Using forest reflectance modelling to estimate albedo for narrow-view satellites

Abstract

Surface albedo estimations from remote sensing are invaluable for energy balance descrip- tions in climate research. The aim of this work is to evaluate the possibility of using Sentinel-2 satellite data to get the total albedo of a small, vegetated area. In this thesis, a simple machine learning model is created to convert simulated Sentinel-2 reflectance measurements to surface albedo.

The Forest Reflectance and Transmission model is used to simulate the reflectance behaviour of a pine forest and a field vegetation stand. The angular behaviour was analysed, and it was found that the directional reflectances at near-nadir view angles are typically lower than the hemispherical reflectance, though this effect also depends on the solar angle. As a result, it is important to include angular modelling in the albedo estimations. Next, a direct- estimation and narrow-to-broadband linear regression model were trained with FRT simulated reflectance data. The ability to predict albedo from the FRT data was high, with r2 > 0.94 for the direct estimation and r2 > 0.999 for the narrow-to-broadband model. Finally, the models for the pine forest and field were compared and found to be distinctly different.

In conclusion, it is important to include angular modelling in albedo estimations. The regression models presented in this thesis perform well for the simulated vegetation stands. Moreover, it is valuable to train separate models for different land use classes. With some further improvements, the regression models for Sentinel-2 data have potential for accurate evaluation of surface albedo at a fine spatial resolution.

Popular Abstract

The past years, climate change has been one of the biggest concerns for most of the world. We are all aware that the average temperature on Earth is increasing, and many initiatives are set up to work against further temperature increase. To battle climate change, it is important to study the energy balance of the Earth as a system. The sun brings energy into the system in the form of electromagnetic radiation. Part of this energy is scattered and absorbed in the atmosphere. The rest of the radiation reaches the surface, and there it is either reflected or absorbed. The part of the incoming solar power that is reflected by either the atmosphere or the surface simply leaves the system again, but the absorbed radiation contributes to the global warming.
The term surface albedo describes the reflectivity of the surface. This reflectivity depends on the land cover. A higher albedo means that more light is reflected by the Earth's surface. In the interest of decreasing the accelerated global warming, a high albedo is desired. Changing the land usage is a way to influence the albedo. But before taking this step, it is critical to know the effects on the surface albedo of changing the vegetation.

Remote sensing and satellite data have been invaluable for monitoring the Earth in recent years. The Sentinel-2 satellite constellation from the European Space Agency is a system that provides reflectance data. The satellite measures the reflectance in thirteen separate wavelength ranges and does so at a very fine spatial resolution. The reflectance is measured by the satellite at one specific angle, but not all incoming sunlight is reflected at this exact angle. Similarly, the satellite measures the reflectance for a selection of wavelength ranges, and not for the complete solar wavelength spectrum. To get the total reflectance for the hemisphere and the solar wavelength spectrum, a conversion must be applied to the satellite data.
The aim of this work is to evaluate the possibility of using Sentinel-2 satellite data to calculate the total albedo of a smaller, vegetated area, and comparing the albedo estimation for different vegetation types. This is done by creating a simple machine learning model, based on simulated reflectance data, to convert Sentinel-2 reflectance measurements to surface albedo.
The model is trained using Forest Reflectance and Transmittance (FRT) simulations. The FRT model simulates the reflectance for a specified type of vegetation. From the FRT results, the albedo and the satellite measurements are calculated, and these are used as training data for the machine learning model.
Analysis of the FRT model results showed the importance of including angular modelling in albedo estimations. The regression models presented in this thesis perform well for the simulated vegetation stands. Additionally, it is shown that it is valuable to train separate models for different land use classes.

With the results obtained in this thesis, a step is taken towards better surface albedo estimations on a more detailed scale. The regression models require further development before practical use, but have shown potential for the evaluation of surface albedo at a fine spatial resolution. The Sentinel-2 satellite system is especially suitable for acquiring surface albedo on a smaller scale, because of its fine spatial resolution.
As development of new satellite missions is continuously ongoing, the measurement instruments that provide additional or more detailed spectral data will also appear. Combined with continued development in albedo estimation algorithms across satellite systems, our ability to understand the surface energy balance will improve.

• author: Bergmans, Nick
• supervisor: Lars Eklundh; Hongxiao Jin; Johan Friberg
• organization: Dept of Physical Geography and Ecosystem Science; Department of Physics
• course: FYSM33 20231
• year: 2023
• type: H2 - Master's Degree (Two Years)
• subject: Earth and Environmental Sciences
• keywords: satellite, remote sensing, albedo, surface reflectance, vegetation, forest, machine learning, albedo estimation
• language: English
• id: 9142313
• date added to LUP: 2023-12-20 14:01:11
• date last changed: 2023-12-20 14:01:11