数字农科院2.0

Retrieval of Soil Moisture and Vegetation Water Content From Passive Microwave Remote Sensing: A Local-Scale Evaluation via Ground-Based Multichannel Radiometry

文献类型： 外文期刊

作者： Ma, Chunfeng;Li, Xin;Wang, Shuguo;Zhang, Yang;Liu, Xiaoyang;Hu, Yanxing;Dai, Liyun;Jin, Rui;Wang, Zengyan;Che, Tao

作者机构：

关键词： Microwave radiometry;Microwave measurement;Estimation;Vegetation mapping;Radiometers;Satellite broadcasting;Machine learning algorithms;Accuracy;Temperature measurement;Passive microwave remote sensing;Forward modeling;passive microwave remote sensing;soil moisture (SM) retrieval;variable channel algorithms

期刊名称： IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The single-channel algorithm (SCA), dual-channel algorithm (DCA), and multichannel algorithm (MCA) are commonly utilized in passive microwave remote sensing for estimating soil moisture (SM) and vegetation water content (VWC). However, determining the most effective algorithm(s) remains a challenge as there is a lack of comparative research on these algorithms. To this end, a comprehensive evaluation of these algorithms is presented using multifrequency radiometry data collected during a ground-based experiment. SM and VWC are separately estimated through a single-objective estimation (SOE) or simultaneously estimated through a dual-objective estimation (DOE) framework. The study reveals that SOE yields better results than the DOE framework in estimating SM and VWC using the SCA, DCA, and MCA algorithms, with decreasing accuracy performance as follows: MCA-SOE>DCA-SOE>SCA-SOE>MCA-DOE>SCA-DOE>DCA-DOE. Within the MCA-SOE scheme, the utilization of five-channel TBs (i.e., combination of V-polarized TB at L-band, dual-polarized TBs at Ku- and Ka-bands) leads to the most accurate SM and VWC retrievals, with an R value of 0.824 and a root mean square error (RMSE) (unbiased RMSE, ubRMSE) of 0.039 (0.037) m(3)/m(3) for SM, and an R value of 0.771 and an RMSE (ubRMSE) of 0.707 (0.705) kg/m(2) for VWC. An exceptional case shows that SM retrieval from a single Ku-band TB slightly exceeds that from MCA-SOE in $R$ value (0.831 versus 0.824), due to the less sensitive Ka-band TBs introduced in MCA-SOE. This evaluation underscores the benefits of employing SCA, DCA, and MCA algorithms for SM and VWC estimation, providing insights for enhancing SM retrieval methodologies and selecting optimal frequency and polarization.

分类号：

  • 相关文献

[1]Evaluating Spatial Representativeness Across Multiple Scales for a Comprehensive Ground Validation Network Using Landsat Land Surface Temperature Data and Random Forest. He, Xuanwei,Liu, Xiangyang,Ru, Chen,Deng, Xiangyi,Zhao, Ruoyi,Yu, Wenping. 2025

[2]An Empirical Relationship of Bare Soil Microwave Emissions Between Vertical and Horizontal Polarization at 10.65 GHz. Liu, Zeng-Lin,Wu, Hua,Tang, Bo-Hui,Liu, Zeng-Lin,Qiu, Shi,Li, Zhao-Liang,Li, Zhao-Liang. 2014

[3]An AI-Based Nested Large–Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method. Mengjie Liang,Kebiao Mao,Jiancheng Shi,Sayed M. Bateni,Fei Meng. 2025

[4]Optimizing Latent Heat Flux Calculation via Composited Thermal Infrared Temperatures. Jiang, Yazhen,Zhao, Jianing,Wu, Anqi,Si, Menglin,Bian, Zunjian,Tang, Ronglin,Li, Zhao-Liang. 2025

[5]Crop Mapping Based on Temporal and Spatial Sample Migrations: A Case Study Over Three Counties in Heilongjiang Province, Northeast China. Zuo, Hao-Nan,Leng, Pei,Li, Yu-Xuan,Song, Qian,Li, Zhao-Liang. 2024

[6]A Time-Constrained and Spatially Explicit AI Model for Soil Moisture Inversion Using CYGNSS Data. Yang, Changzhi,Mao, Kebiao,Shi, Jiancheng,Guo, Zhonghua,Bateni, Sayed M.. 2025

[7]Complex Landscape Rice Extraction Using Integrated Sentinel-2 Spectral-Temporal-Spatial Imagery and a Hybrid Deep Learning Architecture. Liu, Tianjiao,Duan, Si-Bo,Liu, Niantang,Zhang, Youzhi,Chen, Jiankui,Zhang, Li,Li, Dong. 2025

[8]Progress and Perspectives of Crop Yield Forecasting With Remote Sensing: A review. Xiao, Guilong,Huang, Jianxi,Zhuo, Wen,Huang, Hai,Song, Jianjian,Du, Kaiqi,Wang, Jingwen,Yuan, Wenping,Sun, Liang,Zeng, Yelu,Su, Wei,Wu, Genghong,Li, Xuecao,Zheng, Juepeng,Miao, Shuangxi,Gobin, Anne,Zhu, Peng,Jin, Zhenong. 2025

[9]An Operational Split-Window Algorithm for Land Surface Temperature Estimation From Chinese FY-3C VIRR Data. Li, Jia-Hao,Li, Zhao-Liang,Fan, Jinlong,Liu, Niantang. 2024

[10]Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data. Cui, Yuxing,Ji, Yishan,Liu, Rong,Li, Weiyu,Liu, Yujiao,Liu, Zehao,Zong, Xuxiao,Yang, Tao. 2023

[11]Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning. Chen Zhai,Wenxiu Wang,Man Gao,Xiaohui Feng,Shengjie Zhang,Chengjing Qian. 2024

[12]Chlorophyll dynamic fusion based on high-throughput remote sensing and machine learning algorithms for cotton yield prediction. Jiajie Yang,Bowei Xu,Bin Wu,Rumeng Zhao,Le Liu,Fuguang Li,Xiantao Ai,Liqiang Fan,Zuoren Yang. 2025

[13]Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing. Liu Xuefeng,Lyu Qiang,He Shaolan,Yi Shilai,Wang Zhitao,Xie Rangjin,Zheng Yongqiang,Deng Lie,Liu Xuefeng,Lyu Qiang,Deng Lie,Hu Deyu. 2016

[14]Hyperspectral response and quantitative estimation on soil aggregate characters. Xingxing Qiao,Chao Wang,Meichen Feng,Meijun Zhang,Xiaoyan Song,Lujie Xiao,Guangxin Li,Xiuliang Jin,Sumera Anwar,Wude Yang. 2021

[15]Simplified methods of estimating fermentable sugar yield in sweet sorghum [Sorghum bicolor (L.) Moench] stems. Mei-Qi Yue,Zhi Wang,Bao-Qing Dun,Fen-Xia Han,Gui-Ying Li. 2021

[16]An Improved Deep-Learning-Based Precipitation Estimation Algorithm Using Multitemporal GOES-16 Images. Ma, Guangyi,Zhu, Linglong,Zhang, Yonghong,Huang, Jie,Liu, Qi,Sian, Kenny Thiam Choy Lim Kam. 2024

[17]Editorial: Reduction of greenhouse gas emissions from soil. Rui Yang,Wei Lin,Yuanzhi Yao,Yao Zhang. 2025

[18]3D Indoor Scene Geometry Estimation from a Single Omnidirectional Image: A Comprehensive Survey. Meng, Ming,Zhu, Yonggui,Zhao, Yufei,Li, Zhaoxin,Zhu, Zhe. 2025

[19]Mapping vegetation cover of grassland ecosystem for desertification monitoring in Hulun Buir of Inner Mongolia, China. Qin, Zhihao,Li, Wenjuan,Xu, Bin,Qin, Zhihao,Zhu, Yuxia. 2008

[20]Extraction of Sugarcane Planting Area Based on Similarity of NDVI Time Series. Deng, Shiqin,Gao, Maofang,Ren, Chao,Li, Shilei,Liang, Yongjian. 2022

作者其他论文 更多>>