What are the advantages and disadvantages of using Landsat and Sentinel data together?

1 Landsat Overview

Landsat is a series of Earth observation satellites operated by NASA and USGS since 1972. It provides continuous and consistent multispectral imagery of the global land surface at 30-meter spatial resolution. Landsat data is freely available and has a long-term archive of more than 40 years. Landsat is ideal for monitoring land use and land cover changes, vegetation dynamics, water resources, and natural disasters.

2 Sentinel Overview

Sentinel is a family of Earth observation satellites developed by the European Space Agency (ESA) as part of the Copernicus program. It consists of six different missions, each with a specific focus and sensor type. Sentinel data is also freely available and has a shorter but more frequent archive since 2014. Sentinel offers higher spatial resolution (up to 10 meters), more spectral bands, and more frequent revisit time than Landsat. Sentinel is suitable for mapping and detecting fine-scale features, such as crops, buildings, roads, and water bodies.

3 Advantages of Using Landsat and Sentinel Together

Using Landsat and Sentinel data together can enhance the remote sensing analysis by providing more information, accuracy, and temporal coverage. Data fusion, gap filling, validation, and diversity are all advantages of combining these two datasets. Data fusion allows for the creation of new products that exploit the strengths of both sensors. Gap filling can help to fill in any missing data from either dataset. Validation and cross-checking between Landsat and Sentinel data also reduces errors and uncertainties in the data processing and analysis. Finally, these datasets can provide different perspectives on the same phenomenon, such as land cover classification, change detection, and anomaly detection.

4 Disadvantages of Using Landsat and Sentinel Together

Using Landsat and Sentinel data together can also present some challenges and limitations that need to be addressed. Data harmonization is one such issue; Landsat and Sentinel data have different spatial, spectral, radiometric, and geometric characteristics that need to be matched before combining them. This process can introduce errors and artifacts. Additionally, the data volume generated by Landsat and Sentinel is large, requiring efficient storage, processing, and analysis. Lastly, data interpretation can vary depending on the application, scale, and context; thus requiring careful evaluation and validation of the data quality, accuracy, and reliability.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?