Data Error Characterizations

March 30, 2023

Error characterization in Earth science ensures accurate satellite data, improved models, and informed decision-making

Characterizing the error of satellite data in Earth science is essential for maintaining the integrity and usefulness of the data collected from space-based observations. It allows researchers to identify and quantify uncertainties, leading to improved data processing, more accurate models, and increased confidence in findings related to climate change, natural disasters, and other critical Earth system processes. This understanding of error ultimately supports better decision-making and more effective management of our planet's resources and environment.

  1. Climate Change: Characterizing data uncertainty helps scientists better understand climate trends and projections, informing policies to mitigate and adapt to global warming.
  2. Natural Disaster Prediction: Accurate uncertainty assessments enable more reliable predictions of events like hurricanes, earthquakes, or floods, supporting effective disaster preparedness and response strategies.
  3. Agriculture and Water Management: By understanding data uncertainty, decision-makers can optimize crop management, irrigation, and water allocation to ensure sustainable food production and water resources.

There is high uncertainty in one of the satellite-based soil moisture products (indicated in red)

What we aim to achieve

By correctly characterizing errors in data, we aim to achieve greater accuracy and reliability in their findings, improve the quality of models and predictions, and enhance the validity of scientific conclusions. This enables more effective decision-making, evidence-based policy recommendations, and a better understanding of complex systems and phenomena.

Data and analytic skills we use for this project

To characterize errors in data, we employ various methodologies that ensure accuracy and reliability. Some of these methods include:

  1. In-situ data comparison: By comparing satellite measurements with ground-based or in-situ observations,  we can identify discrepancies and quantify biases or uncertainties in the remote sensing and model data. This approach is particularly useful for calibration and validation of satellite- and model-derived data products.
  2. Triple collocation analysis: This method involves the simultaneous comparison of three independent datasets, often with different sources of errors, to estimate the uncertainty and error structure in each dataset. The analysis allows researchers to assess the quality of the data and identify potential error sources without relying on a single reference dataset.
  3. Intercomparison of data: We compare multiple datasets generated by different instruments, algorithms, or models to identify systematic differences and uncertainties. By analyzing the variations and biases among the datasets, we can better understand the sources of errors and improve data quality.

These methodologies, when used individually or in combination, allow us to characterize errors effectively and enhance the accuracy and reliability of the data used in Earth science and other fields.

This project aims not only to characterize the errors in satellite and model products but also to explore other related research topics below. If you are interested in any of the following research areas, please do not hesitate to contact me!
  1. Developing advanced machine learning algorithms for detecting and correcting systematic errors in satellite imagery, particularly in the context of land cover classification and atmospheric composition measurements.
  2. Comparing and harmonizing data from different satellite missions to create a more comprehensive and accurate global database for various Earth science parameters, such as soil moisture or sea surface temperature.
  3. Assessing the propagation of uncertainties in Earth system models by quantifying the influence of input data errors on the model output and identifying the most critical sources of uncertainty.
  4. Developing innovative techniques to combine in-situ and satellite observations to enhance the accuracy and spatial resolution of essential climate variables, such as precipitation and evapotranspiration.
  5. Analyzing the impact of extreme events, such as volcanic eruptions or wildfires, on satellite data quality and determining methods to correct for these disturbances in the data.
  6. Evaluating the performance of satellite-based remote sensing algorithms in challenging environments, such as polar regions, arid areas, or complex urban landscapes, to improve error characterization and data quality in these areas.
  7. Designing new data assimilation techniques that explicitly account for error characteristics in both observations and models to improve the accuracy of weather forecasts and climate projections.
  8. Exploring the potential of emerging technologies, such as small satellite constellations and high-resolution sensors, to reduce errors and uncertainties in Earth observation data and enhance the monitoring of dynamic processes, such as land-use change or coastal erosion.

Read other projects

Harnessing Deep Learning to Predict and Decode the Mysteries of Flash Droughts (GAN/SHAP/3D-CNN with Transfer Learning)

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.

Read this project
Streamflow and Drought Predictions over Ungaged Regions using Deep and Transfer Learning Approaches

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.

Read this project
Applications of Bayesian Machine Learning in Big Data in Earth Science

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.

Read this project
Water Balance Budgeting with Bayesian Machine Learning

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.

Read this project
Integrating Earth Science and Engineering for Climate Resilience: Innovative Approaches to Infrastructure and Societal Justice

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.

Read this project
Enhancing Earth Science Predictions through Advanced Data Assimilation Techniques

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.

Read this project
Floods and Droughts Predictions using Machine Learning Approaches

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.

Read this project
Data Error Characterizations

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.

Read this project
Developing Algorithms to Improve the Temporal Sampling of Satellite Data

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.

Read this project
Exploring the Impact of Human Activities on the Subdaily Global Terrestrial Water Cycle

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.

Read this project
Satellite Image Disaggregation with Machine Learning

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.

Read this project