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Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.). [ABSTRACT FROM AUTHOR]

This paper explores the potential of Global Navigation Satellite Systems (GNSS) multipath signals for soil moisture retrieval, specifically focusing on Navigation with Indian Constellations (NavIC). Utilizing GNSS-Interferometric Reflectometry (IR) techniques, wavelet analysis is employed to extract the low-frequency signal sensitive to soil moisture from the reflected multipath signals. The reconstructed low-frequency multipath signal is then subjected to Lomb-Scargle Periodogram (LSP) analysis to determine multipath frequency. A comprehensive sensitivity analysis reveals the dependency of multipath frequency on field soil moisture, particularly in the presence of vegetation. The sensitivity varies across different crop growth stages, categorized by crop height. The Pearson correlation coefficient (r) for crop height less than one wavelength was 0.81 and for greater than one wavelength it was 0.67. The sensitivity of multipath frequency for soil moisture was 0.48 rad s−1 cm3 cm−3 and 0.47 rad s−1 cm3 cm−3 for crop height less than one wavelength and crop height greater than one wavelength respectively.This research underscores the potential of GNSS-IR techniques, incorporating wavelet analysis and LSP, for soil moisture sensitivity, especially in areas with varying vegetation and crop growth stages. [ABSTRACT FROM AUTHOR]

Studies of soil moisture with Global Navigation Satellite System (GNSS) have gained the attention of several researchers. Multipath amplitude, multipath phase, and multipath frequency are multipath observables that are utilized in the study of soil moisture. However, an inter-comparison of the performance of these parameters for soil moisture under different roughness and vegetation conditions is very much required to have a better insight so that more robust inversion algorithm for soil moisture retrieval with multipath observables can be designed. Therefore, this paper analyses the performance of these multipath observables for soil moisture over bare smooth soil, rough surface, and vegetated soil. Two different fields have been investigated to include the location variability. Navigation with Indian constellation (NavIC) multipath signal has been used in this study. Statistical parameters such as correlation coefficient (R), Root Mean Square Error (RMSE), and sensitivity have been determined to study the performance of multipath observable for soil moisture under different surface roughness and vegetation conditions. [ABSTRACT FROM AUTHOR]

This letter aims to develop an inversion model to estimate soil moisture using Navigation with Indian Constellation (NavIC) L-band signal. Several research works suggest that microwave signal property gets affected after reflecting from the soil surface. The nature of the reflected microwave signal depends on the signal’s penetration depth, which is the factor of water content present in the soil surface. NavIC multipath signal can be used for the estimation of soil moisture using this property. The carrier to noise ratio $(C/N_{\mathrm {o}})$ of NavIC signal is used for this purpose. The model proposed in the letter is based on the relationship of estimated multipath phase value with the volumetric moisture content present in the soil surface. The output of the developed model is highly encouraging. A linear relation relationship with a high correlation coefficient value of 0.902 and root mean square error (RMSE) of 4.03% is obtained between ground truth soil moisture and retrieved soil moisture from developed algorithm. [ABSTRACT FROM AUTHOR]