Most polar-orbit-satellite based passive microwave radiometers, e.g. AMSR-2 and the FY-3B Microwave Radiation Imager (FY3B-MWRI), have the revisit cycles of ~2 days in low latitudes. This indicates that requirement of complete global coverage on the daily basis has not been met. On the other hand, using synergistic observations from different radiometers seems promising to enhance the temporal resolution of observations in such areas, whereas cross-radiometer inter-calibration is essential prior to their synergism. For this purpose, conventional inter-calibration models are usually built based on linear relations of brightness temperature (TB) between different radiometers. However, the linear model has relatively poor performance in low latitudes primarily due to wet surface conditions. In this study, we propose an improved non-linear model for calibrating MWRI TB against AMSR-2 TB at 18.7 GHz and 23.8 GHz channels. Global land surface temperature (LST) is estimated using the post-calibration MWRI TB data and then validated against AMSR-2 based LST, MODIS based LST, and in situ near-surface air temperature datasets in low latitudes respectively. The validation results show that LST outcome based on the improved non-linear inter-calibration method has smaller global Root Mean Square Errors (RMSEs) below 3.5 K, compared with the RMSEs of ~6 K obtained from the conventional linear inter-calibration method and from the results without inter-calibration. Further analysis also shows that synergistic observations between AMSR-2 and MWRI are capable of shortening the maximum global revisit cycle of AMSR-2 LST from ~2 days to less than 1.2 days. These results indicate that the improved inter-calibration method is effective for obtaining daily LST datasets with quasi-complete global coverage and reliable accuracy.

大多数基于极轨卫星的无源微波辐射计，例如 AMSR-2 和 FY-3B 微波辐射成像仪 (FY3B-MWRI)，在低纬度地区的重访周期约为 2 天。这表明尚未满足每日完全覆盖全球的要求。另一方面，使用来自不同辐射计的协同观测似乎有希望提高这些区域观测的时间分辨率，而交叉辐射计相互校准在它们协同作用之前是必不可少的。为此，传统的相互校准模型通常基于不同辐射计之间的亮温 (TB) 的线性关系构建。然而，线性模型在低纬度地区的性能相对较差，主要是由于潮湿的表面条件。在这项研究中，我们提出了一种改进的非线性模型，用于在 18.7 GHz 和 23.8 GHz 信道上针对 AMSR-2 TB 校准 MWRI TB。全球地表温度 (LST) 使用校准后的 MWRI TB 数据估算，然后分别针对基于 AMSR-2 的 LST、基于 MODIS 的 LST 和低纬度的原位近地表气温数据集进行验证。验证结果表明，基于改进的非线性互校准方法的 LST 结果具有小于 3.5 K 的全局均方根误差 (RMSE)，与传统线性互校准方法获得的 ~6 K 的 RMSE 相比，以及从没有相互校准的结果。进一步的分析还表明，AMSR-2 和 MWRI 之间的协同观测能够将 AMSR-2 LST 的最大全球重访周期从~2 天缩短到不到 1.2 天。