###### Abstract
These methods proved to be convenient and have large spatial resolutions; however, the space measurement technologies have low temporal resolution and compromised accuracy compared to ground-based methods. Furthermore, the use of imaging spectroradiometers (MERIS and MODIS) is affected by clouds in addition to their low temporal resolutions [22]. Two distinct Iranian regions with diverse climate conditions were explored to investigate the impact of shifts in meteorological indicators such as water vapor and temperature on groundwater reserves [23]. The application of Sentinel-1A acquisitions, the InSAR technique, and advanced integration methods were implemented to determine meteorological indicators. Synoptic observations, meteorological data, the general circulation model, and the statistical downscaling model (SDSM) were all used to project the indicators. The prediction of groundwater levels was accomplished through a combination of groundwater level observations, water vapor data derived from GPS estimations, and an evapotranspiration index relying on an artificial neural network (ANN) [23]. However, the ANN model's accuracy and reliability are strongly dependent on the quality and amount of accessible data. Also, inadequate or inconsistent geodetic measurements and groundwater level data may further affect the analysis's robustness. Optimal approaches that enhance the downscaling process of the function-based tomography technique sourced from GNSS satellites were suggested [24]. The dependency on Global Navigation Satellite System (GNSS) data and the related hardware infrastructure is one possible downside of adopting the function-based troposphere tomography approach for effective precipitation downscaling. This reliance on GNSS signals could pose challenges in areas prone to signal interference or regions with limited or disrupted satellite signal reception, such as densely forested regions and urban canyons. The precision and reliability of the tomography approach may be affected in such areas, resulting in inaccuracies in the downscaling process. [PERSON] et al. [25] utilized copulas functions to depict the seasonal dependency of precipitation and temperature. Whilst they managed to capture the statistical dependency, they could not completely account for the complex physical mechanisms that drive these interactions in the atmosphere. Furthermore, copulas frequently assume stationarity, which implies that the interactions between variables remain constant across time. In reality, climate change and other variables may cause non-stationarity, which could impact risk prediction accuracy. Geostationary (GEO) satellites such as the Geostationary operational environmental satellites (GOES) have been vital in effectively monitoring and tracking severe weather environments like storms and hurricanes in real time due to their high temporal resolution and ability to repeat observations over a specific area [26]. Nevertheless, for detecting water vapor, their high orbital attitudes contain large amounts of plasma. Additionally, the spatiotemporal interpolation methods used to derive water vapor from GEO satellites are computationally demanding, require sophisticated algorithms, and are likely biased by data anomalies [26]. Table 1 below summaries the accuracy and temporal and spatial resolutions of some of the air, space, and ground-based techniques below. Small satellites technologies such as CubeSats are also increasingly gaining relevance in climate and meteorology studies [27]. As a part of the space precision atomic-clock timing utility mission (SPATIUM), the Kyushu Institute of Technology (Kyutech) launched