Jianguo Tan

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					Towards Digital Earth                                                                                                       1
  — Proceedings of the International Symposium on Digital Earth
Science Press ,1999

      Development of the Cloud Detection and Rehabilitation Operational System
                          Applied to NOAA Satellite Image

                          Jianguo Tan1 Hongmei Zhou1 Xian Lu1 Chongjun Yang2
                                      Shanghai Meteorological Institute, Shanghai, 200030

                                  Institute of Remote Sensing Application, CAS, Beijing, 100101

ABSTRACT In this paper, the techniques and models of cloud detection and cloud rehabilitation applied to
AVHRR image of NOAA meteorological satellite and the basic functions of the operation system on base of
Visual Basic language has been introduced. By experimentation and comparing with the fact it have
acquired the high operation efficiency and precision of the cloud detection and cloud rehabilitation. Thereby,
the cloud detection and cloud rehabilitation operation system can provide the guarantee of credible NOAA/
AVHRR images in the fields of remote sensing application.
KEY WORDS NOAA satellite, cloud detection, cloud rehabilitation, operation system

1. Introduction                                                    than in short infrared band, etc.
The data of NOAA satellite have been paid more                         In addition, the differences of reflectivity
attention and applied to the many fields such as                   between low cloud or thin cloud and underlying
meteorology, hydrology, geology, agriculture and                   mediums.
environment for its characteristics of short period,                   All these are advantage in detecting the cloud.
broad coverage. For reason of weather there is                     So based on the difference between cloud and no
seldom cloudless sky, it is difficult to get a no cloud            cloud area in characteristic in spectrum and the
satellite image covering whole interesting area.                   threshold value are used to distinguish the cloud
Through the methods of cloud detection and                         area and no cloud area.
rehabilitation, we can detect cloud, eliminate cloud
disturbance and enhance the availability of NOAA                   2.1.1. Cloud Detection through Reflectivity in Visible
satellite. It can provide data resource assurance to               Band or Near-Infrared Band
NOAA satellite remote sensing dynamic watching.                    The discriminant is as follow:
Based on many experimental investigation, a cloud                                        Ai  A1i    (1)
detection and rehabilitation optional system has                   Where AI is the reflectivity in visible band(i=1)or
been developed and had a good effect in                            near-infrared band;A1I is the threshold value of
enhancement of data utilization of AVHRR.                          reflectivity.
                                                                       In order to get good effect and automation of
2. Theory and Methods                                              cloud detection, we analyze the reflectivity in visible
                                                                   band, which is sensitive to cloud and find that the
2.1 Methods and Models of Cloud Detection                          reflectivity intensity of cloud affects the reflectivity of
In fact, cloud detection is target recognition and                 underlying mediums that not cover with cloud. The
classification process, which can be used statistical              thicker of the cloud, its reflectivity is higher, while
method and threshold value. In this system                         the reflectivity of underlying medium is higher. The
threshold value has been used in cloud detection                   enhanced reflectivity intensity relates to the cloud
based on experimental analysis of spectrum                         reflectivity. In addition, climate also affects the
characteristic.                                                    reflectivity. In the clear days or the heat days, cloud
     The basic theory of threshold value is that the               and underlying mediums have higher reflectivity
cloud has bigger reflectivity in visible band and near             because of the intense radiation intensity. Through
–infrared band and lower brightness temperature in                 repeated experiments and analyses based on
thermal infrared band than that of other underlying                above characteristics, we obtained the comparative
mediums such as vegetation, soil, and water etc. In                relationship between minimum reflectivity of
generally cloud area has several characteristic as                 underlying mediums and maximum reflectivity of
follow:                                                            cloud in visible band and achieved the automatic
     Bad horizontal homogenization in the top of                   cloud detection in visible band. The relationship
cloud;                                                             between reflectivity and threshold in visible band
     Good relationship between different infrared                  shows in Table 1.
bands in clear day destroyed by cloud;
     Absorption of vapor in long infrared band more
2                Jianguo Tan et al./Development of the Cloud Detection and Rehabilitation Operational System

                                                                 data have little change between same period, near
    Table1 threshold value in cloud detection through            time image. Therefore, considering objectivity of
       reflectivity of visible band( NOAA satellite)             data and continuity of image, it is substituted the
                                                                 cloud data by the cloudless data of same satellite
                      Reflectivity                               orbital, near-time AVHRR image after being
    Minimum      Maximum          Threshold value                reversibly calculated according to variation between
                 65              15                             two image. The method is as follow:
    <3           65<max90        16                                 Given the original cloud image is X , and
                 >90              18                             substituting image is Y
                 45              15
                                                                    fi 
                                                                            xi  x
                                                                                                    x    i
    3min<6      45<max90        16
                 >90              20
                                                                         x max  x min               m
    6           70              23
                                                                    Fi 
                                                                             yi  y
                                                                                                     yi       (i=1,2…n)
                 >70              24
                                                                          y m a  y min
                                                                                x                     n
                                                                 then, the pixel value of no cloud area is :
2.1.2. Cloud Detection through Brightness
Temperature in Infrared Band                                     xi  fi * ( x max  x min)  x
The discriminant is as follow:                                   the    pixel     values     of     cloud      area   is :
                  Ai  A2i (2)                                   xi  Fi * ( x max  x min)  x
Where AI is the brightness temperature in different              where xi,yi,xmax,ymax,xmin,ymin are maximum and
infrared bands (i=3,4,5) 2I is the brightness                    minimum value in X and Y respectively.
temperature threshold.                                               In addition we find the difference of variation
    Cloud has sensitive response in thermal infrared             reflectivity between ocean and land in order to
band, we chose images takes in different seasons                 enhance the precision of cloud rehabilitation, we
to analyze, and found that cloud has same pattern                replace the cloud area in ocean and land
in CH4 and CH5.          For southern China, the                 respectively and get better effect than before.
threshold of thermal infrared temperature is higher
in summer and it is about 10℃, below which is                    3. Main Function of the System
cloud, while in spring and autumn, the threshold is              The main function is as follow:
lower and it is about 5 ℃, while in winter, the                  Image Show: single band image or three-band
threshold is lowest and it is below 0℃. In different             composition image can be show in this system, also
region and different season there are different                  its gray value can be gotten.
threshold. For example, northern China has many                  Calculation of Reflectivity: solar angle correction
lower temperature thresholds than that in southern               of gray value to visible or near-infrared band and its
China. Therefore, In order to detect accurately thin             conversion to reflectivity.
cloud and low cloud, based on the detecting results              Calculation       of    Brightness      Temperature:
of many data different season. We generally                      correction of boundary-dark to middle-infrared or
employed the threshold of temperature which is                   thermal infrared band , calculation of brightness
lower 1℃ to 2℃ than minimum temperature of                       temperature using Planck formula.
ground surface or sea surface of research region in              Cloud Detection:
same season, which obtained good results.                        1) Single channel threshold detection: an image of
                                                                 any channels is chosen, and a gray value threshold
2.2. Cloud Edge Model                                            for cloud pixel is determined, the pixel whose value
In order to eliminate the effect on the edge of cloud            is higher than the threshold id identified as cloud
area, w extend the cloud area, for a cloud area, we              pixel.
give an coefficient of expansion K and get an                    2) Threshold detection through reflectivity of visible
Boolean modular which 0 express cloud area and 1                 channel and near-infrared channel: calculate pixels’
express the no cloud area. While original image                  reflectivity in visible channel and near-infrared
multiply this modular we can get a no cloud edge                 channel, the pixel whose value is higher than a
affection image.                                                 given threshold of reflection is identified as cloud
2.3. Cloud Rehabilitation Model                                  3) Automatic cloud detection through visible
In order to use AVHRR data more effectively, apart               channel: according to the temporal and spatial
from detecting cloud image, the pixels of cloud                  distribution characteristics of cloud, analyze pixels’
should be rehabilitated. We find that the relative               reflectivity in visible channel using method of
                Jianguo Tan et al./Development of the Cloud Detection and Rehabilitation Operational System           3

automatic computer identification, detect cloud with            rehabilitation.
different thresholds.                                           Moulding Board Smoothness : convolution
4) Threshold through brightness temperature                     operation and bilinear interpolation.
detection of infrared channel: calculate pixels’
brightness temperature and real temperatures using              4. Application Case
Planck formula, the pixel whose temperature is                  In order to test the function of this system, we
lowed than a given threshold of temperature is                  chose the NOAA satellite images in September 7
identified as cloud pixel.                                      and September 8,1998, the result of cloud detection
5) Automatic cloud detection of thermal infrared                and rehabilitation is show in figure 1. It shows that
channel: owing to the temperature of cloud in                   through the method of cloud detection and
different region as well as different season, identify          rehabilitation in this system, we have get the
cloud with computer automatically.                              continuous, objective and reliable image so it also
6) Automatic cloud detection with combination of                enhance the serviceability of NOAA /AVHRR data
visible and thermal infrared channels: detection of             greatly.
thermal infrared is mainly sensitive to temperature,
and not sensitive to low cloud and thin cloud, for              5. Conclusion
their temperature are almost equal with that of                 The development of the operational system in cloud
ground objects, but they can be clearly seen in                 detection and rehabilitation applied to NOAA
visible channel. Therefore, considering respective              satellite image provides an objective and advanced
advantage of visible and thermal infrared detectors,            science method to eliminate the Cloud disturbance
we combined the reflection of visible channel with              and enhance the utilization of NOAA satellite data.
temperature      infrared   channel      to   identify          The model of cloud detection and rehabilitation is
automatically cloud.                                            feasible either in theory or in practice. But in fact we
Image Matching: three-band image composition,                   find that the reflectivity of seawater in along the
image enhancement, image stretch, GCP fetch,                    coast which abounded with sand is bigger or equal
re-sampling etc.                                                than that of thin cloud, so we will focus on research
Image Substitution: including cloud area extension,             in distinguishing the sand area and thin cloud area.
ocean       and    land    distinguishment,     cloud
4   Jianguo Tan et al./Development of the Cloud Detection and Rehabilitation Operational System