摘要
基于Tau-Omega模型提出一种涉及地面土壤湿度修正的星载全球导航卫星系统反射测量(GNSS-R)森林地上生物量反演方法。选择SMAP卫星的土壤湿度作为辅助数据,运用Tau-Omega模型对旋风卫星导航系统(CYGNSS)反射率做出改正,提高建模参数的准确性。将SMAP卫星提供的植被光学深度(VOD)和地上植被生物量(AGB)地图作为生物量参考数据,比较了改正前后观测值与参考数据的相关性变化。结果表明,改正后相关系数提升明显,改正后参数较反射率与VOD的相关系数从0.54提升到了0.67,与AGB的相关系数从0.46提升到了0.56。随后通过人工神经网络分别基于改正后的参数和反射率建立GNSS-R VOD和AGB反演模型。结果表明,所提方法能够有效提高VOD和AGB的反演精度,且在生物量水平较低的地区改进效果更优。对于VOD反演,改进后相关系数从0.70提升到了0.83,RMES从0.21降低到了0.17;对于AGB反演,改进后相关系数从0.61提升到了0.71,RMES从74 t/hm^(2)降低到了65 t/hm^(2)。
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/hm^(2)to 65 t/hm^(2).
作者
周勋
郑南山
丁锐
章恒一
何佳星
ZHOU Xun;ZHENG Nanshan;DING Rui;ZHANG Hengyi;HE Jiaxing(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;Key Laboratory of Land Environment and Disaster Monitoring,Ministry of Natural Resources,China university of mining and technology,Xuzhou 221116,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2024年第8期2619-2626,共8页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(41974039)
自然资源部国土环境与灾害监测重点实验室开放基金(LEDM2021B11)
江苏省研究生科研与实践创新计划(KYCX22_2595)
中国矿业大学研究生创新计划项目(2022WLJCRCZL256)。
