Improving remotely sensed fused ocean data products through cross-sensor calibration

被引:2
|
作者
Lewis, Mark David [1 ]
Amin, Ruhul [1 ]
Gallegos, Sonia [1 ]
Gould, Richard W., Jr. [1 ]
Ladner, Sherwin [1 ]
Lawson, Adam [1 ]
Li, Rong-rong [2 ]
机构
[1] Naval Res Lab, Stennis Space Ctr, MS 39529 USA
[2] Naval Res Lab, Washington, DC 20375 USA
来源
关键词
vicarious calibration; cross-sensor calibration; remote sensing; visible infrared; imaging radiometer suite; moderate resolution imaging; spectroradiometer; VICARIOUS CALIBRATION;
D O I
10.1117/1.JRS.9.095063
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Standard oceanographic processing of the visible infrared imaging radiometer suite (VIIRS) and the moderate resolution imaging spectroradiometer (MODIS) data uses established atmospheric correction approaches to generate normalized water-leaving radiances (nLw) and bio-optical products. In many cases, there are minimal differences between temporally and spatially coincident MODIS and VIIRS bio-optical products. However, due to factors such as atmospheric effects, sensor, and solar geometry differences, there are cases where the sensors' derived products do not compare favorably. When these cases occur, selected nLw values from one sensor can be used to vicariously calibrate the other sensor. Coincident VIIRS and MODIS scenes were used to test this cross-sensor calibration method. The VIIRS sensor was selected as the "base" sensor providing "synthetic" in situ nLw data for vicarious calibration, which computed new sensor gain factors used to reprocess the coincident MODIS scene. This reduced the differences between the VIIRS and MODIS bio-optical measurement. Chlorophyll products from standard and cross-sensor calibrated MODIS scenes were fused with the VIIRS chlorophyll product to demonstrate the ability for this cross-sensor calibration and product fusion method to remove atmospheric and cloud features. This cross-sensor calibration method can be extended to other current and future sensors. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
引用
收藏
页数:19
相关论文
共 50 条
  • [41] A hybrid calibration method for improving hydrological systems using ground-based and remotely-sensed observations
    Moazenzadeh, Roozbeh
    Izady, Azizallah
    Journal of Hydrology, 2022, 615
  • [42] A hybrid calibration method for improving hydrological systems using ground-based and remotely-sensed observations
    Moazenzadeh, Roozbeh
    Izady, Azizallah
    JOURNAL OF HYDROLOGY, 2022, 615
  • [43] Classification trees for improving the accuracy of land use urban data from remotely sensed images
    Shalaby, MT
    Darwish, AA
    MANAGEMENT INFORMATION SYSTEMS 2000: GIS AND REMOTE SENSING, 2000, 1 : 381 - 391
  • [44] An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products
    CHEN Jian
    ZHANG Ren
    WANG Huizan
    AN Yuzhu
    WANG Luhua
    WANG Gongjie
    ActaOceanologicaSinica, 2014, 33 (01) : 48 - 55
  • [45] Improving the efficiency of conservation policies with the use of surrogates derived from remotely sensed and ancillary data
    Vina, Andres
    Chen, Xiaodong
    Yang, Wu
    Liu, Wei
    Li, Yu
    Ouyang, Zhiyun
    Liu, Jianguo
    ECOLOGICAL INDICATORS, 2013, 26 : 103 - 111
  • [46] Improving specific class mapping from remotely sensed data by cost-sensitive learning
    Silva, Joel
    Bacao, Fernando
    Dieng, Maguette
    Foody, Giles M.
    Caetano, Mario
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (11) : 3294 - 3316
  • [47] An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products
    Jian Chen
    Ren Zhang
    Huizan Wang
    Yuzhu An
    Luhua Wang
    Gongjie Wang
    Acta Oceanologica Sinica, 2014, 33 : 48 - 55
  • [48] A GEOLOGICAL EXAMPLE OF IMPROVING CLASSIFICATION OF REMOTELY SENSED DATA USING ADDITIONAL VARIABLES AND A HIERARCHICAL STRUCTURE
    CONRADSEN, K
    GUNULF, J
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1986, 52 (08): : 1181 - 1187
  • [49] Improving soil water representation in the Australian Water Resources Assessment landscape model through the assimilation of remotely-sensed soil moisture products
    Renzullo, L. J.
    Collins, D.
    Perraud, J. -M.
    Henderson, B.
    Jin, H.
    Smith, A.
    20TH INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2013), 2013, : 2883 - 2889
  • [50] An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products
    Chen Jian
    Zhang Ren
    Wang Huizan
    An Yuzhu
    Wang Luhua
    Wang Gongjie
    ACTA OCEANOLOGICA SINICA, 2014, 33 (01) : 48 - 55