Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests-a state of the art machine learning algorithm-to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002-2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R 2 ≈ 0.99 and R 2 ≈ 0.93, respectively) during leave-one-out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root-mean-square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R 2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water-food-energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues.

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible.

The estimation of fresh snow depth (FSD) using X-band synthetic aperture radar (SAR) is feasible but challenging depending on the hydrometeorological conditions and data availability. In this study, the FSD is computed for the Beas river watershed in the northwestern Himalayas near Manali, India. It incorporates the recent copolar phase difference (CPD) based FSD inversion model. Moreover, the TerraSAR-X and TANDEM-X bistatic data acquired in January 2016 are used as inputs to the model along with the snow density measurements at the Dhundi ground station. Additionally, apart from applying layover and forest masks, the potential uncertainty sources in the complex mountainous terrains are identified using the H-A-α decomposition and unsupervised Wishart classification techniques. Furthermore, due to the limited number of weather stations, the results are validated using a 3×3 neighbourhood window surrounding the Dhundi site. Also, the effects of different FSD ensemble window sizes are tested for performing sensitivity analysis.

<p><strong>Abstract.</strong> Accurate monitoring of satellites plays a pivotal role in analysing critical mission specific parameters for estimating orbital position uncertainties. An appropriate database management system (DBMS) at the software end, could prove its potential as a convenient solution over the existing file based two line element (TLE) data structure. The current web-based satellite tracking systems, such as n2yo, satview, and satflare, are unable to provide location-based satellite monitoring. Moreover, the users need to zoom into the displayed world map for obtaining information of the satellites that are currently over the respective area. Also, satellite searching is a cumbersome task in these web-based systems. In this research work, a systematic approach has been utilised to develop a generic open-source Web-GIS based tool for addressing the aforementioned issues. This tool incorporates a PostgreSQL database for storing the parsed TLE data which are freely available on the CelesTrak (NORAD) repository. Our choice of selecting PostgreSQL as a backend DB is primarily due to its open source and scalable properties compared to other resource intensive databases. Using suitable python libraries (e.g. Skyfield and Orbit-Predictor), the position and velocity at any point of time can be accurately estimated. For this purpose, the tool has been tested on several cities for displaying location-based satellite tracking that includes different types of space-objects.</p>

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