Empirically Reconstructing Biophysics with Remote Sensing Data

Post by Bethany Blakely graduate student with Adrian Rocha and Jason McLachlan at the University of Notre Dame

Biophysics is important . . . but only recently!

The exchange of energy between the land surface and the atmosphere (biophysics) plays a huge role in local and global climate. The polar ice-albedo feedback, where snowmelt reduces albedo and further accelerates melting, is an example that most readers will be familiar with. Vegetation is an important mediator of biophysical change (Figure 1). Changes in stature, phenology, water use, and other vegetation characteristics alter the exchange of energy between a vegetated surface and the surrounding atmosphere. Despite the potential climatic importance of these effects, they were often neglected in early efforts to understand vegetation-climate interactions. One contributor to the relative neglect of biophysics in past climate science may be its temporospatial misalignment with vegetation processes. Surface-atmosphere energy exchanges happen rapidly and at a local scale, making them hard to rectify with annual to centennial changes in landscape and climate.

Figure 1

Figure 1. Major ways in which vegetation affects surface-atmosphere exchange of energy. CRO = crops, DBF = deciduous broadleaf forests, ENF = evergreen needleleaf forests, GRA = grassland. Forested land tends to absorb more energy than non-forested land due to lower albedo but dissipates a larger portion of that energy through evaporation, limiting temperature increase. Zhao et al 2014


Remote sensing to the rescue?

Remote sensing is a useful tool for bridging the gap between biophysical and landscape processes. It offers a great deal flexibility in scale and is well suited to represent the kinds of things that matter for biophysics. After all, remote sensing satellites literally measure outputs of energy from the land surface. Since the processed data products from these measurements are standardized at relatively fine temporospatial resolution but collected over annual to decadal periods of time, they facilitate highly data-informed generalizations about fine-scale processes.

The biophysics of the past

I’ve been spending the past two years using “okay, mostly learning to use”┬áremote sensing data to empirically link vegetation and biophysics. The goal is to understand how vegetation change since European settlement has altered the biophysics of the land surface, and how that might affect climate.

Fig 2

Figure 2: PLS pre-settlement vegetation and MODIS modern vegetation

I used 10-year averages of MODIS remote sensing data to create regressions linking vegetation type to two important biophysical properties: albedo and surface temperature. I then projected that relationship onto the vegetation of the past. Think 30,000-color paint-by-number grid cells.

Fig 3

Figure 3: Differences (modern vs historic) in albedo and surface temperature

The biophysical changes in the land surface are striking. Typical albedo has increased in the winter – there are less trees (because of logging, urban expansion, etc.) to cover up the snow – but is mostly unchanged in the growing season. Surface temperature has decreased in winter but increased in summer. This trend is particularly interesting because it seems to suggest a loss of temperature regulation with the loss of forests; the modern surface is colder in the cold season and warmer in the warm season. For more detail on my findings, check out my recent AGU poster. Although we can’t know exactly how a centuries-gone landscape exchanged energy with its atmosphere, remote sensing data offers a way to construct useful empirical baseline for changes in vegetation biophysics. In addition to offering its own scientific insights, this work could serve as an interesting comparison to outputs from Paleon Models, a role which I plan to participate in on in the upcoming site level model intercomparison project.