###### Abstract  
As computer and space technologies have developed, geoscience information systems (GIS) and remote sensing (RS) techniques have also been rapidly growing. Recently, the importance of groundwater has grown across the world. The integration of RS and GIS techniques with knowledge of geology has effectively been used to assess groundwater potential and the groundwater pollution problem. We do not doubt that the use of RS and GIS techniques is a powerful tool to study groundwater resources and design suitable exploration plans. This Special Issue aims to create a multidisciplinary forum of discussion for recent advances in the RS and GIS fields for their groundwater applications. Topics of this Special Issue include:  
- Application of RS and GIS techniques in groundwater research  
- Spatial analysis and geocomputation in groundwater research  
- Spatial prediction using machine learning techniques in groundwater potential research  
- Geospatial big data processing and artificial intelligence for groundwater research  
- Geospatial research for groundwater potential and pollution  
- Case studies of groundwater potential and pollution using GIS and RS. In this Special Issue, twenty papers were submitted, and seven highly qualified papers were published. In the papers, the study areas were Iran [1, 2, 3], South Korea [4, 5], Mexico [6] and the United Arab Emirates [7]. The machine learning techniques were used in the five papers [1, 2, 4, 5, 7]. Six of the seven papers presented potential mapping results using RS and GIS techniques. The following summarizes the contents of the papers published in this Special Issue. [PERSON] et al. [1] created a groundwater potentiality (GWP) map over the Damghan sedimentary plain area, which is located in the region of a semi-arid climate of Iran. For this, they used the random forest (RF), binary logistic regression (BLR), a technique for order preference by similarity to ideal solution (TOPSIS), weight-of-evidence (WoE), and support vector machine models (SVM). Ground water parameters were collected from 80 wells, and the data from 56 (70%) wells were used for the training data while the data from 24 (30%) were used for the test data. The performance between the models was estimated by using the area under the curve of the receiver operating characteristic (AUROC). The best performance was calculated from the BLR model, while the worst performance was from the SVM model. [PERSON] et al. [2] presented a novel ensemble approach through integration of the Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR) and random forest (RF) models to produce a GWP map in the Bastam watershed, Iran. The proposed methods were composed of six main steps: (1) data preparation, (2) data assessment, (3) relative importance calculation using groundwater conditioning factors,(4) decision matrix calculation, (5) ground water map generation using the proposed ensemble approach, and (6) performance validation. To evaluate the accuracy of the produced GWP map, the success rate curve (SRC) and prediction rate curve (PRC) were calculated, and the values of SRC and PRC were about 0.925 and 0.934, respectively. This result shows the proposed approach can be used for (1) the management of groundwater resources and (2) the decision-making in groundwater-investment planning to achieve sustainability. [PERSON] et al. [3] (1) developed an automated GIS tool to select absence samples, (2) applied the selected absence samples to the machine learning models including RF and multivariate adaptive regression splines (MARS) to create a groundwater potential map, (3) evaluated the model performance by using the receiver operating characteristic (ROC) curve, and (4) compared between the two model results. The ROC was about 0.931, and the result indicates that the proposed tool can be used to generate an improved groundwater potential map. [PERSON] et al.