Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview

Bu, Jinwei; Wang, Qiulan; Wang, Ziyi; Fan, Shaoqiang; Liu, Xinyu; Zuo, Xiaoqing

doi:10.1109/jstars.2024.3415754

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IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3415754

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Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview

Jinwei Bu,

Qiulan Wang,

Ziyi Wang

et al.

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2024

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Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods

Zhang,

Bu,

Zuo

et al. 2024

Remote Sensing

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Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m2. Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m2. Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m2. In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively.

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Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods

Zhang,

Bu,

Zuo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

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Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview

Cited by 1 publication

References 189 publications

Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods

Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods

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