Journal of The Remote Sensing Society of Japan
Online ISSN : 1883-1184
Print ISSN : 0289-7911
ISSN-L : 0289-7911
Search
Advanced search
OR
Browse
Search
Volume 33, Issue 3
Displaying 1-7 of 7 articles from this issue
Show all abstracts
    • |<
    • <
    • 1
    • >
    • >|
Foreword
Papers
  • Development of an Optimum Water Vapor Product for Land SurfaceTemperature Retrieval from MTSAT data
    Shin AKATSUKA , Kei OYOSHI , Wataru TAKEUCHI
    2013 Volume 33 Issue 3 Pages 173-184
    Published: July 05, 2013
    Released on J-STAGE: January 15, 2014
    DOIhttps://doi.org/10.11440/rssj.33.173
    JOURNAL FREE ACCESS
    Show abstractHide abstract
    In order to retrieve land surface temperature (LST) from satellite remote sensing data accurately, the information of atmospheric condition for atmospheric correction, especially atmospheric water vapor content, is absolutely imperative. When hourly LST is retrieved from Multi-functional Transport Satellite (MTSAT) data, the hourly atmospheric water vapor products and those which covers the area correspond with the MTSAT observation area are needed.
    　In this study, we developed an optimum water vapor product for LST retrieval from MTSAT data. We assessed the accuracy of water vapor products, which are obtained from MTSAT IR data and reanalysis data, and that of LST retrieved from MTSAT data using each water vapor product for atmospheric correction. It was revealed that the reanalysis PW product is better for LST retrieval from MTSAT data, but the accuracy of the PW is reduced in the high altitudes due to the low spatial resolution of the reanalysis products. Therefore, we proposed the method for improving the accuracy of the reanalysis PW product with refinement using both digital elevation model and reanalysis products at each pressure level. The RMSE of LST retrieved from MTSAT data with the refined reanalysis PW product was less than 2.0 K and the spatial distribution of LST can be figured out in more detail using it.
    View full abstract
    Download PDF (714K)
  • Operational Use of Remote Sensing for Harvest Management of Rice
    Eiji SAKAIYA, Yoshio INOUE
    2013 Volume 33 Issue 3 Pages 185-199
    Published: July 05, 2013
    Released on J-STAGE: January 15, 2014
    DOIhttps://doi.org/10.11440/rssj.33.185
    JOURNAL FREE ACCESS
    Show abstractHide abstract
    Harvesting at the optimum timing is critical for production of hig-quality rice. Relationship between the optimum harvesting date (i.e., maturing date) and spectral reflectance was investigated using reflectance data from airborne-sensor and hig-resolution satellite sensors. The reflectance at around 670 nm was best correlated with the date of maturity. A new remote sensing method was developed for predicting the maturing date of rice in each field by combining the reflectance data and the data of the spatial and temporal distribution patterns of maturing date in a region.
    View full abstract
    Download PDF (1665K)
  • Vegetation Analysis of Larix kaempferi Using Radiant Spectra Normalized by Their Arithmetic Mean
    Akiko ONO, Atsuo ONO
    2013 Volume 33 Issue 3 Pages 200-207
    Published: July 05, 2013
    Released on J-STAGE: January 15, 2014
    DOIhttps://doi.org/10.11440/rssj.33.200
    JOURNAL FREE ACCESS
    Show abstractHide abstract
    In reflecting spectra of the earth’s surface, though the magnitude varies with the intensity of sunstroke, the angle of land inclination, the observation angle of the sensors and so on, the shape is less deformed with these effects. From this point of view, we have developed a spectral shape-dependent analysis by normalizing each band-radiance with their mean value. Here, we applied this normalization method to spectral radiometer data for a forest of Larix kaempferi using 3 components of wavelength ranges, Green, Red, and NIR. Each normalized spectral components measured on the ground observation site become almost stable in the sunlight daytime, and their seasonal variations are smooth. Moreover, respective band values of normalized reflectance obtained from ground site and satellite data are almost same. Therefore this method is effective for analysis of vegetation vitality. The normalized reflecting index of NIR is linear to NDVI, and those of Green and Red are sensitive to the variation of leaf color.
    View full abstract
    Download PDF (730K)
Laboratory Introduction
Doctorial Thesis Abstracts
    • |<
    • <
    • 1
    • >
    • >|
feedback
 Top