POTENTIAL OF SENTINEL-1 FOR HIGH-RESOLUTION SOIL MOISTURE MONITORING

被引:13
|
作者
Gruber, Alexander [1 ]
Wagner, Wolfgang [1 ]
Hegyiova, Alena [1 ]
Greifeneder, Felix [1 ]
Schlaffer, Stefan [1 ]
机构
[1] Vienna Univ Technol, Dept Geodesy & Geoinformat, Vienna, Austria
关键词
Soil Moisture; Synthetic Aperture Radar; Sentinel-1; ERS SCATTEROMETER; MODE DATA; RETRIEVAL;
D O I
10.1109/IGARSS.2013.6723717
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Soil moisture is a crucial variable for a large variety of applications with different requirements on the spatial and temporal resolution of the observations. Coarse-scale instruments can provide data operationally with a nearly-daily global coverage at a spatial resolution of several hundreds of square kilometers, whereas SAR instruments provide a spatial resolution of less than one hectare to about one square kilometer but with a revisit time varying from several days to several months. This study uses coarse-scale MetOp ASCAT data and higher resolution Envisat ASAR data taken in the GM mode and the WS mode together with in-situ measurements to demonstrate (i) the potential of Sentinel-1 to capture very local soil moisture variations and (ii) the expected impact of the significantly improved radiometric accuracy of Sentinel-1 compared to existing soil moisture missions.
引用
收藏
页码:4030 / 4033
页数:4
相关论文
共 50 条
  • [1] SENTINEL-1 HIGH RESOLUTION SOIL MOISTURE
    Mattia, F.
    Balenzano, A.
    Satalino, G.
    Lovergine, F.
    Loew, A.
    Peng, J.
    Wegmuller, U.
    Santoro, M.
    Cartus, O.
    Dabrowska-Zielinska, K.
    Musial, J.
    Davidson, M. W. J.
    Yueh, S.
    Kim, S.
    Das, N.
    Colliander, A.
    Johnson, J.
    Ouellette, J.
    Walker, J.
    Wu, X.
    McNairn, H.
    Merzouki, A.
    Powers, J.
    Caldwell, T.
    Entekhabi, D.
    Cosh, M.
    Jackson, T.
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 5533 - 5536
  • [2] Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data
    Ma, Chunfeng
    Li, Xin
    McCabe, Matthew F.
    [J]. REMOTE SENSING, 2020, 12 (14)
  • [3] USING SENTINEL-1 DATA FOR MONITORING OF SOIL MOISTURE
    Garkusha, Igor N.
    Hnatushenko, Volodymyr V.
    Vasyliev, Volodymyr V.
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 1656 - 1659
  • [4] On the potential of Sentinel-1 for sub-field scale soil moisture monitoring
    van Hateren, T. C.
    Chini, M.
    Matgen, P.
    Pulvirenti, L.
    Pierdicca, N.
    Teuling, A. J.
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 120
  • [5] Sentinel-1 soil moisture at 1 km resolution: a validation study
    Balenzano, Anna
    Mattia, Francesco
    Satalino, Giuseppe
    Lovergine, Francesco P.
    Palmisano, Davide
    Peng, Jian
    Marzahn, Philip
    Wegmuller, Urs
    Cartus, Oliver
    Dabrowska-Zielinska, Katarzyna
    Musial, Jan P.
    Davidson, Malcolm W. J.
    Pauwels, Valentijn R. N.
    Cosh, Michael H.
    McNairn, Heather
    Johnson, Joel T.
    Walker, Jeffrey P.
    Yueh, Simon H.
    Entekhabi, Dara
    Kerr, Yann H.
    Jackson, Thomas J.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2021, 263
  • [6] High resolution mapping of soil moisture in agriculture based on Sentinel-1 interferometric data
    Conde, Vasco
    Catalao, Joao
    Nico, Giovanni
    Benevides, Pedro
    [J]. REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XX, 2018, 10783
  • [7] An Operational High Resolution Soil Moisture Retrieval Algorithm Using Sentinel-1 Images
    Baghdadi, Nicolas
    El Hajj, Mohammad
    Zribi, Mehrez
    [J]. 2019 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM - SPRING (PIERS-SPRING), 2019, : 4086 - 4092
  • [8] Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images
    Lee, Soo-Jin
    Choi, Chuluong
    Kim, Jinsoo
    Choi, Minha
    Cho, Jaeil
    Lee, Yangwon
    [J]. REMOTE SENSING, 2023, 15 (16)
  • [9] High-Resolution Mapping of Soil Moisture by AMSR2 Data Disaggregation Based on Sentinel-1 and Machine Learning
    Santi, Emanuele
    Baroni, Fabrizio
    Fontanelli, Giacomo
    Palchetti, Enrico
    Paloscia, Simonetta
    Pettinato, Simone
    Pilia, Simone
    Ramat, Giuliano
    Santurri, Leonardo
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 15077 - 15088
  • [10] Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1
    Hornacek, Michael
    Wagner, Wolfgang
    Sabel, Daniel
    Hong-Linh Truong
    Snoeij, Paul
    Hahmann, Thomas
    Diedrich, Erhard
    Doubkova, Marcela
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (04) : 1303 - 1311