A Method for Retrieving Daily Land Surface Albedo from Space at 30-m Resolution

Remote Sensing

Bo Gao²  Huili Gong²  Tianxing Wang³  Jose Moreno¹  Dale A. Quattrochi¹ 

[1] Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing 100048, China; E-Mail;Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing 100048, China; E-Mail:;State Key Laboratory of Remote Sensing Science, The Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China; E-Mail:

关键词: albedo;    downscaling;    spatio-temporal resolution;    HJ-1A/B;    MODIS;   

DOI  :  10.3390/rs70810951

来源: mdpi

【 摘 要 】

Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA) from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.