The Landsat data archive is frequently touted as the longest continuous space-based record of Earth’s land in existence, and plays an unparalleled role in monitoring and understanding our ever-changing land surface. This is entirely true, but in practical terms, taking advantage of the full Landsat time series is a major challenge. Landsat sensors have been collecting data since 1972, and as you can imagine, technology enhancements over time have incrementally resulted in more precise, richer data. The consequence is varying spatial, spectral, and radiometric resolutions between data collected from different sensors. These differences require some type of data normalization to be applied in order for simple inter-sensor comparisons to be made.
In an effort to leverage the entire 45+ year time series archive, an automated workflow program (LandsatLinkr - LLR) was developed by the Laboratory for Applications of Remote Sensing in Ecology (LARSE) at Oregon State University. The program is a proof-of-concept model for “easily” harmonizing the entire Landsat archive into a congruent time series, producing annual near-cloud-free composite data that can easily be compared across all years from 1972-present. It assembles existing image processing and harmonization methods into an organized system that runs in the R programming environment using a single command followed by an interactive interface to select procedures and provide inputs. Essentially it takes data from Landsat sensors MSS and OLI and makes them match the properties of TM/ETM+ surface reflectance data products. To find out more about the details of the process and the outputs, visit the LandsatLinkr page. An example of its application follows.
Additional examples of LandsatLinkr applications
The top row of Figure 2 shows the MSS TC planes for a Landsat image while the bottom row are the TC planes for the coincident TM image. As you can see, the distribution of pixel values between the images for each TC plane are very similar. The greatest deviation is for TC wetness, which is heavily weighted by short-wave infrared, which MSS data does not contain (is it modeled in the LLR process).
The next example, illustrates how MSS and TM Tasseled Cap brightness, greenness, and wetness, shown respectively as red, green, and blue, exhibit close similarity between the coincident images.
A particular pixel's spectral-temporal chronology, illustrated in Figure 4 (note the smooth, blended transition between sensors and their overlap), shows a forest pixel that is greening up, followed by a major disturbance around 2005-2006 (likely a harvest), and then subsequent vegetation regrowth.