## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics
#### 3.2.2 Ground-Based Soil Moisture Measurements
NIDIS is working with the US Department of Agriculture (USDA) and other partners to integrate SM data from around the country, and the principal goals is to establish a national \"network of networks\" that effectively demonstrates data and operational coordination of in situ network to weather monitoring, climate, agricultural and forest/ecological monitoring, remote sensing and model validation and flood forecasting [68]. Most long-term SM monitoring networks are operated by federal and state agencies, and this network is expanding. As of the writing of this paper in June 2024 there are more than 2100 stations that are currently in operation and are made up of 29 networks from different agencies ([[http://nationalsoilmoisture.com](http://nationalsoilmoisture.com)]([http://nationalsoilmoisture.com](http://nationalsoilmoisture.com)), accessed on 17 April 2024). This expansion is part of the National Soil Moisture Network (NSMN). Particularly in Oklahoma, the SM monitoring network has expanded and stations are part of an environmental monitoring network called Mesonet. Of all the networks that make up the National Coordinated Soil Moisture Monitoring Network (NCSMMND), the main ones are the Soil Climate Analysis Network (SCAN), the Snow Telemetry Network (SNOTEL), and the US Climate Reference Network (USCRN). In situ networks for SM monitoring, while extensive, do not cover all regions of the United States, much less the entire world. To address these coverage gaps, researchers employ computer models that integrate extensive data sets, including readings of precipitation, temperature, humidity, vegetation, and topography. These models simulate SM dynamics in areas lacking direct observation data, thus providing a comprehensive global perspective on SM trends. One notable example of these applications is the research carried out by [PERSON], et al., (2021) [62], where they developed SMAP-HydroBlocks. This is a satellite-based surface SM dataset with an unprecedented spatial resolution of 30 m for the contiguous United States. This data set was created by combining HydroBlocks model [69] with a Tau-Omega Radiative Transfer Model, using microwave data from the SMAP satellite and in situ observations. For the analysis and validation of the SMAP-HydroBlocks data set, they used more than a thousand observation sites from the Snow Telemetry (SNOTEL) Network. [PERSON] et al., (2021) [70] evaluated long-term SM trends from 18 general circulation models that are part of the Coupled Model Intercomparison Project Phase Six (CMIP6) and in situ observations over continental United States (CONUS), including 303 in situ  
In situ measurements are essential for the calibration and validation of remote sensing data and models, providing precise information for SM assessment, which is crucial for ecological restoration and sustainable land management in semi-arid lands. Table 2 organizes and highlights the importance of in situ measurements in monitoring SM dynamics in semi-arid lands, including a detailed comparative analysis of the main in situ SM monitoring networks.