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A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

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Název: A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval
Autoři: Fade Chen, Lilong Liu, Fei Guo, Liangke Huang
Zdroj: Remote Sensing, Vol 16, Iss 5, p 931 (2024)
Informace o vydavateli: MDPI AG
Rok vydání: 2024
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: cyclone global navigation satellite system (CYGNSS), vegetation water content (VWC), soil moisture, spaceborne GNSS-reflectometry (GNSS-R), Science
Popis: In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega ( τ − w ) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m 2 , respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m 2 , respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/2072-4292/16/5/931; https://doaj.org/toc/2072-4292; https://doaj.org/article/41058e22f1cb47c5bbbe5a695b76ffa3
DOI: 10.3390/rs16050931
Dostupnost: https://doi.org/10.3390/rs16050931
https://doaj.org/article/41058e22f1cb47c5bbbe5a695b76ffa3
Přístupové číslo: edsbas.29958EF
Databáze: BASE
Popis
Abstrakt:In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega ( <semantics> τ − w </semantics> ) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m 2 , respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m 2 , respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.
DOI:10.3390/rs16050931