 2015;Tabari et al, 2013).…”

Section: Vi-based Empirical Algorithms and Machine Learning Methodsmentioning

confidence: 99%

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Pan¹,

Pan²,

Tian³

et al. 2019

Preprint

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Abstract. Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p 0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

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Section: Vi-based Empirical Algorithms and Machine Learning Methodsmentioning

confidence: 99%

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Pan¹,

Pan²,

Tian³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…A similar approach was done by Zhang et al [122] in China where they extended the application of remote sensing data in the BPNN and the ANFIS for estimating ET 0 . Further studies were done to include more artificial intelligence models such as the M5 tree model, bagging, random forest [5], ELM [123], and boosted tree [124]. However, the accuracy of these studies was constrained by the quality of the images for retrieving the estimated meteorological data.…”

Section: Ensemble Models For Remote Sensingmentioning

confidence: 99%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

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Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

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“…The traditional machine learning method has been examined, with the goal of improving performance in filling gaps in flux tower observation-based regional ET and potential ET [24][25][26]. As a data-driven method, an artificial neural network has the potential to be used to obtain temporally continuous ET based on limited ET estimates from the Ts-VI triangle model [27].…”

Section: Introductionmentioning

confidence: 99%

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

Cui

Yao

et al. 2020

Remote Sensing

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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.

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Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems

Cited by 132 publications

References 82 publications

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

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

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