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As an indispensable ecological parameter, surface soil moisture (SSM) is of great significance for understanding the growth status of vegetation. The cooperative use of synthetic aperture radar (SAR) and optical data has the advantage of considering both vegetation and underlying soil scattering information, which is suitable for SSM monitoring of vegetation areas. The main purpose of this paper is to establish an inversion approach using Terra-SAR and Landsat-7 data to estimate SSM at three different stages of corn growth in the irrigated area. A combined scattering model that can adequately represent the scattering characteristics of the vegetation coverage area is proposed by modifying the water cloud model (WCM) to reduce the effect of vegetation on the total SAR backscattering. The backscattering from the underlying soil is expressed by an empirical model with good performance in X-band. The modified water cloud model (MWCM) as a function of normalized differential vegetation index (NDVI) considers the contribution of vegetation to the backscattering signal. An inversion technique based on artificial neural network (ANN) is used to invert the combined scattering model for SSM estimation. The inversion method is established and verified using datasets of three different growth stages of corn. Using the proposed method, we estimate the SSM with a correlation coefficient R ≥ 0.72 and root-mean-square error R M S E ≤ 0.043 cm 3 /cm 3 at the emergence stage, with R ≥ 0.87 and R M S E ≤ 0.046 cm 3 /cm 3 at the trefoil stage and with R ≥ 0.70 and R M S E ≤ 0.064 cm 3 /cm 3 at the jointing stage. The results suggest that the method proposed in this paper has operational potential in estimating SSM from Terra-SAR and Landsat-7 data at different stages of early corn growth. [ABSTRACT FROM AUTHOR]

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles >60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds. [ABSTRACT FROM AUTHOR]

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden's j-index ranging from 0.6 to 0.97 depending on flight date and site. [ABSTRACT FROM AUTHOR]

Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002–2020 period are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh corrected TOA reflectances (RC). In each case, a NN architecture consisting of 3 layers of 15 neurons improved performance and data availability compared to current state-of-the-art algorithms used in the region. The NN operating on TOA reflectance outperformed BOA and RC versions. By operating on TOA reflectance data, the NN approach overcomes the common but difficult problem of atmospheric correction in coastal waters. Moreover, the NN provides data for regions which other algorithms often mask out for turbid water or low zenith angle flags. A distinguishing feature of the NN approach is generation of associated product uncertainties based on multiple resampling of the training data set to produce a distribution of values for each pixel, and an example is shown for a coastal time series in the North Sea. The final output of the NN approach consists of a best-estimate image based on medians for each pixel, and a second image representing uncertainty based on standard deviation for each pixel, providing pixel-specific estimates of uncertainty in the final product. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index I SSM , radar incidence angle, normalized difference vegetation index (NDVI) optical index, and the VH/VV ratio. Those approaches are tested using in situ data from the ISMN (International Soil Moisture Network) with observations covering different climatic contexts. The results show an improvement in soil moisture estimations using the hybrid algorithms, in particular the change detection with the neural network one, for which the correlation increases by 54% and 33% with respect to that of the neural network or change detection alone, respectively. [ABSTRACT FROM AUTHOR]

Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf RelativeWater Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%-12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%-33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations. [ABSTRACT FROM AUTHOR]

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R2) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAirTM platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAirTM imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system. [ABSTRACT FROM AUTHOR]

The wave data measured by CFOSAT (China France Oceanography Satellite) have been validated mainly based on numerical model outputs and altimetry products on a global scale. It is still necessary to further calibrate the data for specific regions, e.g., the southern South China Sea. This study analyses the practicability of calibrating the dominant wavelength by using artificial neural networks and mean impact value analysis based on two sets of buoy data with a 2-year observation period and contemporaneous ERA5 reanalysis data. The artificial neural network modeling experiments are repeated 1000 times randomly by Monte Carlo methods to avoid sampling uncertainty. Both experimental results based on the random sampling method and chronological sampling method are performed. Independent buoy observations are used to validate the calibration model. The results show that although there are obvious differences between the CFOSAT wavelength data and the field observations, the parameters observed by the satellite itself can effectively calibrate the data. In addition to the wavelength, nadir significant wave height, nadir wind speed, and the distance between the calibration point and satellite observation point are the most important parameters for the calibration. Accurate data from other sources, such as ERA5, would be helpful to further improve the calibration results. The variable contributing the most to the calibration effect is the mean wave period, which virtually provides relatively accurate wavelength information for the calibration network. These results verify the possibility of synchronous self-calibration for the CFOSAT wavelength data and provide a reference for the further calibration of the satellite products in other regions. [ABSTRACT FROM AUTHOR]

This study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km2 area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topographic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson's correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively. [ABSTRACT FROM AUTHOR]

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate. [ABSTRACT FROM AUTHOR]

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications. [ABSTRACT FROM AUTHOR]

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage. [ABSTRACT FROM AUTHOR]

To meet the sustainable development goals in rocky desertified regions like Guizhou Province in China, we should maximize the crop yield with minimal environmental costs. In this study, we first calculated the yield gap for 6 main crop species in Guizhou Province and evaluated the quantitative relationships between crop yield and influencing variables utilizing ensembled artificial neural networks. We also tested the influence of adjusting the quantity of local fertilization and irrigation on crop production in Guizhou Province. Results showed that the total yield of the selected crops had, on average, reached over 72.5% of the theoretical maximum yield. Increasing irrigation tended to be more consistently effective at increasing crop yield than additional fertilization. Conversely, appropriate reduction of fertilization may even benefit crop yield in some regions, simultaneously resulting in significantly higher fertilization efficiency with lower residuals in the environment. The total positive impact of continuous intensification of irrigation and fertilization on most crop species was limited. Therefore, local stakeholders are advised to consider other agricultural management measures to improve crop yield in this region. [ABSTRACT FROM AUTHOR]

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool. [ABSTRACT FROM AUTHOR]

The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 1016 electron/m2), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia. [ABSTRACT FROM AUTHOR]

We present "The Wall", the first web-based platform that animates the Earth in true natural color and close to real-time. The living planet is displayed both during day and night with a pixel resolution of approximately 1 km and a time frequency of 10 min. The automatic processing chains use the synchronized measurements provided by three geostationary satellites: the METEOSAT Second Generation (MSG2), Himawari-8, and GOES-16. A Rayleigh scattering correction is applied, and a cloud of artificial neural networks, chosen to render "true natural color" RBG composites, is used to recreate the missing daytime bands in the visible spectrum. The reconstruction methodology is validated by means of the TERRA/AQUA "Moderate Resolution Imaging Spectroradiometer" (MODIS) instrument reflectance values. "The Wall" is a dynamic broadcasting platform from which the scientific community and the public can trace local and Earth-wide phenomena and assess their impact on the globe. [ABSTRACT FROM AUTHOR]

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%–35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly and Weddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACETWS) and groundwater storage (GRACEGWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of similar geophysical signals (GRACETWS time series); three clusters were identified (cluster 1: 13,700 km2; cluster 2: 59,200 km2; cluster 3: 33,100 km2; Step I). Variables (total precipitation, normalized difference vegetation index (NDVI), snow cover, streamflow, Lake Michigan level, Lake Huron level, land surface temperature, soil moisture, air temperature, and evapotranspiration (ET)), which could potentially contribute to, or correlate with, GRACETWS over the test site were identified, and the dataset was randomly partitioned into training (80%) and testing (20%) datasets (Step II). Multivariate regression, artificial neural network, and extreme gradient boosting techniques were applied on the training dataset for each of the identified clusters to extract relationships between the identified hydro-climatic variables and GRACETWS solutions on a coarser scale (13,700–33,100 km2), and were used to estimate GRACETWS at a spatial resolution matching that of the fine-scale (0.125° × 0.125° or 120 km2) inputs. The statistical models were evaluated by comparing the observed and modeled GRACETWS values using the R-squared, the Nash–Sutcliffe model efficiency coefficient (NSE), and the normalized root-mean-square error (NRMSE; Step III). Lastly, temporal variations in GRACEGWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACETWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACETWS for local applications given their apparent enhanced performance (average value: R-squared: 0.86; NRMSE 0.37; NSE 0.86) over the multivariate regression (R-squared: 0.74; NRMSE 0.56; NSE 0.64) and artificial neural network (R-squared: 0.76; NRMSE 0.5; NSE 0.37) methods; and (2) identifying local hydrologic variables and the optimum downscaling approach for individual clusters is critical to implementing this method. The adopted method could potentially be used for groundwater management purposes on local scales in the study area and in similar settings elsewhere. [ABSTRACT FROM AUTHOR]