The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations

被引:41
作者
Li, Xiaojun [1 ]
Wigneron, Jean-Pierre [1 ]
Frappart, Frederic [1 ]
De Lannoy, Gabrielle [2 ]
Fan, Lei [3 ]
Zhao, Tianjie [4 ]
Gao, Lun [5 ]
Tao, Shengli [6 ]
Ma, Hongliang [7 ]
Peng, Zhiqing [4 ]
Liu, Xiangzhuo [1 ]
Wang, Huan [1 ,6 ]
Wang, Mengjia [8 ]
Moisy, Christophe [1 ]
Ciais, Philippe [9 ]
机构
[1] Univ Bordeaux, INRAE, UMR 1391 ISPA, F-33140 Villenave Dornon, France
[2] Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Heverlee, Belgium
[3] Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China
[4] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[5] Univ Illinois, Inst Sustainabil Energy & Environm, Agroecosyst Sustainabil Ctr, Urbana, IL 61801 USA
[6] Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Key Lab Earth Surface Proc,Minist Educ, Beijing 100871, Peoples R China
[7] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China
[8] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[9] Univ Paris Saclay, Lab Sci Climat & Environm, CEA, CNRS,UVSQ, Gif Sur Yvette, France
基金
中国国家自然科学基金;
关键词
SMOS; SMAP; SMAP-IB; L-band; Merging; Soil moisture; Vegetation optical depth; EFFECTIVE SCATTERING ALBEDO; MICROWAVE EMISSION; LAND SURFACES; DATA SETS; CALIBRATION; MODEL; ASSIMILATION; VALIDATION; ROUGHNESS; REGIONS;
D O I
10.1016/j.rse.2022.113272
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
ESA's Soil Moisture Ocean Salinity (SMOS, since 2009) and NASA's Soil Moisture Active Passive (SMAP, since 2015) are the only two space-borne L-band radiometer missions currently in orbit, which provide key infor-mation on global surface soil moisture (SM) and vegetation water content (via the vegetation optical depth, VOD). However, to date very few studies considered merging SMOS and SMAP for both SM and VOD retrievals simultaneously. This study presents the first global long-term and continuous SM and L-band VOD (L-VOD) dataset retrieved after merging the SMOS and SMAP brightness temperature (TB) observations, called the SMOS-SMAP-INRAE-BORDEAUX or SMOSMAP-IB product. We first developed a fitted SMOS TB dataset at a fixed incidence angle of 40 degrees, and next applied a monthly linear rescaling of SMAP TB to SMOS TB for each polarization to produce a merged SMOS/SMAP TB (theta = 40 degrees) dataset. The retrievals were then based on a mono-angular retrieval algorithm sharing a similar forward model with the SMOS-IC and the official SMOS retrieval algo-rithms. Results showed that the inter-calibration approach we used here could effectively remove the bias be-tween the SMAP TB and fitted SMOS TB, with bias values reduced to 0.01 K (-0.02 K) compared to 3.45 K (1.65 K) for V (H) polarization before inter-calibration. The SMOSMAP-IB SM and L-VOD retrievals based on this new inter-calibrated SMOS/SMAP TB led to metrics that were equally good or better than those of other products (i.e., ESA CCI, SMOS-IC and the official SMAP products). When considering only long duration products, SMOSMAP-IB SM retrievals exhibited (i) the highest overall median R value of 0.72 with in-situ data from ISMN (International Soil Moisture Network) during 2013-2018, followed by SMOS-IC (R = 0.68) and CCI (R = 0.67), and (ii) the same smallest ubRMSD values as CCI (ubRMSD = 0.057 m3/m3 vs 0.061 m3/m3 for SMOS-IC). L-VOD retrievals from SMOSMAP-IB were found to have comparable spatial and temporal skills to SMOS-IC. Spatially, they both correlated well with aboveground biomass (R = 0.87), and temporally, they both showed a good representation of the short vegetation NDVI signal and of the forest area loss in the Brazilian Amazon from 2011 to 2019. Developing SMOSMAP-IB is a step forward towards building a time-continuous L-band SM and VOD products in response to the possible failure of one of the SMOS or SMAP sensors in the future.
引用
收藏
页数:21
相关论文
共 98 条
  • [1] The global SMOS Level 3 daily soil moisture and brightness temperature maps
    Al Bitar, Ahmad
    Mialon, Arnaud
    Kerr, Yann H.
    Cabot, Francois
    Richaume, Philippe
    Jacquette, Elsa
    Quesney, Arnaud
    Mahmoodi, Ali
    Tarot, Stephane
    Parrens, Marie
    Al-Yaari, Amen
    Pellarin, Thierry
    Rodriguez-Fernandez, Nemesio
    Wigneron, Jean-Pierre
    [J]. EARTH SYSTEM SCIENCE DATA, 2017, 9 (01) : 293 - 315
  • [2] Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements
    Al-Yaari, A.
    Wigneron, J. -P.
    Dorigo, W.
    Colliander, A.
    Pellarin, T.
    Hahn, S.
    Mialon, A.
    Richaume, P.
    Fernandez-Moran, R.
    Fan, L.
    Kerr, Y. H.
    De Lannoy, G.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2019, 224 : 289 - 303
  • [3] Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets
    Al-Yaari, A.
    Wigneron, J. -P.
    Kerr, Y.
    Rodriguez-Fernandez, N.
    O'Neill, P. E.
    Jackson, T. J.
    De Lannoy, G. J. M.
    Al Bitar, A.
    Mialon, A.
    Richaume, P.
    Walker, J. P.
    Mahmoodi, A.
    Yueh, S.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2017, 193 : 257 - 273
  • [4] Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations
    Al-Yaari, A.
    Wigneron, J. P.
    Kerr, Y.
    de Jeu, R.
    Rodriguez-Fernandez, N.
    van der Schalie, R.
    Al Bitar, A.
    Mialon, A.
    Richaume, P.
    Dolman, A.
    Ducharne, A.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2016, 180 : 453 - 464
  • [5] Al-Yaari A., 2020, US GLOB CHANG BIOL, V00, P1
  • [6] [Anonymous], 1988, FAO/UNESCO Soil Map of the World. Revised Legend
  • [7] Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled- and Ground-Based Measurements
    Bai, Li
    Lv, Xin
    Li, Xiaojun
    [J]. REMOTE SENSING, 2019, 11 (24)
  • [8] A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data
    Bai, Yu
    Zhao, Tianjie
    Jia, Li
    Cosh, Michael H.
    Shi, Jiancheng
    Peng, Zhiqing
    Li, Xiaojun
    Wigneron, Jean -Pierre
    [J]. REMOTE SENSING OF ENVIRONMENT, 2022, 280
  • [9] Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France
    Barbu, A. L.
    Calvet, J. -C.
    Mahfouf, J. -F.
    Lafont, S.
    [J]. HYDROLOGY AND EARTH SYSTEM SCIENCES, 2014, 18 (01) : 173 - 192
  • [10] Atmospheric conservation properties in ERA-Interim
    Berrisford, P.
    Kallberg, P.
    Kobayashi, S.
    Dee, D.
    Uppala, S.
    Simmons, A. J.
    Poli, P.
    Sato, H.
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2011, 137 (659) : 1381 - 1399