*Article* **Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula**

**Jae-Cheol Jang \* , Eun-Ha Sohn, Ki-Hong Park and Soobong Lee**

National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon 27803, Korea; ehsohn@korea.kr (E.-H.S.); parkkihong@korea.kr (K.-H.P.); sblee88@korea.kr (S.L.)

**\*** Correspondence: jaecheol00@korea.kr; Tel.: +82-07-7850-5915

**Abstract:** Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson's correlation coefficient (*R*) of 0.649 mm day−<sup>1</sup> , <sup>−</sup>0.134 mm day−<sup>1</sup> , and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson's *R* of 1.730 mm day−<sup>1</sup> , 1.212 mm day−<sup>1</sup> , and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions.

**Keywords:** evapotranspiration; GK2A/AMI; artificial neural network; Korean Peninsula

## **1. Introduction**

Evapotranspiration (ET) reflects fundamental components of hydrologic and energy cycles of the Earth and is a key element in hydrological resource management [1]. As climate change has progressed, trends in drought and flood have shown different spatial variability, and the importance of hydrological system monitoring has been emphasized [2]. Accordingly, it is fundamental to quantify and monitor ET. However, since water resources are directly affected by regional hydrologic systems and meteorology, ET shows high spatial and temporal variability [3].

A major application of ET is drought monitoring. Climate change has altered drought trends, increasing the intensity, frequency, and extent of droughts [4]. Thereafter, numerous indices for drought monitoring have been developed, with several, such as the standardized precipitation evapotranspiration index [5], precipitation evapotranspiration difference condition index [6], reconnaissance drought index [7], and combined terrestrial evapotranspiration index [8], directly associated with ET. Based on these drought indices, many studies were conducted to investigate the long-term variability of water budget under specific climate change conditions [9], effects of climate elasticity of ET on water balance [10], spatiotemporal variability of drought characteristics [11], and impacts of

**Citation:** Jang, J.-C.; Sohn, E.-H.; Park, K.-H.; Lee, S. Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. *Hydrology* **2021**, *8*, 129. https://doi.org/10.3390/ hydrology8030129

Academic Editors: Aristoteles Tegos and Nikolaos Malamos

Received: 14 July 2021 Accepted: 23 August 2021 Published: 27 August 2021

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drought events on agricultural production [12]. In addition to various applications of ET, the methods to estimate ET with higher accuracy and spatiotemporal resolution have also been studied.

ET can be classified depending on the soil moisture condition. Potential ET (PET) is defined as the water vapor transpired and evaporated from vegetation and soil in unlimited soil moisture conditions [13]. Actual ET (AET) represents the water vapor transferred from a surface under limited soil moisture conditions. Weighing lysimeters are the most accurate AET measuring instruments [14]. Although they measure AET directly, the available AET data are substantially limited for end-users [15]. To ensure the versatility of ET data, various ET estimating models have been developed that can be broadly classified into three types [16]: (1) fully physical combination models that deal with mass and energy transfer principles [17,18]; (2) semi-physical models that account for mass or energy transfer principles and are based on temperature and radiation [19,20]; and (3) black-box models that are based on empirical relationship, artificial neural networks (ANN), fuzzy, and genetic algorithm. Although there are various ET estimating models, the most widely used method is the Penman–Monteith (PM) method [21,22]. The PM method is a fully physical model developed by Penman [17] and later modified by Monteith [18]. This model is recommended as the global reference model for ET monitoring by the Food and Agriculture Organization of the United Nations (FAO).

Although in-situ ET measurements are highly precise, the spatial variability of ET is high, and the availability of in-situ ET measurements is limited [23]. Remotely sensed data have been used to address this problem. Satellite data have broad spatial coverage with high temporal resolution and produce reliable products [24]. MODerate resolution Imaging Spectroradiometer (MODIS) derives the operative ET products with 500 m spatial and 8 days temporal resolution [25]. Several studies have estimated the spatial distribution of ET using low Earth orbit (LEO) satellites with optical-infrared and microwave sensors [26,27]. When calculating ET using LEO satellites, external input data, such as meteorological data, are generally necessary [28]. In particular, because LEO satellites observe the Earth's surface at specific local times, it is difficult for the instantaneous observation to monitor the environmental conditions all day and all weather [29]. Therefore, due to the high temporal variability of ET, LEO satellite-derived ET has inevitable limitations for routine monitoring of daily ET and surface energy fluxes [11,30]. In addition, since LEO satellites apply the physical-based model or energy conservation-based model for estimating ET, there exist uncertainties of external input data for applying the model [25–27]. Using geostationary orbit (GEO) satellites data can compensate for the limitations associated with the temporal resolution of LEO satellite data. However, it is difficult to resolve the uncertainties of external input data and the data contaminated by weather conditions, including clouds and aerosols [29].

The Korean Peninsula is located on the margin of Northeast Asia, bordering the northwest Pacific Ocean (Figure 1a). Since it is located in a monsoon region, where meteorological droughts occur during the summer monsoon, the droughts tend to propagate into agricultural or hydrological droughts [31]. In particular, the Korean Peninsula land cover type showed complex spatial distribution comprising of diverse vegetation cover types (Figure 1b). Furthermore, in the Korean Peninsula, the drought frequency has increased, and drought trends and characteristics vary regionally [32]. The Korean Peninsula has various land cover types and specific terrain properties; these factors make it particularly difficult to monitor daily ET even employing both in-situ measurement and remotely sensed data. Due to frequent cloud cover and rainfall, it is challenging to observe the land surface using optical-infrared satellites in the summer monsoon season [33]. Therefore, to overcome this limitation, numerical model data and ancillary data have been used to retrieve ET [34,35].

**Figure 1.** MODIS land cover from the Annual International Geosphere-Biosphere Programme over (**a**) Northeast Asia and (**b**) the Korean Peninsula in 2019. **Figure 1.** MODIS land cover from the Annual International Geosphere-Biosphere Programme over (**a**) Northeast Asia and (**b**) the Korean Peninsula in 2019.

In order to manage hydrological resources over the Korean Peninsula, Korea Meteorological Administration (KMA) monitors the ET in real-time using in-situ measurements and numerical model data. In-situ measurements exhibit good performance with high temporal resolution every hour; however, its availability is limited due to the point observation. For complementing the limitation of in-situ measurements, KMA calculates the spatial distribution of ET using numerical model data based on geophysical models. Numerical model data-derived ET is suitable for analyzing droughts with a large time scale. In contrast, the accuracy of the ET changes depending on the numerical model data, In order to manage hydrological resources over the Korean Peninsula, Korea Meteorological Administration (KMA) monitors the ET in real-time using in-situ measurements and numerical model data. In-situ measurements exhibit good performance with high temporal resolution every hour; however, its availability is limited due to the point observation. For complementing the limitation of in-situ measurements, KMA calculates the spatial distribution of ET using numerical model data based on geophysical models. Numerical model data-derived ET is suitable for analyzing droughts with a large time scale. In contrast, the accuracy of the ET changes depending on the numerical model data, and it is difficult to calculate the ET that reflects various topographical characteristics of the Korean Peninsula due to the sparse spatial resolution of the numerical model data.

and it is difficult to calculate the ET that reflects various topographical characteristics of the Korean Peninsula due to the sparse spatial resolution of the numerical model data. Although physical-based models show good performance, due to numerous associated meteorological parameters, it is difficult to estimate accurate ET, especially in remote sensing applications. Then, over the last few decades, many researchers have identified that machine learning (ML) approaches were an effective method to overcome the complexity of ET estimation [29]. Because ML techniques solve the non-linear relationship between input and output variables, a lot of ML techniques have been proposed to estimate Although physical-based models show good performance, due to numerous associated meteorological parameters, it is difficult to estimate accurate ET, especially in remote sensing applications. Then, over the last few decades, many researchers have identified that machine learning (ML) approaches were an effective method to overcome the complexity of ET estimation [29]. Because ML techniques solve the non-linear relationship between input and output variables, a lot of ML techniques have been proposed to estimate ET for hydrological applications [36], such as k-nearest neighbors [37], support vector machine [38], random forest [37], and artificial neural network (ANN) [39]. Previously, most studies applied ML approaches to in-situ measurements; however, many recent studies have also applied ML approaches to remote sensing data [40–42].

ET for hydrological applications [36], such as k-nearest neighbors [37], support vector machine [38], random forest [37], and artificial neural network (ANN) [39]. Previously, most studies applied ML approaches to in-situ measurements; however, many recent studies have also applied ML approaches to remote sensing data [40–42]. In this study, considering the spatiotemporal variability in ET, we developed a model that estimates daily PET based on ANN using the GEOstationary Korea Multi-Purpose In this study, considering the spatiotemporal variability in ET, we developed a model that estimates daily PET based on ANN using the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A). The objective was to retrieve real-time daily ET with a spatial resolution of 1 km for hydrological resource monitoring on the Korean Peninsula. To reflect the complex relationships and nonlinearity between the GK2A-derived data and ET, we used precipitation data and the digital elevation data as input data for the ANN. Daily PET from KMA was used as reference data for ANN model training.

SATellite 2A (GEO-KOMPSAT 2A, GK2A). The objective was to retrieve real-time daily

rived data and ET, we used precipitation data and the digital elevation data as input data for the ANN. Daily PET from KMA was used as reference data for ANN model training. The accuracy of the model was verified by comparing modeled data with ET from in-situ measurements of the KMA and National Institute of Forest Science (NIFoS) for the period

excluding the period of training data.

The accuracy of the model was verified by comparing modeled data with ET from in-situ measurements of the KMA and National Institute of Forest Science (NIFoS) for the period excluding the period of training data.

#### **2. Data and Methods**
