This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods
by
Yongfeng Zhang
Yongfeng Zhang 1,
Jinwei Bu
Jinwei Bu 1,*
,
Xiaoqing Zuo
Xiaoqing Zuo 1,
Kegen Yu
Kegen Yu 2,
Qiulan Wang
Qiulan Wang 1 and
Weimin Huang
Weimin Huang
Professor Huang received his BSc and MSc in radio physics (radio wave propagation and antennas) and [...]
Professor Huang received his BSc and MSc in radio physics (radio wave propagation and antennas) and PhD in space physics from the School of Electronic Information at Wuhan University in 1995, 1997, and 2001, respectively. He also completed an additional MEng and a three-year postdoctoral fellowship in electrical and computer engineering at Memorial University of Newfoundland (MUN) in 2004 and 2007, respectively. From 2008 to 2010, he was a design engineer with Rutter Technologies, St. John's, NL, Canada. Since 2010, he has been with the Faculty of Engineering and Applied Science, Memorial University, where he became a full professor in 2019 and department deputy head during 2020–2023. Professor Huang’s past and present research interests include the mapping of oceanic surface parameters and targets via high-frequency ground wave radar, X-band marine radar, global navigation satellite systems, synthetic aperture radar, digital image processing, and applied electromagnetics.
3
1
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(15), 2793; https://doi.org/10.3390/rs16152793 (registering DOI)
Submission received: 6 July 2024
/
Revised: 24 July 2024
/
Accepted: 29 July 2024
/
Published: 30 July 2024
Abstract
Abstract: 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.
Share and Cite
MDPI and ACS Style
Zhang, Y.; Bu, J.; Zuo, X.; Yu, K.; Wang, Q.; Huang, W.
Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods. Remote Sens. 2024, 16, 2793.
https://doi.org/10.3390/rs16152793
AMA Style
Zhang Y, Bu J, Zuo X, Yu K, Wang Q, Huang W.
Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods. Remote Sensing. 2024; 16(15):2793.
https://doi.org/10.3390/rs16152793
Chicago/Turabian Style
Zhang, Yongfeng, Jinwei Bu, Xiaoqing Zuo, Kegen Yu, Qiulan Wang, and Weimin Huang.
2024. "Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods" Remote Sensing 16, no. 15: 2793.
https://doi.org/10.3390/rs16152793
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.