###### Abstract  
Soil moisture (SM) plays a crucial role in land-atmosphere interaction systems, directly influencing evapotranspiration, photosynthesis, and the water dynamics of the soil surface. Invariably, SM is negatively impacted by disturbances such as fires, which are becoming more frequent across semi-arid ecosystems. Different ecological restoration activities have been implemented to mitigate the impacts of disturbance that, when left untreated, can worsen the effects of recurrent droughs and accelerate desertification and land degradation processes. To measure and monitor the dynamics of SM, advanced techniques and tools have been developed that integrate remote sensing and in situ measurement. This review encompasses various themes on the application of remote sensing for measuring and monitoring SM dynamics in semi-arid ecosystems at different scales. We focused our analysis on the western United States region and thus have developed a review on the following topics: (a) the different data sources (e.g., satellite, unmanned aerial vehicles), (b) approaches to measure field-based SM, and (c) algorithms and techniques to model SM at different scales. We summarize these topics by emphasizing repeatable approaches for the transparent estimation of this variable, identifying current data gaps, and highlighting future trends to fulfill the expanding demand for SM monitoring strategies. soil moisture; semi-arid ecosystems; remote sensing technologies 202020147677[[https://doi.org/10.3390/app1417677](https://doi.org/10.3390/app1417677)]([https://doi.org/10.3390/app1417677](https://doi.org/10.3390/app1417677)) +
Footnote †: journal: Applied and  
1A. Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools  
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European Space Agency's (ESA) missions and is the first to provide global observations of variability in SM and sea surface salinity [8]. The Advanced Microwave Scanning Radiometer (AMSR2), launched in 2012 from the Japan Aerospace Exploration Agency (JAXA), is a passive sensor that measures daily global SM and brightness temperature (BT) through the C-band [9]. Remote sensing with unmanned aerial vehicles (UAVs) offers the option of capturing data with high temporal and spatial resolutions. This approach is increasingly employed to develop precision agriculture and efficient SM monitoring [10]. Different sensors can be used on a UAV, such as thermal infrared sensors [11], red, green, and blue (RGB) imaging [12], light detection and ranging (LiDAR) [13], multispectral (MS) [14], and hyperspectral [15] and microwave sensors (L-band passive microwave) [16]. Using UAVs for measuring and monitoring SM compliments data obtained with ground and satellite sensors and offers wider opportunities to capture higher spatial resolution datasets with a temporal frequency that is limited only by weather conditions. Numerous portable devices equipped with sensors facilitate in situ SM measurement, providing accuracy, speed, reliability, simplicity, and non-destructive sampling. Commonly utilized methods include tensiometers and granular matrix sensors for direct measurement, as well as soil water content sensors like capacitance-based sensors that use the soil charge storing capacity to calibrate to water content [17], the frequency domain reflectometry (FDR) and time domain reflectometry (TDR). Both FDR and TDR are indirect techniques that estimate SM by measuring the electrical conductivity of pore water [18]. It is clear then that SM monitoring can occur at different dimensions (i.e., spatial, temporal, spectral), and our review here is an attempt to summarize these dimensions in research that has been conducted exclusively on semi-arid lands. Modeling SM over large- and small-scale areas must consider appropriate statistical techniques to evaluate relationships with predictor covariates. Over the past decade, deep learning (DL) and machine learning (ML) algorithms have significantly advanced the field of SM analytics.