An improved inversion method of forest biomass based on satellite GNSS-R

被引：0
|
作者：
Zhou, Xun [1 ]
Zheng, Nanshan [1 ,2 ]
Ding, Rui [1 ,2 ]
Zhang, Hengyi [1 ]
He, Jiaxing [1 ]
机构：
[1] School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou
[2] Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China university of mining and technology, Xuzhou
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2024年 / 50卷 / 08期
基金：
中国国家自然科学基金;
关键词：
CYGNSS; forest biomass; GNSS-R; SMAP satellite; soil moisture; vegetation optical depth;
D O I：
10.13700/j.bh.1001-5965.2022.0654
中图分类号：
学科分类号：
摘要：
Based on the Tau-Omega model, a spaceborne global navigation satellite system relectometry (GNSS-R) forest aboveground biomass inversion method considering the correction of ground soil moisture is proposed. The cyclone global navigation satellite system (CYGNSS) reflectance was corrected using the Tau-Omega model to increase the modeling parameters' accuracy, and SMAP satellite soil moisture was chosen as supplementary data. The biomass reference data utilized was the vegetation optical depth (VOD) supplied by SMAP satellite and the above-ground biomass (AGB) maps. The correlation changes between the observed values and the reference data prior to and following improvement were compared. The results show that the correlation coefficient increases significantly after the correction. The correlation coefficient between the parameters after improvement with VOD is increased from 0.54 to 0.67 compared to the reflectivity with VOD, and the correlation coefficient with AGB is increased from 0.46 to 0.56. Then, the GNSS-R VOD and AGB inversion models were established based on the corrected parameters and reflectivity through the artificial neural network, respectively. The results show that the improved method can effectively improve the inversion accuracy of VOD and AGB, and the improvement effect is better in areas with low biomass levels. For VOD inversion, after improvement, the correlation coefficient increased from 0.70 to 0.83, and the RMES decreased from 0.21 to 0.17; for AGB inversion, after improvement, the correlation coefficient increased from 0.61 to 0.71, and the RMES decreased from 74 t/hm2 to 65 t/hm2 © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
收藏
页码：2619 / 2626
页数：7
相关论文
共 26 条
  • [1] FAN W Y, ZHANG H Y, YU Y, Et al., Comparison of three models of forest biomass estimation, Chinese Journal of Plant Ecology, 35, 4, pp. 402-410, (2011)
  • [2] KELLER M., Revised method for forest canopy height estimation from geoscience laser altimeter system waveforms, Journal of Applied Remote Sensing, 1, 1, (2007)
  • [3] CHAVE J, ANDALO C, BROWN S, Et al., Tree allometry and improved estimation of carbon stocks and balance in tropical forests, Oecologia, 145, 1, pp. 87-99, (2005)
  • [4] CARRENO-LUENGO H, LUZI G, CROSETTO M., Above-ground biomass retrieval over tropical forests: A novel GNSS-R approach with CyGNSS, Remote Sensing, 12, 9, (2020)
  • [5] BAGHDADI N, LE MAIRE G, BAILLY J S, Et al., Evaluation of ALOS/PALSAR L-band data for the estimation of eucalyptus plantations aboveground biomass in Brazil, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 8, pp. 3802-3811, (2015)
  • [6] LE TOAN T, QUEGAN S, DAVIDSON M W J, Et al., The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle, Remote Sensing of Environment, 115, 11, pp. 2850-2860, (2011)
  • [7] GONCALVES F, TREUHAFT R, LAW B, Et al., Estimating aboveground biomass in tropical forests: Field methods and error analysis for the calibration of remote sensing observations, Remote Sensing, 9, 1, (2017)
  • [8] WU X R, GUO P, SUN Y Q, Et al., Recent progress on vegetation remote sensing using spaceborne GNSS-reflectometry, Remote Sensing, 13, 21, (2021)
  • [9] ULABY F T., Microwave remote sensing, active and passive, Microwave Remote Sensing Fundamentals and Radiometry, 1, pp. 191-208, (1981)
  • [10] WU X R, LI Y, LI C L., Research on crop biomass monitoring using GNSS-R technique based on Bi-Mimics model, Remote Sensing Technology and Application, 27, 2, pp. 220-230, (2012)
123