融合地形参数的CYGNSS青藏高原土壤湿度反演OA
Integrating topography parameters for soil moisture retrieval using CYGNSS on the Qinghai-Tibet Plateau
青藏高原的土壤湿度是影响全球大气环流和气候变化的重要因素,旋风全球导航卫星系统(CYGNSS)使用全球导航卫星系统反射信号技术(GNSS-R),为监测青藏高原的土壤湿度提供了新的手段,但高原复杂的地形环境使得CYGNSS的反射率难以被直接用于土壤湿度的反演.基于此,提出了融合修正后CYGNSS反射率、CYGNSS入射角、地形参数(海拔、坡度和地表粗糙度)等 5 个特征参数的星载GNSS-R土壤湿度机器学习反演模型.对 CYGNSS 反射率进行发射功率的系统误差修正、地表植被和地表粗糙度衰减误差修正;将修正后反射率等 5个参数作为输入特征量,SMAP土壤湿度作为验证数据,使用 2020年解冻期(6-9月)数据按 5∶5随机分为训练集和验证集,分别建立随机森林(RF)和人工神经网络(ANN)青藏高原土壤湿度反演模型,以2021年解冻期的数据作为测试集,考察模型的泛化能力.随机森林模型的结果优于人工神经网络模型,测试集上反演结果的均方根误差(RMSE)为 0.058 6 cm3/cm3,皮尔逊相关系数为 0.703 3,模型具有较好的泛化性能,且反演得到的青藏高原土壤湿度的空间变化和降水的空间变化趋势相吻合.将CYGNSS土壤湿度与那曲实测土壤湿度进行对比,均方根误差为 0.070 cm3/cm3,具有较高的精度.研究结果表明,融合修正后CYGNSS反射率、CYGNSS入射角和地形参数建立的反演模型能够较为准确地反演青藏高原大范围的土壤湿度.
The soil moisture of the Qinghai-Tibet Plateau plays a crucial role in global atmospheric circulation and climate change.The cyclone global navigation satellite system(CYGNSS),utilizing global navigation satellite system reflectometry(GNSS-R),provides a novel method to monitor soil moisture on the Qinghai-Tibet Plateau;however,the complex topographic environment of the plateau hinders the direct use of CYGNSS reflectivity for soil moisture retrieval.This paper proposes a spaceborne GNSS-R soil moisture machine learning inversion model,which integrates five characteristic parameters:corrected CYGNSS reflectivity,CYGNSS incident angle,and terrain parameters(elevation,slope,surface roughness).First,the CYGNSS reflectance is corrected for two aspects:systematic errors in transmit power,and attenuation induced by surface vegetation and surface roughness.Then,the corrected reflectivity(along with the other four aforementioned parameters)is adopted as input feature quantities,and SMAP soil moisture data is used for model verification.For data partitioning,the 2020 thaw period(June–September)data are randomly split into a training set and a verification set at a 5∶5 ratio.On this basis,two soil moisture inversion models(random forest(RF)and artificial neural network(ANN))are established specifically for the Qinghai-Tibet Plateau.Use the data from the 2021 thaw period as a test set to examine the generalization ability of the model.The results of the random forest model are better than the artificial neural network model,the inversion result yielding a root mean square error(RMSE)of 0.058 6cm3/cm3and Pearson correlation coefficient of 0.703 3 on the test set.The model exhibits strong generalization performance:the spatial variation of the inverted soil moisture is consistent with the spatial variation trend of precipitation over the Qinghai-Tibet Plateau.Finally,a comparison between the CYGNSS-derived soil moisture and the in-situ measured soil moisture(from Naqu)shows high accuracy,with a RMSE of 0.070 cm3/cm3.The research results show that the inversion model,which integrates corrected CYGNSS reflectance,CYGNSS incident angle,and topography parameters,achieves a more accurate invert of soil moisture in a large range of the Qinghai-Tibet Plateau.
张云;师丽云;杨树瑚;潘海燕;韩彦岭;洪中华
上海海洋大学 信息学院,上海 201306||上海海洋大学 上海海洋智能信息与导航遥感工程技术研究中心,上海 201306上海海洋大学 信息学院,上海 201306上海海洋大学 信息学院,上海 201306||上海海洋大学 上海海洋智能信息与导航遥感工程技术研究中心,上海 201306上海海洋大学 信息学院,上海 201306||上海海洋大学 上海海洋智能信息与导航遥感工程技术研究中心,上海 201306上海海洋大学 信息学院,上海 201306||上海海洋大学 上海海洋智能信息与导航遥感工程技术研究中心,上海 201306上海海洋大学 信息学院,上海 201306||上海海洋大学 上海海洋智能信息与导航遥感工程技术研究中心,上海 201306
海洋科学
土壤湿度青藏高原旋风全球导航卫星系统反射率修正机器学习
soil moistureQinghai-Tibet Plateaucyclone global navigation satellite systemreflectance correctionmachine learning
《北京航空航天大学学报》 2026 (3)
643-654,12
国家自然科学基金(42176175,42271335)国家重点研发计划(2019YFD0900805) National Natural Science Foundation of China(42176175,42271335)National Key Research and Development Program of China(2019YFD0900805)
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