Salem Ibrahim Salem - Research Themes

My research focuses on remote sensing and machine learning for water resources and environment.

Theme 1: Machine Learning Applications in Environmental Monitoring

Summary

Environmental monitoring is vital in assessing the impact of human activities and natural processes on ecosystems. Traditional methods like field observations and satellite imagery, while useful in collecting environmental data such as water quality and land use changes, have limitations. Field observations often lack extensive spatial coverage and require significant effort, while satellite data, offering broader spatial and temporal coverage, sometimes fail to capture environmental complexity and involve substantial data processing challenges.

Incorporating machine learning (ML) into environmental monitoring marks a significant advancement in overcoming these challenges. ML provides robust tools for analyzing complex, large datasets, yielding more accurate and efficient predictions and insights. This technology enables the identification of patterns and trends that are not easily discernible through conventional methods, thereby facilitating better-informed decision-making in environmental conservation and policy.

Objectives

To develop machine learning algorithms that can effectively process and analyze large-scale environmental datasets from diverse sources.
To enhance the accuracy of environmental monitoring systems using machine learning, enabling more effective management and conservation strategies.

Methods

The approach includes collecting a comprehensive range of environmental data, both in-situ and via satellite. This data will be used to train and validate machine learning models capable of analyzing complex environmental phenomena. The methods will involve data preprocessing, feature selection, and the development of custom ML algorithms suited for specific environmental parameters. The goal is to create models that are not only accurate but also adaptable to different environmental contexts and scalable for widespread application.

Theme 2: Monitoring and Prediction of Harmful Algal Blooms

Summary

Harmful Algal Blooms (HABs) have emerged as a critical environmental challenge, significantly impacting water quality globally. These blooms, characterized by the excessive growth of algae, vary widely in species, including cyanobacteria (blue-green algae), and dinoflagellates and diatoms (often associated with red tides). HABs can be categorized into two types: one where the algae themselves are toxic, and another where massive blooms deplete oxygen or clog fish gills. The variability and complexity of these species make predicting and managing HABs a daunting task. HABs are not only ecological threats but also cause substantial economic losses, such as the fisheries damage in Hokkaido, Japan, which exceeded 1 billion yen. Globally, similar economic impacts are reported annually.

The monitoring of these blooms is feasible through ocean color observation, but there remains a lack of comprehensive, accurate global models for HAB monitoring and prediction. This gap highlights the need for advanced research and innovative methodologies in this field.

Objectives

To develop precise predictive models for HAB occurrences, accounting for the diverse species and their unique impacts.
To integrate satellite-based ocean color observation with in-situ data for comprehensive HAB monitoring.
To advance the understanding of HAB dynamics, facilitating effective management strategies.

Methods

This theme will focus on utilizing both in-situ and satellite data, including ocean color remote sensing and ancillary data like chlorophyll-a and sea surface temperature. Advanced machine learning models will be created to analyze these data sets, improving our ability to predict and monitor HABs accurately. This holistic approach is aimed at overcoming the challenges in HAB prediction and providing reliable tools for environmental management and economic loss mitigation.

Theme 3: Atmospheric Correction and In-Water Optical Properties

Summary

Atmospheric correction and retrieval of in-water optical properties in water bodies, is a critical process. Atmospheric correction aims to remove scattering effects caused by gases and aerosols to accurately obtain Earth's reflectance. This process is more complex for inland and coastal waters due to their intricate optical properties compared to open oceans. Retrieving in-water optical properties such as chlorophyll-a concentration, turbidity, and dissolved organic matter is vital for assessing water quality and ecosystem health.

Advancements in remote sensing technologies have made it possible to observe these complex water bodies with greater accuracy. However, developing precise algorithms for atmospheric correction and in-water property retrieval remains a significant challenge due to the diverse and dynamic nature of these environments.

Objectives

To refine and evaluate atmospheric correction methodologies for more accurate satellite data interpretation in inland and nearshore coastal waters.
To develop robust algorithms for the retrieval of in-water optical properties, enhancing the monitoring of water quality and aquatic ecosystems.
To bridge the gap between satellite data and actual water quality parameters through innovative remote sensing techniques.

Methods

This theme will involve the collection of in-situ data and the utilization of satellite imagery to develop and validate algorithms for atmospheric correction and in-water property retrieval. Methods will include the analysis of spectral signatures, algorithm development for specific water types, and the integration of machine learning techniques to improve accuracy. These efforts aim to produce reliable tools for environmental monitoring, providing essential data for managing and preserving aquatic ecosystems.

Theme 4: Calibration/Validation of Earth-observation missions

Summary

Calibration and validation (Cal/Val) are pivotal in maintaining the accuracy and reliability of Earth-observation mission. These processes involve aligning satellite sensor data with established reference data to correct discrepancies. Inter-sensor comparison, a key component of Cal/Val, ensures data consistency across different sensors, addressing potential biases and enhancing data comparability. Vicarious calibration, using ground-based or airborne measurements, is vital for validating satellite data, especially in remote sensing for oceanography and land monitoring.

Objectives

Ensure long-term consistency and accuracy of satellite sensors through rigorous Cal/Val processes.
Improve the reliability of satellite data for scientific research and applications.
Enhance inter-sensor comparisons to facilitate integrated data usage from multiple satellite sources.

Methods

Our approach includes collecting in-situ measurements from buoys, NASA AERONET, and other reliable sources for ground truth data. These are pivotal in calibrating and validating satellite observations. The process encompasses thorough data analysis, statistical comparisons, and algorithm development for accurate satellite data alignment. For inter-sensor comparison, methods involve analyzing data from different sensors under similar conditions to identify and correct any discrepancies, ensuring the integration of satellite data is seamless and reliable. This comprehensive methodology is essential for the successful application of remote sensing data across various environmental and scientific domains.

Theme 5: Monitoring and Predictive Modeling of Oceanic Carbon Dynamics

Summary

The ocean plays a vital role in the Earth's carbon cycle, primarily through the biological and physical carbon pumps, which are crucial in regulating atmospheric CO2 levels. The biological carbon pump involves phytoplankton absorbing CO2 and converting it into organic carbon. Some of this organic carbon is transferred to deeper ocean layers, effectively sequestering carbon. The physical pump, influenced by water temperature and salinity, facilitates the absorption of CO2 in colder waters, contributing further to carbon sequestration. Understanding the ocean's role in carbon capture and storage is essential, especially considering the increasing atmospheric CO2 levels and resultant ocean acidification.

Recent studies emphasize the importance of monitoring and modeling organic carbon in the oceans, particularly Dissolved Organic Carbon (DOC), Dissolved Inorganic Carbon (DIC), and Particulate Organic Carbon (POC). These components are interconnected in the aquatic carbon cycle, with CO2 playing a central role. However, observing these parameters poses challenges due to their variability and the complexities of oceanic systems. The lack of comprehensive models for accurately monitoring and predicting these dynamics highlights a significant gap in our understanding of oceanic carbon processes.

Objectives

To develop robust models for monitoring and predicting oceanic carbon dynamics, focusing on DOC, DIC, and POC.
To advance our understanding of the biological and physical carbon pumps in sequestering atmospheric CO2.
To bridge the observational gaps in oceanic carbon processes using innovative remote sensing and machine learning techniques.

Methods

The approach will involve collecting in-situ data and satellite observations to analyze oceanic carbon components. Machine learning models will be developed for predictive analysis, and land-ocean coupled modeling will be utilized to understand and forecast oceanic carbon dynamics. This integrated methodology aims to provide a comprehensive understanding of the ocean's role in the global carbon cycle and its impact on climate change.

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