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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #316158

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product

Author
item Wang, Zhuosen - COLLABORATOR
item Schaaf, Crystal - COLLABORATOR
item Sun, Qingsong - COLLABORATOR
item Kim, Jihyun - COLLABORATOR
item Erb, Angela - COLLABORATOR
item Gao, Feng
item Roman, Miguel - COLLABORATOR
item Yang, Yun
item Petroy, Shelley - COLLABORATOR
item Taylor, Jeffrey - COLLABORATOR
item Masek, Jeffrey - COLLABORATOR
item Morisette, Jeff - COLLABORATOR
item Zhang, Xiaoyang - COLLABORATOR

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2017
Publication Date: 3/27/2017
Citation: Wang, Z., Schaaf, C., Sun, Q., Kim, J., Erb, A., Gao, F.N., Roman, M., Yang, Y., Petroy, S., Taylor, J., Masek, J., Morisette, J., Zhang, X. 2017. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product. Geophysical Research Letters. 59:104-117.

Interpretive Summary: Climate change has had a profound effect on vegetation distribution and growth. The effect has been observed from ground and space. Currently, global vegetation conditions and change mapping are mainly based on coarse resolution (>500 meter) remote sensing imagery,which are too coarse for heterogeneous landscapes. This paper uses the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) data fusion approach to synthesize the daily 500 meter resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and the 16-day 30 meter resolution Landsat images. Albedo and phenology at 30 meter resolution over three Ameriflux sites were computed and validated using ground observations. The synthesized data set was used to evaluate the future National Ecological Observatory Network (NEON) sites. Accurate albedo and phenology estimates at multiple scales are critical for modeling carbon and water cycles, and particularly for assessing crop conditions at the field and regional scales required by the National Agricultural Statistics Service and Foreign Agricultural Service.

Technical Abstract: Climate warming over the past half century has led to observable changes in vegetation phenology and growing season length; which can be measured globally using remote sensing derived vegetation indices. Previous studies in mid- and high northern latitude systems show temperature driven earlier springs and longer growing seasons by up to two weeks. Phenology dynamics also alter the surface albedo, the energy not absorbed by the surface and influence the global energy balance. The surface albedo warming/cooling feedback enhances climate change, especially over snow covered areas. To monitor and quantify these changes, the phenology and albedo products from moderate spatial resolution MODIS and other satellite data have been widely used. The spatial resolution of a MODIS pixel is not ideal to characterize the surface dynamics of heterogeneous landscapes. Higher resolution satellite data, such as Landsat, lack the temporal resolution to capture rapid dynamic changes. In this study, high temporal and spatial resolution synthetic albedo and nadir BRDF adjusted Reflectance derived vegetation index were generated from 500 m MODIS operational V006 daily BRDF/albedo products and 30 m Landsat data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The enhanced dataset was used to detect albedo and vegetation phenology dynamics over three Ameriflux tower sites and to serve as a model for the pending implementation of the NEON, National Ecological Observatory network. In combining these data sources, we improve the capture of seasonal vegetation dynamics with greater spatial and temporal details, especially over heterogeneous land surfaces.