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3. GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects.

Author

Wei, Haohan, Yang, Xiaofeng, Pan, Yuwei, and Shen, Fei

Subjects
*SOIL moisture, *MACHINE learning, *GLOBAL Positioning System, *NORMALIZED difference vegetation index, *HYDROLOGIC cycle
Abstract

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting. [ABSTRACT FROM AUTHOR]

Published
2023
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18. Abstract 13153: Joint Influence of Diabetes and Heart Failure on All-Cause Mortality Among US Adults

Author

Wei, Haohan, Nicholson, Andrew, Wang, Binhuan, and Natarajan, Sundar

Abstract

Background:Men and women with heart failure and/or diabetes mellitus (DM) may have different risk for mortality. A combination of physiological and behavioral differences could explain this. We examined the independent influence of DM and HF on mortality in adults with HF and explored the role of gender on this relationship, if any.Methods:This is an analysis of NHANES population-based data (1999-2010) linked to mortality. We categorized the overall population into four groups: DM alone (DM), HF alone (HF), DM and HF (Both), and neither DM nor HF (Neither). After initial descriptive statistics, multivariate Cox-proportional hazards models evaluated the influence of each group by gender, while controlling for other sociodemographic characteristics and cardiovascular risk factors including age, body mass index (BMI), hypertension, history of coronary heart disease, c-reactive protein (CRP), albumin, A1c, smoking, ethnicity and income. All analyses incorporated the complex survey design, including weights, stratifications, and clustering.Results:The overall mortality (raw number of deceased, weighted population %) for each subgroup from a total of 27,218 participants (population estimate = 304,859,717) were: Male_DM (197, 13.9%), Female_DM (145, 10.2%), Male_HF (118, 30.8%), Female_HF (83, 33.4%), Male_Both (41, 33.1%), Female_Both (33, 41.3%), Male_Neither (960, 5.3%) and Female_Neither (762, 4.7%). Compared to the Female_Neither group, the hazard ratios (95% CI, p-value) for Male DM, HF, Both and Neither were 2.01 (1.44 - 2.80, <0.0001), 3.49 (2.32 - 5.25, <0.0001), 4.39 (2.60 - 7.40, <0.0001) and 1.71 (1.48 - 1.98, <0.0001) respectively. The corresponding metrics for Female DM, HF and Both were 1.03 (0.71 - 1.49, 0.88), 2.17 (1.51 - 3.11, <0.0001) and 2.86 (1.67 - 4.89, 0.0002) respectively. The HRs of Male compared to Female for Neither, DM, HF and Both were 1.71 (1.48 - 1.97, <0.0001), 1.95 (1.28 - 2.97, 0.0021), 1.61 (0.99 - 2.62, 0.054), 1.53 (0.74 - 3.19, 0.25), respectively.Conclusions:This analysis reinforces diabetes and HF as strong independent risk factors for mortality with the combination of diabetes and HF being particularly concerning. Men seem to have a higher mortality risk than women.

Published
2019
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