## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.2 Ground-Based Soil Moisture Measurements  
In situ SM measurements play a pivotal role in the processing, calibration, and validation of geospatially explicit data obtained from remote sensing and models [22]. The major repository of SM data comes from the ISM N, which is a worldwide consortium that integrates and standardizes SM data from various in situ monitoring stations. It offers openly accessible, advanced quality control methods, data essential for the validation of satellite-derived SM estimates, reanalysis of climate models, and support of extensive hydrological and meteorological research [21]. As of April 2024, the ISMN contains data from 80 networks and 3147 stations located all over the globe, with data from 1952 to the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Spatial Resolution** & **Temporal Availability** & & **Advantages** & **Limitations** \\ \hline SMAP (Soil Moisture & \({}^{9}\) km (SMAP14, 13, SSM, 13, SSM, 13, SSM) & Daily and 3 h & – & Highly accuracy & – & Sensitivity to dense vegetation \\ Active Passive) & 36 km (13, SSM) & Daily and 3 h & – & Effective in vegetative and bare soils (L band) & – & Complex data processing required \\  & & & – & High spatial resolution & – & Limited spatial resolution for local applications \\  & & & – & Vegetation penetration & – & Temporal resolution \\  & & & in specific modes) & – & Uniformly for SM and topography (C band) & – & Regions of the world \\  & & & – & Good vegetation & – & Coarse spatial resolution \\  & & & – & Can measure SM under moderate vegetation & – & Sensitivity to high levels of vegetation and rough surfaces \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of satellite dataset for soil moisture measurement and monitoring. present ([[https://ismn.earth/](https://ismn.earth/)]([https://ismn.earth/](https://ismn.earth/)), accessed on 8 April 2024). Nevertheless, the global in situ SM measurement network is unevenly distributed across the world, with the highest station density concentrated in the arid and semi-arid zones. The ISMN is particularly sparse in the tropics, southern hemisphere, and boreal regions. In our review, we found many examples of using ground-based SM measurements and produced geospatial products, for example, ([PERSON] et al., (2023) [19] developed a new global data set of soil surface moisture (GSSM1 km) at a high spatial resolution (1 km) and temporal (daily), covering the years 2000 to 2020. They used a Random Forest (RF) Model combining meteorological and biophysical co-variable predictors of SM derived from satellite and reanalysis data sets. Predictor variables or co-variables included antecedent precipitation evaporation index (APEI), LST, and various vegetation indices such as NDVI and EVI. [PERSON] et al., (2023) [60] developed a global surface soil SM model with a daily resolution of 1 km gap-free from 2000 to 2020. They combined the SSM product of the European Space Agency--Climate Change Initiative (ESA-CCI) and ERA5 reanalysis data. They also applied the RF algorithm to disaggregate the coarse-resolution SM into 1 km, with the help of in situ observations from the ISMN and other optical remote sensing datasets. [PERSON] et al., (2021) [20] developed a global, long-term dataset of soil moisture (SoMo.ml), using Machine Learning (ML) models trained on in situ measurements. This dataset extrapolates daily SM dynamics both spatially and temporally, utilizing data from over 1000 global stations. SoMo.ml provides SM data at multiple depths (0-10 cm, 10-30 cm, and 30-50 cm) with a spatial resolution of 0.25\({}^{\circ}\) and daily temporal resolution, covering the period from 2000 to 2019. In the US, the National Integrated Drought Information System (NIDIS) is leading the effort to establish the National Coordinated Soil Moisture Monitoring Network (NCSMMN).