Coherence & Surface Inundation

HydroGNSS Cal Val Workshop — Guildford, 31/03/2023.Presentación del algoritmo de inversión de datos que estamos implementando para la misión de la ESA, HydroGNSS, para obtención de la detección de agua (inundaciones, humedales); actividades hechas hasta el momento para validar el algoritmo, y también planes de validación y calibración futuras.Peer reviewe

Analysis of signal-to-noise ratio retrieved from multi-GNSS satellite data of land surface reflections

The reflection of Global Navigation Satellite System (GNSS) signals, known as GNSS reflectometry (GNSS-R), has made significant progress in monitoring ocean and land surface geophysical parameters. The main purpose of this investigation is to analyze the impact of antenna gain, land surface properties, and coherent integration time on the signal-to-noise ratio (SNR) of multi-GNSS reflected signals using complex waveform (CW) products generated by the Cyclone Global Navigation Satellite System (CYGNSS) constellation recording multi-GNSS raw intermediate frequency (IF) data. For different GNSS constellations, CYGNSS antenna gain of 6–9 dB can achieve an SNR of 0 dB or more over most pan-tropical regions. The normalized SNR (NSNR) calculated by the calibration procedure is used for such analysis. The results show that the coherent reflection caused the largest NSNR by the inland water body, and NSNR from the GPS reflected signal is about 2 dB and 4 dB larger than Galileo and BDS, respectively, whereas the dense vegetation caused a strong attenuation of the reflected signal and resulted in the smallest NSNR. Different GNSS systems have similar sensitivity to roughness coefficient (RC). In contrast, different GNSS constellations exhibit different sensitivities for moderate soil moisture (SM), the NSNR increases for GPS, BDS, and Galileo are estimated by linear fitting to be 4.85 dB, 7.67 dB, and 8.73 dB, respectively. NSNR gradually increases with above-ground biomass (AGB) when AGB is less than 100 Mg/ha, which seems to be the threshold for signals to penetrate the sparse vegetation and sense SM under the vegetation. However, when AGB is greater than 100 Mg/ha, the opposite is true. Furthermore, NSNR can be improved by longer coherent integration time, the average improvement in NSNR is about 1.5 dB with 2 ms coherent integration time and approximately 3 dB with 4 ms coherent integration time.This work was funded by the National Natural Science Foundation of China (No. 42074041); Shaanxi Province Science and Technology Innovation Team (Ref. 2021TD-51) and the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022); National Key Research and Development Program of China (No. 2020YFC1512000); and Fundamental Research Funds for the Central Universities, Chang’an University (No. 300102262401). This work of Weiqiang LI is partially supported by Grant RYC2019-027000-I funded by MCIN/AEI/http://dx.doi.org/10.13039/501100011033 and by “European Union Next Generation EU/PRTR,” as is also supported by Grant 20215AT007 funded by Spanish National Research Council. Qi Liu's work was partly supported by the China Scholarship Council (CSC) through a State Scholarship Fund (No. 202106560048).Peer reviewe

Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors