The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACE_TWS) and groundwater storage (GRACE_GWS) 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 (GRACE_TWS 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, GRACE_TWS 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 GRACE_TWS solutions on a coarser scale (13,700–33,100 km²), and were used to estimate GRACE_TWS 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 GRACE_TWS 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 GRACE_GWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACE_TWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACE_TWS 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.

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km²), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km²). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Opportunistic macroalgal blooms have been used for the assessment of the ecological status of coastal and estuarine areas in Europe. The use of earth observation (EO) data sets to map green algal cover based on a Normalized Difference Vegetation Index (NDVI) was explored. Scenes from Sentinel-2A/B, Landsat-5, and Landsat-8 missions were processed for eight different Irish estuaries of moderate, poor, and bad ecological status using European Union Water Framework Directive (WFD) classification for transitional water bodies. Images acquired during low-tide conditions from 2010 to 2018 within 18 days of field surveys were considered. The estimates of percentage coverage obtained from different EO data sources and field surveys were significantly correlated (R² = 0.94) with Cohen’s kappa coefficient of 0.69 ± 0.13. The results showed that the NDVI technique could be successfully applied to map the coverage of the blooms and to monitor estuarine areas in conjunction with other monitoring activities that involve field sampling and surveys. The combination of wide-spread cloud-coverage and high-tide conditions provided additional constraints during the image selection. The findings showed that both Sentinel-2 and Landsat scenes could be utilized to estimate bloom coverage. Moreover, Landsat, because of its legacy program, can be utilized to reconstruct the blooms using historical archival data. Considering the importance of biomass for understanding the severity of algal accumulations, an artificial neural networks (ANN) model was trained using the in situ historical biomass samples and the combination of radar backscatter (Sentinel-1) and optical reflectance in the visible and near-infrared (NIR) regions (Sentinel-2) to predict the biomass quantity. The ANN model based on multispectral imagery was suitable to estimate biomass quantity (R² = 0.74). The model performance could be improved with the addition of more training samples. The developed methodology can be applied in other areas experiencing macroalgal blooms in a simple, cost-effective, and efficient way. The study has demonstrated that both the NDVI-based technique to map spatial coverage of macroalgal blooms and the ANN-based model to compute biomass have the potential to become an effective complementary tool for monitoring macroalgal blooms where the existing monitoring efforts can leverage the benefits of EO data sets.

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Construction of intensity–duration (ID) thresholds and early-warning and nowcasting systems for landslides (EWNSLs) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of a prototype EWNSL to forecast and nowcast landslides over the Faifa Mountains in the Red Sea Hills. The developed methodologies rely on readily available, temporal, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. The adopted procedures entail the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and generate the Faifa ID threshold (I =4.89D^−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and identify locations where mass movement is likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non-landslide-producing ones and identified the locations of these landslides with an accuracy of 60 %.