%0 Dataset
%T Global daily seamless 9-kilometer vegetation optical depth (VOD) product (2010-2021)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/d2cd0fde-9ba5-416c-803f-595683ba0ae6
%W NCDC
%R 10.5281/zenodo.13334757
%A Yuan Qiangqiang
%K VOD;Global;vegetation optical depth;spatiotemporal fusion
%X This dataset adopts a penalty least squares regression method based on three-dimensional discrete cosine transform. Firstly, seamless global daily L-VOD products are generated. Then, non local filtering ideas are used to achieve spatiotemporal fusion of high-resolution and low resolution data. Finally, a global daily seamless 9-kilometer L-VOD product is generated from January 1, 2010 to July 31, 2021. To verify product quality, time series validation and simulated missing area validation were performed on the reconstructed data. The fusion product has been validated in both time and space, and compared numerically with the original 9-kilometer data during the overlap period. The results showed that the coefficient of determination (R ²) of the seamless SMOS (SMAP) dataset under simulated real missing masks was 0.855 (0.947), and the root mean square error (RMSE) was 0.094 (0.073). The spatiotemporal consistency of the reconstructed daily L-VOD product is consistent with the time series distribution of the original effective values. The spatial information of the fusion product and the original 9-kilometer data during the overlapping period is basically consistent (R ²: 0.926-0.958, RMSE: 0.072-0.093, average absolute error MAE: 0.047-0.064). The time changes of the integrated product and the original product are basically synchronized. This dataset can provide timely vegetation information during natural disasters such as floods, droughts, and forest fires, supporting early disaster warning and real-time response.