Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R2 values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions.

NASA’s Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA’s Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.

In situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in situ and GRACE-derived comparisons occur, limited research considers agroecological dependencies. Herein, we examined differences in groundwater monitoring approaches (observed [in situ, O] vs. predicted [GRACE-derived, P]) within the Yazoo–Mississippi Delta (YMD), an agroecosystem in the southeastern USA. We compared variations in modeled groundwater hydrology, land cover, and irrigation dynamics of the YMD within the upper-quartile (UQ) area of interest (AOI) (highest groundwater levels) and lower-quartile (LQ) AOI (lowest groundwater levels) every year from 2008 to 2020. Spatially, OUQ and PUQ were in northern portions of the YMD, with the OLQ and PLQ in southern portions. Groundwater levels between OUQ:PUQ and OLQ:PLQ each had correlations > 0.85. Regarding land cover, most categories varied within ±2.50% between model estimates over time. Relatedly, we documented 14 instances where correlations between land use category and groundwater level were inverted across models (OLQ:PLQ (5), OUQ:OLQ (6), PUQ:PLQ (3)). Irrigation results were not statistically different among all models. Overall, our results highlight the importance of quantifying model incongruences for groundwater and land cover management.

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R2 for each model was 0.88. Large R2 and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.

Stream faunal assemblage structure is tied closely to hydrology and associated physiochemical properties. By altering natural flows, dams and their impoundments impact faunal assemblages over long distances. Although numerous studies have assessed the effect of dams on stream fauna, information is lacking for crayfishes. In this study, we characterized the effects of relatively large storage dams on crayfish assemblage structures. Over 2 years, we sampled three impounded and two unimpounded streams across two drainages in Alabama, United States, to identify biotic and abiotic factors correlated with crayfish assemblage metrics. Compared to impounded streams, unimpounded streams had greater habitat complexity (e.g., aquatic vegetation and woody debris), fewer predator fishes, lower minimum temperatures, and more variable discharges. These characteristics correlated with a higher density and diversity of crayfishes and smaller adults in impounded compared to unimpounded streams. Crayfish species assemblages differed between drainages, as did the biotic and abiotic factors affecting crayfish assemblages in each drainage, suggesting that these factors were species-specific in their effects. Additionally, analysis of land uses suggested that factors other than dams may have also contributed to the observed differences in assemblage structures between impounded and unimpounded streams. For instance, in the more urbanized drainage, crayfish assemblages were more similar between up and downstream sections in all streams, regardless of impoundment. Our results indicate that large dams alter stream crayfish assemblage structure, with potentially cascading effects in trophic and organic matter dynamics both up and downstream.

Life-history timing, trophic interactions, and colonization potential of stream benthos are linked to a stream's flow regime. Modifications to flow regimes often alter the composition of benthic assemblages. We assessed the impacts of relatively large storage dams (>15 m in height, >400-ha impoundments) on crayfish assemblages in southern Appalachian Mountain streams, Alabama, USA. We sampled crayfishes at multiple sites in flowing sections upstream and downstream of dams in 3 impounded streams and along similar lengths of 2 unimpounded streams in the Bear Creek and Cahaba River drainages. We analyzed at 2 scales: within and between drainages. This multi-scale approach allowed us to assess the effects of impoundments on crayfish assemblages in 1 drainage and then assess whether impoundment effects could be generalized across drainages. We compared crayfish assemblages between impounded and unimpounded streams and examined changes in crayfish assemblages with distance from impoundments, considering land-use history as a potential confounding factor. Adult densities averaged 45% higher, and sizes 10% smaller, in downstream sections of unimpounded streams compared with impounded streams. Densities and sizes of adults in upstream sections did not differ between unimpounded and impounded streams. In both drainages, unimpounded stream crayfish assemblage structure gradually shifted along the stream length, with species present upstream differing from those present downstream. Conversely, in all impounded streams, species assemblages did not differ between upstream and downstream sections, illustrating that dams diminish crayfish diversity. Nonetheless, finer-scale assessments showed that crayfish density and richness increased with distance downstream of impoundments. This suggests that assemblage recovery is possible with increasing distance downstream of impoundments. Additionally, we detected fewer crayfish assemblage differences between impounded and unimpounded streams in the Cahaba River drainage than the Bear Creek drainage. In the Cahaba River drainage, urbanization and the presence of an invasive crayfish species in both streams may have hindered our ability to distinguish impoundment effects on crayfishes. Our results show that large dams negatively affect crayfishes and may affect hundreds of crayfish species in thousands of stream km, possibly altering stream systems by interfering with the numerous ecosystem functions influenced by crayfishes.

Globally, precipitation is expected to increase along with the rise of temperatures due to climate change, increasing the likelihood of freshwater intrusion into coastal ecosystems. In the spring and summer of 2019. heavy rainfall and snowmelt in the midwestern United States caused historic flooding of the Mississippi River, warranting two openings of the Bonnet Carre Spillway (BCS) to reduce pressure on levees in New Orleans, LA. These openings released an unprecedented volume of freshwater into Lake Pontchartrain and subsequently into the Mississippi Sound. This study investigated the impacts of these freshwater releases on populations of the eastern oyster Crassostrea virginica and water quality in the western Mississippi Sound and the Bay of St. Louis. Platforms housing oysters and water quality sensors that measured dissolved oxygen, salinity, and temperature were deployed at oyster reef sites. At each reef, native oyster populations were assessed via dredge sampling to determine oyster survival. After 13 days, deployed oysters suffered 100% mortality at all sites except the Henderson Point Reef and Kittiwake Reef, the sites farthest east of the BCS. On September 27, 2019, 62 days following the BCS closing, dredge sampling revealed no live native adult oysters or spat, even at sites where living oysters were collected earlier in the summer. If BCS openings increase in frequency or duration because of elevated precipitation, oyster populations in Mississippi could remain unsustainable for harvesting unless future freshwater intrusions are incorporated into management planning.

The current remote sensing systems designed to measure soil moisture have a relatively coarse spatial resolution ranging from 25 to 50 km. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a passive sensor that measures soil moisture through C-band (6.9 and 7.3 GHz) observation of brightness temperature (BT). AMSR2 uses land parameter retrieval model to retrieve surface soil moisture and vegetation optical depth. This model partitions the microwave observation into its respective soil and vegetation emission components. AMSR2 loses sensitivity to soil moisture as vegetation density increases, during the growing season. Field observations show that AMSR2 tends to overestimate the soil moisture when the vegetation intensity increases and covers the soil. We address two existing issues in the use of soil moisture products of the AMSR2: (1) spatial resolution of the soil moisture product and (2) the impact of vegetation cover on the radiative transfer. We used a vegetative index to estimate when soil moisture retrieval is not sufficiently accurate and how the optical data can be used to improve soil moisture estimation. The land surface temperature and vegetation index products of the Visible Infrared Imaging Radiometer Suite are used to downscale the AMSR2 soil moisture products to 1 km. A series of soil moisture data collected in the field were used to analyze the accuracy of the downscaled soil moisture values and the results indicated that introduction of the BT in the downscaling model improves the accuracy of the soil moisture products over the vegetated areas. The mean absolute error (MAE) of the downscaled soil moisture values is similar to 5.6%-an improvement to 8.0% of original AMSR2 soil moisture products. In the presence of vegetation, however, the MAE is still greater than that of a similar analysis of AMSR2 product over semiarid areas. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

The Upper Jurassic (Oxfordian Age) Smackover Formation is a significant source for hydrocarbon production in southwest Alabama. Brooklyn Field is in southeast Conecuh County, Alabama, and has been a major producer of oil and natural gas for the state. The Smackover is a carbonate formation that has been divided into seven distinct lithofacies in the Brooklyn and Little Cedar Creek fields. In southwest Alabama, the facies distribution in the Smackover Formation was influenced by paleotopography of the underlying Paleozoic rocks of the Appalachian system. The goal of this study is to determine elemental ratios in rock core within the Smackover Formation using an X-ray fluorescence (XRF) handheld scanner and to correlate these elemental characteristics to the lithofacies of the Smackover Formation in the Brooklyn and Little Cedar Creek fields. Eight wells were used for the study within Brooklyn Field and Little Cedar Creek fields. Cores from the eight wells were scanned at six-inch intervals. Chemical logs were produced to show elemental weights in relation to depth and lithofacies. The chemical signatures within producing zones were correlated to reservoir lithofacies and porosity. Aluminum, silicon, calcium, titanium, and iron were the most significant (>95% confidence level) predictors of porosity and may be related to the depositional environment and subsequent diageneses of the producing facies. The XRF data suggests relative enrichments in iron, titanium, and potassium. These elements may be related to deposition in relatively restricted marine waters.

Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site.

Given the significance of natural and built assets of the Gulf of Mexico region, the three states of Alabama, Louisiana, and Mississippi, leveraged their unique partnerships, proximity, and significant prior investments in cyberinfrastructure (CI) to develop the Northern Gulf Coastal Hazards Collaboratory (NG-CHC). This collaboratory was established to catalyze collaborative research via enhanced CI to reduce the regions vulnerability to natural and human disasters by facilitating high performance modeling to test hypotheses focused on engineering design, coastal system response, and risk management of coastal hazards. The objective of the NG-CHC is to advance research and inspire collaboration through highly available innovation-enabling CI, with a particular focus on geosciences and engineering from the watershed to the coast. An integrated CI capable of simulating all relevant interacting processes is needed to implement a system that captures the dynamic nature of coastal surface processes. The NG-CHC has implemented CI to locate appropriate data and computational resources, create necessary workflows associated with different simulation demands, and provide visualization tools for analysis of results. Three simulation management systems, SIMULOCEAN, SULIS, and ASGS, were implemented, each with a defined suite of hypotheses and institutional participants to run collaborator experiments. The NG-CHC focused on developing suites of CI tools centered on handling the functional needs of each simulation management system in a collaborative environment. The NG-CHC also developed curriculum units, computer games and simulations to extend the knowledge of coastal hazards to students from middle school to college. Education and outreach activities were developed to increase public understanding and support for sustainable coastal practices. The elements of the CI tool box within NG-CHC describe generic tools needed to promote a 'collaborative modeling environment' in other coastal systems.

The Kauth–Thomas (K–T) transform, also known as the Tasseled-Cap (T-C) transform, is a widely used metric capable of capturing scene characteristics in related coordinate directions in a defined feature space. There is a perceived apprehension to use the transform because the derivation methods are diverse, and concern that coefficients are only useful for the images from which they were derived. We address these concerns and outline a workflow for future derivations. First, we ensure greater consistency of future K–T transform derivations by creating an empirical approach to identify scenes most favorable for derivations. This approach can be applied to large data archives and provide a “first-cut” criterion for selecting vegetation land cover dominated scenes. The algorithm provides a quantified basis to exclude imagery from a K–T transform derivation because of ill-suited land cover. This empirically derived relationship can be used to sort large imagery archives for vegetation land cover dominated scenes. This “first-cut” method still requires the need for an analyst, but the work load is greatly reduced.Each K–T transform matrix is sensor and data-model specific. For every sensor and data type (e.g., radiance, reflectance, DN), a new transform matrix must be derived. As an example of using the work flow we derive the DN-based Landsat 7 ETM+ K–T transform coefficients and show the resulting tasseled-cap space. The proposed process uses the first two dominant eigenvalues which are the roots of the characteristic equations solving the variance–covariance matrix of the spectral data. We have discovered that these quantities are discriminative of the land cover type contained in the scene. Not only can these specific types eigenvalues be used to select appropriate scenes for the K–T transform derivation but can also be included in the image header as a useful parameter in imagery database inquiries. Like cloud cover and image quality flags, the ln-lambda1 [ln(λ1)] and ln-lambda2 [ln(λ2)] values provide general information about the variability and land cover of the scene.

Shallow surficial failures or levee slides in the Mississippi River levee system are very common. There is currently no system to identify or predict the location of these slides before they occur. Studies of slide occurrence mechanisms suggest that probable slide-affected areas are characterized by anomalous vegetation. Compact Airborne Spectrographic Imager II (CASI II) imagery was analysed for selected levee sites in association with slide inventory data and field observations. Normalized Difference Vegetation Index (NDVI), Red edge Vegetation Stress Index (RVSI) and Red Edge Position Index (REP) were calculated from the acquired CASI II imagery. The vegetation indices were used to locate the stressed or anomalous vegetation and predict levee slides. The statistical significance of the predictors was determined by logistic regression. All predictors were found statistically significant for developing a slide prediction model. The slide prediction model was developed by combining the single predictors, categorized vegetation indices, into a model based on all three predictors. Percentage of Search Area Reduction (PSAR) and Failure Index (FI) were used to evaluate the performance of the slide prediction model. Evaluation of the model performance shows that it achieves a maximum FI of 0.43 and PSAR of 99.5.

The utilization of evapotranspiration (ET) estimates, derived from satellite remote sensing, into the Annualized Agricultural Non-Point Source (AnnAGNPS) pollution model was investigated. Modifications within AnnAGNPS were performed to allow the internal calculations of ET based on climate parameters, collected from ground-based measurements, to be replaced by ET estimates produced with the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Direct comparison of these sources of ET indicated that AnnAGNPS calculated ET estimates were 35% higher than the MODIS-calculated ET estimates resulting in a 10% increase in runoff at the outlet of the Yalobusha River Watershed for 2004. Simulated monthly runoff values based on MODIS and AnnAGNPS ET estimates for the year 2004 were compared with observed values from stream gages indicating slightly better agreement from the simulation using MODIS-derived ET. Analysis of the spatial distribution of the percent difference in runoff between AnnAGNPS-based ET and MODIS-based ET simulations showed higher differences at the most downstream and upstream of the watershed. These differences could be attributed to different soil properties or to the effect of distant field/weather station assigned to these locations. Although these findings should not be generalized to watersheds located in different ecosystems, the results indicate that the utilization of remotely sensed ET in hydrological modelling is feasible and that use of MODIS to estimate ET provided a more comprehensive spatial coverage than the often available through ground-based measurements. Copyright (C) 2010 John Wiley & Sons, Ltd.

Automated and semi-automated techniques have been researched as an alternative way to reduce human interaction and thus improve the information extraction process from imagery. This research developed an innovative methodology by integrating machine learning algorithms with image processing and remote sensing procedures to form the evolutionary framework. In this biologically-inspired methodology, non-linear solutions are developed by iteratively updating a set of candidate solutions through operations such as: reproduction, competition, and selection. Uncertainty analysis is conducted to quantitatively assess the system's variability due to the random generation of the initial set of candidate solutions, from which the algorithm begins. A new convergence approach is proposed and results indicate that it not only reduces the overall variability of the system but also the number of iterations needed to obtain the optimal solution. Additionally, the evolutionary framework is evaluated in solving different remote sensing problems, such as: non-linear inverse modeling, integration of image texture with spectral information, and multitemporal feature extraction. The investigations in this research revealed that the use of evolutionary computation to solve remote sensing problems is feasible. Results also indicate that, the evolutionary framework reduces the overall dimensionality of the data by removing redundant information while generating robust solutions regardless of the variations in the statistics and the distribution of the data. Thus, signifying that the proposed framework is capable of mathematically incorporating the non-linear relationship between features into the final solution.

Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimization engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalization capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R 2. The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R 2 exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.

Information extraction from high spatial resolution imagery is sometimes hampered by the limited number of spectral channels available from these systems. Standard supervised classification algorithms found in commercial software packages may misclassify different features with similar spectral characteristics; leading to a high occurrence of false positives. An additional step in the information extraction process was developed incorporating the concept of object geometry. Objects are defined as a contiguous group of pixels identified as belonging to a single class in the spectral classification. Using results from the spectral classification, a supervised approach was developed using genetic programming to select and mathematically combine feature-specific shape descriptors from a larger set of shape descriptors, to form a new classifier. This investigation focused on extraction of residential housing from QuickBird and IKONOS imagery of the Mississippi Gulf Coast before and immediately after hurricane Katrina. Use of genetic programming significantly reduced false positives caused by asphalt pavement and isolated roofing material scattered throughout the image.