Evaluation of Improvement Schemes for FY-3B Passive Microwave Soil-Moisture Estimates Retrieved Using the Land Parameter Retrieval Model

被引:1
|
作者
Liu, Haonan [1 ]
Wang, Guojie [2 ]
Hagan, Daniel Fiifi Tawia [1 ,3 ]
Hu, Yifan [2 ]
Nooni, Isaac Kwesi [4 ]
Yeboah, Emmanuel [2 ]
Zhou, Feihong [1 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
[3] Univ Ghent, Hydroclimate Extremes Lab, B-9000 Ghent, Belgium
[4] Wuxi Univ, Sch Atmospher Sci & Remote Sensing, Wuxi 214105, Peoples R China
关键词
soil moisture; land-surface temperature; LPRM; FengYun-3B; open water; vegetation; VEGETATION OPTICAL DEPTH; TRIPLE COLLOCATION; SURFACE TEMPERATURE; PRODUCTS; SCALE; HETEROGENEITY; METHODOLOGY; IMPACT; ERRORS;
D O I
10.3390/rs15215108
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Satellite observations have provided global and regional soil-moisture estimates in the last four decades. However, the accuracy of these observations largely depends on reducing uncertainties in the retrieval algorithms. In this study, we address two challenges affecting the quality of soil-moisture estimates from a widely used soil-moisture-retrieval model, the land parameter retrieval model (LPRM). We studied two improvement schemes that were aimed at reducing uncertainties in open water signals (the LPRMv6_OWF) and vegetation signals (the LPRMv6_Veg), as well as a scheme to reduce their combined impacts (the LPRMv6_OWFVeg) on LPRM-retrieved soil moisture using the FengYun-3B (FY-3B) satellite observations. To assess the impacts of the improvement schemes, we utilized in situ soil moisture from the Jiangsu and Jiangxi provinces in China. We found that the retrievals (Rs) of the LPRMv6_Veg and the LPRMv6_OWFVeg were mainly in the range of 0.2 to 0.5 in Jiangsu and Jiangxi, with increases of 0.1 compared to those of the LPRMv6. The standard deviation (SD) of the LPRMv6_OWFVeg increased in Jiangsu, while the R of the LPRMv6_OWF increased in Jiangsu by 0.05-0.1 compared to that of the LPRMv6, but the SD tended to become worse. In Jiangxi, there was an increase of 0.1 in R. The results show that each of these algorithms improved the accuracy of soil-moisture inversion to some extent, compared to the original algorithm, with the LPRMv6_OWFVeg showing the greatest improvement, followed by the LPRMv6_Veg. The accuracy of both the LPRMv6_OWF and the LPRMv6_OWFVeg decreased to some extent when the open-water fraction (OWF) was greater than 0.2. Full areal extent analyses based on triple collocation showed significant improvements in correlations and minimized errors across different vegetation scenarios over the entire region of China in both the LPRMv6_OWF and the LPRMv6_Veg. However, reduced qualities were found in arid regions in northern China because of the nonlinear relationships between land-surface temperature, vegetation, and soil moisture in the LPRM. These results highlight important lessons for developing comprehensive improvement schemes for soil-moisture retrievals from passive microwave satellite observations.
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页数:22
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共 21 条
  • [1] Evaluation of the FY-3B/MWRI soil moisture product on the central Tibetan Plateau
    Cui, Yaokui
    Long, Di
    Hong, Yang
    Han, Zhongying
    Zeng, Chao
    Hou, Xueyan
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 1655 - 1658
  • [2] Retrieval of snow depth in Northeast China using FY-3B/MWRI passive microwave remote sensing data
    Ren, Ruizhi
    Gu, Lingjia
    Chen, Haipeng
    Cao, Junsheng
    [J]. SATELLITE DATA COMPRESSION, COMMUNICATIONS, AND PROCESSING VIII, 2012, 8514
  • [3] RAINFALL RETRIEVAL OF TROPICAL CYCLONES USING FY-3B MICROWAVE RADIATION IMAGER (MWRI)
    Zhang, Ruan-yu
    Wang, Zhen-zhan
    Zhang, Lan-jie
    Li, Yun
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 550 - 553
  • [4] Evaluation of the Tau-Omega Model for Passive Microwave Soil Moisture Retrieval Using SMAPEx Datasets
    Gao, Ying
    Walker, Jeffrey P.
    Ye, Nan
    Panciera, Rocco
    Monerris, Alessandra
    Ryu, Dongryeol
    Rudiger, Christoph
    Jackson, Thomas J.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (03) : 888 - 895
  • [5] Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data
    Han, Xiao-Jing
    Yao, Na
    Wu, Zihao
    Leng, Pei
    Han, Wenjing
    Chen, Xueyuan
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 10
  • [6] COMPARED PERFORMANCES OF MICROWAVE PASSIVE SOIL MOISTURE RETRIEVALS (SMOS) AND ACTIVE SOIL MOISTURE RETRIEVALS (ASCAT) USING LAND SURFACE MODEL ESTIMATES (MERRA-LAND)
    Al-Yaari, A.
    Wigneron, J-. P.
    Ducharne, A.
    Kerr, Y.
    Wagner, W.
    Reichle, R.
    De lannoy, G.
    Al Bi-tar, A.
    Dorigo, W.
    Parrens, M.
    Fernandez, R.
    Richaume, P.
    Mialon, A.
    [J]. 2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 2463 - 2466
  • [7] FORECASTING SOIL MOISTURE USING A DEEP LEARNING MODEL INTEGRATED WITH PASSIVE MICROWAVE RETRIEVAL
    Kannan, Archana
    Tsagkatakis, Grigorios
    Akbar, Ruzbeh
    Selva, Daniel
    Ravindra, Vinay
    Levinson, Richard
    Nag, Sreeja
    Moghaddam, Mahta
    [J]. 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6112 - 6114
  • [8] Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China
    Jiang, Jinxiong
    Li, Hua
    Liu, Qinhuo
    Wang, Heshun
    Du, Yongming
    Cao, Biao
    Zhong, Bo
    Wu, Shanlong
    [J]. REMOTE SENSING, 2015, 7 (06): : 7080 - 7104
  • [9] MEASURING SURFACE SOIL-MOISTURE USING PASSIVE MICROWAVE REMOTE-SENSING .3.
    JACKSON, TJ
    [J]. HYDROLOGICAL PROCESSES, 1993, 7 (02) : 139 - 152
  • [10] Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements
    Wu, Ying
    Qian, Bo
    Bao, Yansong
    Petropoulos, George P.
    Liu, Xulin
    Li, Lin
    [J]. REMOTE SENSING, 2019, 11 (19)