###### Abstract
[4] created a groundwater productivity potential (GPP) map over Okcheon city, South Korea using the boosted regression tree (BRT), RF, and logistic regression (LR) models. They exploited the relation between the groundwater-productivity such as specific capacity (SPC) and transmissivity (T) and the land-cover map, topographic map, lineament map, geology, etc. The land-cover map and topographic map were created from satellite images. They collected groundwater productivity data from 86 wells. The 50% productivity data were used as the training data, and the other 50% of the data were used as the test data. The model parameters of BRT, RF and LR were estimated by using the training data. The existence of GPP was determined by T or SPC. The input neurons in the models were composed of 18 hydrogeology-related factors. They created three GPP maps from the T value by using the three models and produced three GPP maps from the SPC value by using the models. Then, they evaluated the model performance of the GPP maps. When the T value was used for the models, the model accuracy was about 81.7%, 80.2%, 85.0% in BRT, RF and LR, respectively, while the model accuracy was approximately 81.5%, 78.6% and 82.22% in BRT, RF and LR, respectively, when the SPC value was used. In the GPP map creation in Okcheon city, Korea, the LR model was best, and the model parameter estimation from the T value was better than the SPC value. [PERSON] et al. [5] created and analyzed a GPP map over Gangneung-si, South Korea using a support vector regression (SVR) model from the hydraulic dataset of transmissivity. They collected the transmissivity data from the 285 wells, and then the 285 data were divided into training and test data at a ratio of 70% vs. 30%. The models used for this study were SVR, SVR with grey wolf optimization (GWO), and SVR with particle swarm optimization (PSO). The area under curve (AUC) value was used to evaluate the model performance. The AUC values estimated from SVR, SVR with GWO, SVR with PSO were about 0.803, 0.878 and 0.814, respectively. The created GPP maps were used for the groundwater development and management in Gangneung-si, South Korea. [PERSON] et al. [6] proposed a new method for dolines mapping, and they evaluate the performance of the proposed method in the study area, which is located in the Yucatan Peninsula. The Yucatan Peninsula is an interesting karst region having unique features such as \"Cenotes\", which is an open doline exposing water, and a semicircular doline alignment. The proposed method is a semiautomatic multi-depth threshold approach (MDTA). They mapped a total of 665 dolines using the proposed method and the detection accuracy and precision of the proposed method were about 85% and 71%, respectively. [PERSON] et al. [7] mapped and monitored the land use/land cover (LULC) changes over the Northern United Arab Emirates (NUAE) to spatially analyze the level and quality of the groundwater. The LULC change maps with the accuracy of more than 0.9 were created by using the SVM machine learning model. They created four enhanced LULC maps from the periods of 1990-2000, 2000-2010, 2010-2018, and 1990-2018. In the study area, the built-up area increased approximately from 227 km\({}^{2}\) to 870 km\({}^{2}\), while the vegetation area increased approximately from 77 km\({}^{2}\) to 291 km\({}^{2}\). The observed LULC changes are closely related to the groundwater level and quality in the NUAE. We hope that the research topics of the seven published papers will help and be widely used in the ground applications based on the RS and GIS techniques. Conceptualization, H.-S.J. and S.L. ; writing--original draft preparation, H.-S.J. ; writing--review and editing, H.-S.J. and S.L. ; funding acquisition, H.-S.J. and S.L. All authors have read and agreed to the published version of the manuscript.