THE JPL SMAP SEA SURFACE SALINITY ALGORITHM

被引:0
|
作者
Fore, A. [1 ]
Yueh, S. [1 ]
Tang, W. [1 ]
Hayashi, A. [1 ]
机构
[1] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA
关键词
WIND;
D O I
10.1109/igarss.2019.8898359
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and have been directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. In this talk we present the SMAP Sea Surface Salinity (SSS) dataset and algorithm.
引用
收藏
页码:7920 / 7923
页数:4
相关论文
共 50 条
  • [21] COASTAL SEA SURFACE SALINITY RETRIEVAL ANALYSIS FROM SMAP MISSION USING MACHINE LEARNING
    Lv, YanFang
    Zhang, YiFan
    Liu, JingYi
    Zhang, LanJie
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3981 - 3983
  • [22] Validation of Satellite SMAP Sea Surface Salinity using Ieodo Ocean Research Station Data
    Park, Jae-Jin
    Park, Kyung-Ae
    Kim, Hee-Young
    Lee, Eunil
    Byun, Seong
    Jeong, Kwang-Yeong
    JOURNAL OF THE KOREAN EARTH SCIENCE SOCIETY, 2020, 41 (05): : 469 - 477
  • [23] Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application
    Menezes, Viviane V.
    REMOTE SENSING, 2020, 12 (03)
  • [24] An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf
    Rajabi-Kiasari, Saeed
    Hasanlou, Mahdi
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (08) : 3221 - 3242
  • [25] Spatial and temporal scales of sea surface salinity in the tropical Indian Ocean from SMOS, Aquarius and SMAP
    Bao, Senliang
    Wang, Huizan
    Zhang, Ren
    Yan, Hengqian
    Chen, Jian
    JOURNAL OF OCEANOGRAPHY, 2020, 76 (05) : 389 - 400
  • [26] Spatial and temporal scales of sea surface salinity in the tropical Indian Ocean from SMOS, Aquarius and SMAP
    Senliang Bao
    Huizan Wang
    Ren Zhang
    Hengqian Yan
    Jian Chen
    Journal of Oceanography, 2020, 76 : 389 - 400
  • [27] Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches
    Jang, Eunna
    Kim, Young Jun
    Im, Jungho
    Park, Young-Gyu
    GISCIENCE & REMOTE SENSING, 2021, 58 (01) : 138 - 160
  • [28] A Development for Sea Surface Salinity Algorithm Using GOCI in the East China Sea
    Kim, Dae-Won
    Kim, So-Hyun
    Jo, Young-Heon
    KOREAN JOURNAL OF REMOTE SENSING, 2021, 37 (05) : 1307 - 1315
  • [29] SMOS sea surface salinity prototype processor:: Algorithm validation
    Zine, S.
    Boutin, J.
    Font, J.
    Talone, M.
    Gabarro, C.
    Reul, N.
    Tenerelli, J.
    Waldteufel, P.
    Petitcolin, F.
    Vergely, J. -L.
    IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, : 3955 - +
  • [30] Quantification of Aquarius, SMAP, SMOS and Argo-Based Gridded Sea Surface Salinity Product Sampling Errors
    Fournier, Severine
    Bingham, Frederick M.
    Gonzalez-Haro, Cristina
    Hayashi, Akiko
    Ulfsax Carlin, Karly M.
    Brodnitz, Susannah K.
    Gonzalez-Gambau, Veronica
    Kuusela, Mikael
    REMOTE SENSING, 2023, 15 (02)