The GF land surface albedo product based on the high-spatial-and-temporal-resolution BRDF priori-knowledge and its preliminary validation

Land surface albedo is a critical parameter in radiation and energy budget. Using GF satellite data to produce the land surface albedo is beneficial for local-scale environmental monitoring. The challenge beneath the albedo estimation from the GF satellite data is the inadequate angular information, which complicates the BRDF inversion and the albedo derivation based on BRDF. We use the high spatial-and-temporal-resolution priori-knowledge BRDF database obtained from coarse spatial resolution multiangular information to help describe the GF BRDF features. Then, the GF albedo is estimated from the derived GF BRDF. The algorithm is applied in GF-1 data to generate the land surface albedo product in China. First, this algorithm and the production are introduced briefly. Then, the spatial-temporal features are evaluated by qualitative analysis and quantitative validation. In the validation, a long time series of field-measured albedo from the sites of different land covers is used. It includes the cropland (maize) in the Daman site from the Heihe remote sensing test site located in the northwest of China, the forest (Chaenomeles, metasequoia, Chinese pine) in the Huailai test remote sensing site located in the north of China, and the grass in the Dongbei remote sensing test site located in the northeast of China. These sites would be covered by bare soil or snow in winter. In this study, we preliminarily evaluate the algorithm feasibility in albedo production and product precision. The time series comparisons of the field measurements and the GF product present good agreement for different sites over 1 year to 2 years. For over 1-or 2-year time-continuous comparisons, the land surface status is changed driven by the phenology (vegetation growing cycle). Thus, this finding reveals the feasibility of the algorithm based on the spatial-temporal distributed BRDF a priori knowledge. The total root mean square error is 0.05, with a relative accuracy of 80.24%, which meets the application requirement. Therefore, this algorithm is feasible for the albedo estimation from the GF data. The validation results show a good agreement between the field measurements and the product. However, the remote sensing common products from GF satellite data have not been initiated long enough. Thus, evaluating the GF satellite data is still needed. Besides the algorithm itself, the albedo accuracy is directly affected by the land surface reflectance product’s precision, which may introduce uncertainties from the geometric and radiometric calibration, atmospheric correction, and even cloud production. Therefore, much work should be done to evaluate this product and clarify the effects of those factors. In this way, the algorithm and albedo production can be improved. © 2023 Science Press. All rights reserved.