New Generation and Old Generation Hyperspectral Remote Sensing Data and their Comparisons with Multispectral Data in the Study of Global Agriculture and Vegetation

被引:0
|
作者
Thenkabail, Prasad S. [1 ]
Aneece, Itiya [1 ]
Teluguntla, Pardhasaradhi [1 ]
Oliphant, Adam [1 ]
Foley, Daniel [1 ]
机构
[1] US Geol Survey, Flagstaff, AZ 86001 USA
关键词
hyperspectral; DESIS; PRISMA; Hyperion; Machine Learning;
D O I
10.1109/IGARSS46834.2022.9883556
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Great advances in remote sensing are taking place with new generation of spaceborne hyperspectral sensors such as the DESIS and PRISMA which are already acquiring data for over a year now and the upcoming launch of EnMAP. Understanding the characteristics of these data for a wide array of applications is of great importance to advance the twenty-first century satellite remote sensing. In this paper we will make a comprehensive assessment of new generation hyperspectral sensors such as the DESIS and the PRISMA and compare the same with the older generation hyperspectral sensors such as the Hyperion as well as with various multispectral sensors in study of major world agricultural crops.
引用
收藏
页码:5744 / 5745
页数:2
相关论文
共 50 条
  • [1] New Generation Hyperspectral Data for Quantum Leap in Remote Sensing Science for Agriculture
    Thenkabail, Prasad S.
    Aneece, Itiya
    Teluguntla, Pardhasaradhi
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2024, 90 (11): : 661 - 663
  • [2] Estimating canopy water content of wetland vegetation using hyperspectral and multispectral remote sensing data
    Sun, Yonghua
    Wang, Yihan
    Huang, Jin
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVII, 2015, 9637
  • [3] Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data
    Li, Zhaoqin
    Guo, Xulin
    PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT, 2016, 40 (02): : 276 - 304
  • [4] Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data
    Das, Prabir Kumar
    Seshasai, M. V. R.
    GEOCARTO INTERNATIONAL, 2015, 30 (06) : 686 - 700
  • [5] Global partitioning of runoff generation mechanisms using remote sensing data
    Lucey, Joseph T. D.
    Reager, John T.
    Lopez, Sonya R.
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2020, 24 (03) : 1415 - 1427
  • [6] Research on Technology of the Global Geographic Data Sets Generation Based on the Original Remote Sensing Data
    Feng, Wenquan
    Zhou, Gan
    Fang, Yong
    MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8, 2012, 433-440 : 4229 - 4234
  • [7] Study on oil and gas exploration in sparse vegetation areas by hyperspectral remote sensing data
    Li, Qianqian
    Chen, Xiaomei
    Liu, Xing
    Mao, Bingjing
    Ni, Guoqiang
    Chinese Optics Letters, 2012, 10 (SUPPL.1):
  • [8] SCIAMACHY - A new-generation of hyperspectral remote sensing instrument
    Mager, R
    Fricke, W
    Burrows, JP
    Frerick, J
    Bovensmann, H
    SPECTROSCOPIC ATMOSPHERIC MONITORING TECHNIQUES, 1997, 3106 : 84 - 94
  • [9] JOINT NONNEGATIVE MATRIX FACTORIZATION FOR HYPERSPECTRAL AND MULTISPECTRAL REMOTE SENSING DATA FUSION
    Karoui, Moussa Sofiane
    Deville, Yannick
    Kreri, Sarah
    2013 5TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2013,
  • [10] New progress in study on vegetation models for hyperspectral remote sensing
    Tong, QX
    Zhao, YC
    Zhang, X
    Zhang, B
    Zheng, LF
    HYPERSPECTRAL REMOTE SENSING OF THE LAND AND ATMOSPHERE, 2001, 4151 : 143 - 152