Estimation of Land Surface Albedo from MODIS and VIIRS Data: A Multi-Sensor Strategy Based on the Direct Estimation Algorithm and Statistical-Based Temporal Filter

Mengsi Wang,Xijia Li,Xianlei Fan,Ying Qu,Qiang Liu

doi:10.3390/rs12244131

Abstract

Land surface albedo is an important variable for Earth’s radiation and energy budget. Over the past decades, many surface albedo products have been derived from a variety of remote sensing data. However, the estimation accuracy, temporal resolution, and temporal continuity of these datasets still need to be improved. We developed a multi-sensor strategy (MSS) based on the direct-estimation algorithm (DEA) and Statistical-Based Temporal Filter (STF) to improve the quality of land surface albedo datasets. The moderate-resolution imaging spectroradiometer (MODIS) data onboard Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (NPP) were used as multi-sensor data. The MCD43A3 product and in situ measurements from the Surface Radiation Budget Network (SURFRAD) and FLUXNET sites were employed for validation and comparison. The results showed that the proposed MSS method significantly improved the temporal continuity and estimation accuracy during the snow-covered period, which was more consistent with the measurements of SURFRAD (R = 0.9498, root mean square error (RMSE) = 0.0387, and bias = −0.0017) and FLUXNET (R = 0.9421, RMSE = 0.0330, and bias = 0.0002) sites. Moreover, this is a promising method to generate long-term, spatiotemporal continuous land surface albedo datasets with high temporal resolution.

Highlights

Land surface albedo, defined as a ratio of the radiation reflected from the land surface and the incident solar radiation (0.3–5.0 μm) [1], is a vital parameter for the Earth’s radiation and energy budget, which has been used widely in studies of global climate change, hydrology, agriculture, and weather forecasting [2,3]
The results demonstrated that the number of validate values, temporal resolution, and temporal continuity significantly improved by using multi-sensor data and the Statistical-Based Temporal Filter (STF) approach, which performed better than the MCD43A3 dataset
We proposed an multi-sensor strategy (MSS) method to estimate of the land surface albedo from multi-sensor data (e.g., moderate-resolution imaging spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS))

Summary

Introduction

Land surface albedo, defined as a ratio of the radiation reflected from the land surface and the incident solar radiation (0.3–5.0 μm) [1], is a vital parameter for the Earth’s radiation and energy budget, which has been used widely in studies of global climate change, hydrology, agriculture, and weather forecasting [2,3]. It is important to monitor the dynamics of the land surface albedo with different instruments [3,9]. The land surface albedo can be directly measured using ground and tower-based pyranometers, the global or regional land surface albedo cannot be accurately represented because of the limited footprints of measuring instruments and high spatial heterogeneity of land surface albedo [10]. The ability to monitor the spatiotemporal variations with remote sensing data offers significant advantages [3]

Results

Discussion

Conclusion