Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Abstract

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo ( Shuai et al., 2011 ) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seas...

A UAV-Based Sensor System for Measuring Land Surface Albedo: Tested over a Boreal Peatland Ecosystem

“…Many BRDF-based methods use external BRDF information from the 8-day 500 m MODIS BRDF model parameter product (MCD43A1) [10] to compute the directional reflectance across all viewing and solar zenith angles, for a given scene [32]. The integrated shortwave MODIS BRDF isotropic, volumetric and geometric parameters were used to calculate surface reflectance at the view and solar zenith angles of each Landsat 8 and Sentinel 2 scene [33] (Equation (3)):…”

“…where A is Landsat/Sentinel narrowband albedo to be calculated, r l is observed Landsat/Sentinel reflectance, Ω l is viewing and solar geometry of Landsat/Sentinel data, α is albedo, and r(Ω l ) is the reflectance at Landsat/Sentinel sun view geometry. Both α and r(Ω l ) are derived by the MODIS BRDF parameters [33]. The narrowband surface albedo calculated using the algorithm above is then used to calculate total shortwave albedo using the coefficients (Equation (4)) formulated by [34].…”

Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa

“…The actual albedo is determined by both atmospheric states and land surface reflective properties (Lucht, Schaaf, & Strahler, 2000;Wang et al, 2017). However, the white-sky albedo (WSA) is related purely to the properties of the land surface and is not affected by atmospheric conditions (Strahler et al, 1999).…”

Monitoring Effects of Land Cover Change on Biophysical Drivers in Rangelands Using Albedo

“…Betts [17] found surface albedo to be an accurate proxy for land cover change in a semi-arid region in Brazil, due to its sensitivity to seasonal phenological variation [17,18] and landscapes affected by land management practices [19]. Land cover change projections in the Eastern Cape of South Africa have highlighted the importance of focusing land and water resources management interventions on rehabilitation in catchments under dualistic 1 farming systems [20].…”