The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

The live storage of Pakistan’s major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R² = 0.85, P_BIAS = 11.2%, NSE = 0.84) and validation (R² = 0.88, P_BIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R² = 0.88, P_BIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield’s absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management.