AI-Based Water Quality Prediction for Inland Water using Satellite and Land Surface Model Data

The Portable L-Band Radiometer, mounted on a drone, enables high-resolution soil moisture retrieval across diverse terrains. Operating at 1.4 GHz (L-band), it collects brightness temperature (TB) data over rice paddies and varied vegetation zones, complementing satellite missions like SMAP and SMOS. By integrating airborne observations with ground-based sensors, the system enhances spatial coverage and improves soil moisture retrieval models for climate and agricultural applications. This drone-based approach significantly increases data accuracy and efficiency compared to traditional methods.

Soil moisture (SM) is vital for hydrometeorology and agriculture, yet its retrieval faces challenges. Satellite-based methods (e.g., SMAP, SMOS) offer global coverage but have resolution limitations, model-based methods lack accuracy in extreme events, and ground-based observations provide precision but limited coverage. To improve satellite SM validation, this project establishes Korea’s first Core Validation Site (CVS) in Hampyeong-gun and Naju-si, deploying TEROS 12-6 sensors across 9 km, 3 km, and 1 km grids for continuous SM and temperature monitoring. The ESA ELBARA-III L-Band Radiometer and drone-mounted PoLRa will collect brightness temperature (TB) data, aiding SM retrieval model development with soil properties and climate data. This CVS will enhance climate resilience studies, SM monitoring, and satellite validation in Korea.

Climate change-driven heatwaves and water cycle disruptions threaten inland water quality (WQ), necessitating efficient monitoring. Traditional methods are labor-intensive with limited coverage, prompting us to develop an AI-based model for predicting chlorophyll-a concentrations in lakes and rivers. By integrating high-resolution satellite imagery (Landsat-8/9, Sentinel-2/3) with land surface models (ERA5-Land, GLDAS, MERRA-2), our model tailors predictions to different water bodies. Future plans include incorporating socio-statistical data (e.g., population, livestock) and climate scenarios (RCP, SSP). Using AI, GIS, and high-performance computing, we explore low-concentration chlorophyll-a prediction, precipitation and flow speed impacts on WQ, transfer learning, multi-sensor data fusion, and uncertainty quantification. Beyond chlorophyll-a, we aim to extend predictions to turbidity and dissolved oxygen, providing a comprehensive AI-driven approach to monitoring and mitigating climate change effects on inland water quality.

The application of deep learning in predicting flash droughts offers a transformative approach to understanding and anticipating these rapid-onset events, significantly enhancing preparedness and response strategies. By unraveling the complex mechanisms behind flash droughts, this project aims to provide precise, timely forecasts, thereby mitigating the severe agricultural, ecological, and socioeconomic impacts associated with these phenomena.

Streamflow and flash drought predictions are essential for managing water resources and mitigating potential disasters in ungaged regions. With remotely-sensed data, deep and transfer learning approaches provide powerful tools to analyze complex hydrological data, enabling more accurate predictions and better decision-making in these areas.

Bayesian methods help us improve our guesses by using new information. In Earth science, these methods are applied to big data to better understand our planet. This approach is useful for predicting things like natural disaster patterns and climate changes. By continuously updating our knowledge with new data, we can make more accurate predictions and decisions in Earth science.

The water balance equation in Earth science, P = E + R + etc, describes the relationship between precipitation (P), evaporation (E), runoff (R), and etc (e.g., soil moisture, ground water) in a given area. Bayesian inference can be applied to solve this equation by incorporating prior knowledge and updating the probability distributions of the variables based on new data, ultimately improving water resource management and prediction.

Earth science informs infrastructure development by providing insights into site suitability, resource management, and sustainable design, enhancing the resilience and long-term viability of projects. It also plays a crucial role in addressing societal justice related to climate change by helping identify vulnerable communities and develop mitigation strategies, ensuring equitable access to resources and protection from environmental hazards.

Data assimilation is vital in earth science as it integrates diverse observations and model simulations, improving the accuracy of forecasts and predictions. This process enhances our understanding of complex Earth systems, enabling better decision-making for environmental management and climate adaptation.

Satellite data and machine learning transformed Earth science by predicting and monitoring natural disasters. This combination delivers precise and timely predictions, crucial for mitigating the impacts of events like floods and droughts.

Characterizing the error of satellite data and land surface models is vital in Earth science, as it ensures the accuracy and reliability of information used for monitoring and predicting environmental phenomena. By understanding these errors, scientists can refine data interpretation, enhance models, and ultimately make better-informed decisions about the Earth's complex systems.

Enhancing the temporal repeat of satellite data for obtaining soil moisture information is a vital research area due to its implications for agriculture, water resource management, climate change research, and ecosystem health. It helps in making informed decisions, increasing productivity, and reducing the impact of natural disasters, as well as contributing to our understanding of the global climate system.

Humans have been modifying the Earth's surface for thousands of years, with practices like clearing forests for agriculture and creating uniform land covers. But how do these changes impact the subdaily global terrestrial water cycle? That's the question a project aims to answer.

Microwave soil moisture data is critical for agriculture, weather, and climate modeling, but has low spatial resolution. Disaggregation via machine learning can improve resolution, offering detailed local soil moisture data. Machine learning can handle complex relationships between microwave signals and soil moisture.