Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020

Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.

All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.

Reference:

Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.

Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.

Zhong, B.,  A. Yang, Q. Liu, S. Wu, X.  Shan, and  X  Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.