Accurate estimation and high-resolution mapping of land surface emissivity are crucial for enhancing understanding of Earth system processes and facilitating informed decision-making across various fields. This study presents a method that combines Soil Moisture Active Passive (SMAP) radiometer and Cyclone Global Navigation Satellite System (CYGNSS) reflectometer data to derive 9 km quasi-global (within ±38° latitudes) microwave land surface emissivity estimates for the first time. In practice, the SMAP emissivity data serve as the base at 36 km scale, while CYGNSS reflectivity data are used to capture the spatial heterogeneity at 9 km scale. The combination is achieved through a spatially varying scaling factor that converts CYGNSS reflectivity to SMAP emissivity space. Global comparisons show a strong correspondence between the downscaled SMAP/CYGNSS 9 km emissivity and SMAP interpolated 9 km emissivity, yielding correlation coefficient (R) values ranging from 0.93 to 0.96 and Root-Mean-Square Error (RMSE) values from 0.018 to 0.020 for four 90-day periods. Moreover, the downscaled emissivity exhibits satisfactory spatial-temporal performance, with a median repeat period of 2.5 days for most mid-latitude regions and 4.7 days for all regions. When compared to SMAP native emissivity at 36 km scale, the upscaled SMAP/CYGNSS 36 km emissivity shows a significant increase of 66% in spatial coverage on the daily scale and a median increase of 64% in temporal coverage. These results indicate that the GNSS-R data are likely to be an effective supplement at small spatial scale with a sub-daily to daily scale for future remote sensing of microwave emissivity.

The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.