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

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−1, −0.134 mm day−1, 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−1, 1.212 mm day−1, 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.

Convolutional neural network-based recognition method for volleyball movements

“…Finally, the decision maker Softmax is used to classify and output the behavior tags identified by the network. Secondly, the fusion of Crop color map and original color map: detect and extract the key areas of human motion from video frames, add them to training as Crop features, add the fusion of Crop features and video frame features after feature extraction, and finally identify actions through decision-making [ [32] , [33] , [34] ].…”

Advances in Evaporation and Evaporative Demand

“…The paper "Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula" [6] by Jae-Cheol Jang, Eun-Ha Sohn, Ki-Hong Park and Soobong Lee, developed a model that estimates the 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.…”

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

“…For the last few years, the rapid development of machine learning algorithms and neural network technologies and the trend of cross-fertilization of disciplines have also facilitated the introduction of these methodological models into other fields. Nowadays, machine learning models have been applied to the simulation of evapotranspiration in crops, plains, and watersheds [15][16][17][18][19], and the established models for estimating ET have achieved a more satisfactory accuracy. In 2019 Zhao et al presented a machine learning model based on physical constraints to simulate ET on a global scale, and the model can also be applied to extreme weather conditions [20].…”

“…In 2021, Yan Liu et al [21,22] improved the accuracy of the Penman-Monteith equation using artificial neural networks (ANNs) as well as remote sensing vegetation indexes, and also achieved high accuracy in simulating ET from cropland using six machine learning algorithms. Meanwhile, Jang et al [19] pointed out that ANNs can perform local optimization of potential evapotranspiration in the Korean Peninsula more accurately than the MODIS data model. Yue Jia et al [23] concluded that the optimized extreme learning machine (ELM) model has higher simulation accuracy than traditional empirical models (e.g., the Priestley-Taylor model) in the estimation of ET of spring maize in China.…”