A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm

被引:13
|
作者
Hu, Lu [1 ,2 ]
Zhao, Tianjie [3 ]
Ju, Weimin [1 ,4 ]
Peng, Zhiqing [3 ]
Shi, Jiancheng [5 ]
Rodriguez-Fernandez, J. [6 ]
Wigneron, Jean-Pierre [7 ]
Cosh, Michael H. [8 ]
Yang, Kun [9 ]
Lu, Hui [9 ]
Yao, Panpan [3 ]
机构
[1] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China
[2] Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[4] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
[5] Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
[6] Univ Toulouse, CESBIO, CNES, CNRS,INRA,IRD,UPS, Toulouse, France
[7] INRAE, ISPA UMR1391, F-33140 Villenave Dornon, France
[8] USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD USA
[9] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China
基金
美国农业部;
关键词
Soil moisture; Vegetation optical depth; MCCA; AMSR-E; AMSR2; Frequency and polarization dependence; PASSIVE MICROWAVE MEASUREMENTS; RADIOFREQUENCY INTERFERENCE; SCANNING RADIOMETER; SCATTERING ALBEDO; LAND SURFACES; RETRIEVAL; SMOS; SMAP; EMISSION; MODEL;
D O I
10.1016/j.rse.2023.113595
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions.
引用
收藏
页数:18
相关论文
共 15 条
  • [1] A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm
    Hu, Lu
    Zhao, Tianjie
    Ju, Weimin
    Peng, Zhiqing
    Shi, Jiancheng
    Rodríguez-Fernández, Nemesio J.
    Wigneron, Jean-Pierre
    Cosh, Michael H.
    Yang, Kun
    Lu, Hui
    Yao, Panpan
    [J]. Remote Sensing of Environment, 2023, 292
  • [2] Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm
    Zhao, Tianjie
    Shi, Jiancheng
    Entekhabi, Dara
    Jackson, Thomas J.
    Hu, Lu
    Peng, Zhiqing
    Yao, Panpan
    Li, Shangnan
    Kang, Chuen Siang
    [J]. REMOTE SENSING OF ENVIRONMENT, 2021, 257
  • [3] VEGETATION OPTICAL DEPTH RETRIEVAL FROM AMSR-E/AMSR2 OBSERVATIONS USING L-MEB INVERSION
    Wang, Mengjia
    Wigneron, Jean-Pierre
    Sun, Rui
    Ciais, Philippe
    Brandt, Martin
    Liu, Yi
    Frappart, Frederic
    Li, Xiaojun
    Liu, Xiangzhuo
    Fan, Lei
    Fensholt, Rasmus
    [J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 5003 - 5006
  • [4] An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products
    Gao, Huiran
    Zhang, Wanchang
    Chen, Hao
    [J]. REMOTE SENSING, 2018, 10 (11):
  • [5] A PRE-OPERATIONAL ALGORITHM FOR THE RETRIEVAL OF SNOW DEPTH AND SOIL MOISTURE FROM AMSR-E DATA
    Santi, Emanuele
    Pettinato, Simone
    Brogioni, Marco
    Macelloni, Giovanni
    Montomoli, Francesco
    Paloscia, Simonetta
    Pampaloni, Paolo
    [J]. 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 3777 - 3780
  • [6] A METHOD FOR DERIVING LAND SURFACE MOISTURE, VEGETATION OPTICAL DEPTH, AND OPEN WATER FRACTION FROM AMSR-E
    Jones, Lucas A.
    Kimball, John S.
    Podest, Erika
    McDonald, Kyle C.
    Chan, Steven K.
    Njoku, Eni G.
    [J]. 2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5, 2009, : 2218 - +
  • [7] Simultaneous retrieval of global scale Vegetation Optical Depth, surface roughness, and soil moisture using X-band AMSR-E observations
    Karthikeyan, L.
    Pan, Ming
    Konings, Alexandra G.
    Piles, Maria
    Fernandez-Moran, Roberto
    Kumar, D. Nagesh
    Wood, Eric F.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2019, 234
  • [8] A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)
    Panpan Yao
    Hui Lu
    Jiancheng Shi
    Tianjie Zhao
    Kun Yang
    Michael H. Cosh
    Daniel J. Short Gianotti
    Dara Entekhabi
    [J]. Scientific Data, 8
  • [9] MULTI-ALGORITHM ENSEMBLE RECONSTRUCTION OF SURFACE SOIL MOISTURE OVER CHINA FROM AMSR-E
    Lu, Hui
    Gong, Peng
    [J]. 2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 718 - 721
  • [10] A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations
    Wang, Mengjia
    Wigneron, Jean-Pierre
    Sun, Rui
    Fan, Lei
    Frappart, Frederic
    Tao, Shengli
    Chai, Linna
    Li, Xiaojun
    Liu, Xiangzhuo
    Ma, Hongliang
    Moisy, Christophe
    Ciais, Philippe
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 105