Journal of the Japan society of photogrammetry and remote sensing
Online ISSN : 1883-9061
Print ISSN : 0285-5844
ISSN-L : 0285-5844
Volume 58, Issue 5
Displaying 1-10 of 10 articles from this issue
Preface
Original Papers
  • Takashi MIURA, Masao MORIYAMA
    2019 Volume 58 Issue 5 Pages 239-249
    Published: 2019
    Released on J-STAGE: November 01, 2020
    JOURNAL FREE ACCESS

    We propose a method for obtaining validation data for the moderate-resolution-sensor-derived Fire Radiative Power (FRP) products from the high spatial resolution satellite data. This method uses two shortwave infrared channels (1.6 [μm] and 2.2 [μm]) to retrieve FRP validation data. Although fire-contaminated high spatial resolution pixels are mostly saturated because of intense fire radiation, our method exploits the saturated fire pixels for FRP validation data retrieval and as the result, an estimation width is obtained as FRP validation data. In this study, FRP validation data was constructed using the ASTER high resolution data and was evaluated by comparing the simultaneous observed MODIS FRP products. The correlation coefficients between our method derived FRP validation data and the MODIS FRP were approximately between 0.7 and 0.9. The corresponding rates between our method derived FRP validation data and the MODIS FRP were approximately between 0.7 and 0.8.

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  • Naoki NAMBA, Rei SONOBE, Hiroshi TANI, Nobuyuki KOBAYASHI, Kan-ichiro ...
    2019 Volume 58 Issue 5 Pages 250-254
    Published: 2019
    Released on J-STAGE: November 01, 2020
    JOURNAL FREE ACCESS

    Crop maps are useful for agricultural field management and synthetic aperture radar data are attractive for generating crop classification because of their all-weather, all-day imaging capability. Additionally, classification algorithms are essential for generating accurate and multi-Grained Cascade Forest, which is also called ‘deep forest', was developed and its high performances have been shown for pattern recognition, voice recognition and so on. In this study, the capability of TerraSAR-X (including TanDEM-X) dual-polarimetric data for crop classification in the Tokachi Plain, Japan was investigated and the comparison of three different classification algorithms including classification and regression tree, random forests and deep forest was conducted.

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  • Erika SUGIYAMA, Rei SONOBE, Hiroshi TANI, Nobuyuki KOBAYASHI, Kan-ichi ...
    2019 Volume 58 Issue 5 Pages 255-259
    Published: 2019
    Released on J-STAGE: November 01, 2020
    JOURNAL FREE ACCESS

    Crop growth monitoring techniques using remotely sensed data have been required for precise management of crop production. In this study, multi-temporal TerraSAR-X (including TanDEM-X) dual-polarimetric data were used for monitoring the growth of beans and beetroot. In addition to gamma naught values, polarimetric parameters were calculated using m-chi decomposition and dual polarization entropy/alpha decomposition. The results showed that the gamma naught of VV polarization and two polarimetric parameters of the m-chi decomposition (single-or odd-bounce (Odd) and randomly oriented (Rnd) scattering) from X-band SAR data possess potential for monitoring crop growth.

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  • Asuka HAYASHI, Rei SONOBE, Tomohito SANO, Hideki HORIE
    2019 Volume 58 Issue 5 Pages 260-264
    Published: 2019
    Released on J-STAGE: November 01, 2020
    JOURNAL FREE ACCESS

    Green tea-flavored sweets have become popular and then some techniques, such as shading treatments, have been developed for increasing chlorophyll content, which is important for improving tea leaf appearance. Chlorophyll content estimation is one of the most common applications of hyperspectral remote sensing, however, previous studies were based on measurements under relatively low stress conditions. The PROSPECT model is one of the most famous radiative transfer models and has been widely used for retrieving chlorophyll, carotenoid, or dry matter content. In this study, the performances of the three versions, which can estimate chlorophyll content from reflectance, were compared. Using PROSPECT-D, root mean square errors of 7.12μg/cm2 was achieved and then it could be a strong tool for assessing the qualities of shade grown tea.

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  • Rei SONOBE, Tomohito SANO, Hideki HORIE
    2019 Volume 58 Issue 5 Pages 265-269
    Published: 2019
    Released on J-STAGE: November 01, 2020
    JOURNAL FREE ACCESS

    Theanine is the most abundant free amino acid in tea leaves (Camellia sinensis) and it is also one of important factors for assessing the quality of tea ; thus, developing an in-situ method to monitor theanine is useful for agricultural management. Some hyperspectral remote sensing techniques, especially spectral indices, have been applied for assessing vegetation properties such as pigment content and water content. In this study, searching for new indices was attempted based on hyperspectral reflectance. The newly identified index, which is expressed as a differential type of index using reflectance at 1735 nm and 1755 nm, possessed a great performance, achieving a root mean square error with leave-one-out cross validation of 0.065 mg/cm2.

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