Full text: Technical Commission VIII (B8)

2. MATHOD 
Clouds are generally characterized by higher reflectance and 
lower temperature than the underlying earth surface. So visible 
and infrared threshold approaches can be used for detection 
cloud cover. However, there is some surface when this 
characterization of clouds is inappropriate, most notably over 
snow and ice. 
Clouds have three radiative properties in the visible and infrared 
spectral range: 
1) Clouds are white in the visible and near infrared; 
2) Clouds are bright in the visible and near infrared; 
3) Clouds are cold in the thermal infrared. 
Water and ice clouds show some distinct extinction features at 
1.6um and 3yum. 
But some surface like snow, ice has spectral properties that are 
similar to the clouds properties. Fig.l shows the repectivity of 
snow and clouds. 
Snow on the ground reflects the sun strongly at one of the 
MODIS wavelength bands (0.6jm) but very little at another 
band (1.6um). Clouds reflect the sun well at both wavelengths. 
1.0 
  
SONVAST 1438 
8 
i 
A 
  
  
  
  
2.5 3.0 
1.5 2.0 
WAVELENGTH rpm) 
Figure 1. Spectral properties of snow and cloud 
But clouds seldom show all the properties at the same time. 
Thin clouds show a portion of the underlying surface spectral 
properties and low clouds are warmer than the background. 
Additionally some surface types, like snow, ice and deserts have 
spectral properties that are similar to clouds. Therefore simple 
threshold algorithms often don’t work. Cloud detection 
algorithms use many different cascaded thresholds. (Saunders, 
1988; King, 1992; Ackermann, 1998) 
2.1 Clouds detection 
Spectral test for cloud detection are based on the fact that 
clouds are highly reflective in the visible, near, and mid-IR 
bands and are cold in the thermal bands (Hulley, 2008). These 
characteristics are used to build thresholds to detect most types 
of clouds. The Landsat-7 Cloud cover algorithm is based on 
Lansat-4 and Landsat-5 and MODIS cloud mask. This 
algorithm uses eight different filters in four bands to distinguish 
clouds and eliminate problematic land surfaces such as snow 
and other highly reflective surface. 
   
The MODIS cloud mask is a science data product that will be 
produced regularly as a Earth Observing System(EOS) standard 
product. Its main purpose is to identify scenes where land, 
ocean and atmosphere products should be retrieved based on 
the amount of obstruction of the surface due to clouds and thick 
aerosol. (Kathleen, 2005) 
MODIS cloud mask algorithm uses a series of sequential tests 
on the passive reflected solar and infrared observations. 
  
Band Spectral 
range(um) 
Application field 
  
Band1 0.620-0.670 Land, cloud, Aerosol boundary 
  
Band6 1.628-1.652 Land, cloud, aerosol 
  
Band8 0.405-0.420 Ocean color, phytoplankton 
  
Band26 | 1.360-1.390 Cirrus, vapor 
  
Band29 | 8.400-8.700 Cloud characteristics, temperature 
  
  
Band31 | 10.780-11.280 | Land cover, Cloud top 
temperature 
  
  
  
  
Tablel.the spectral range and application field of the MODIS 
bands for the cloud detection 
Cloud detection employs the normalized difference cloud index 
(NDCI) defined as the difference of reflectances observed in 
two bands divided by the sum of the two reflectances. 
Here have two type of NDCI. One is defined as the difference of 
reflectances observed in a visible band (0.66jm) and a near 
infrared band (0.936um). The other is defined as the difference 
between 0.66pm and 1.64um. 
Cloud is high repectivity in visible band (0.66um), and it is very 
appropriate for the discriminate the edge of land and cloud at 
this band. For the near infrared band (0.936um) the Spectral 
characteristic of cloud has relations with the vapors from the 
atmosphere. It is the vapors absorption valley. So the NDCI is 
defined as below. 
_ Band0. 66 — band0. 936 
NDCI = 
Band0. 66 + band0. 936 (1) 
  
When the NDCI is positive the surface is cloud; when the NDCI 
is near to zero the surface is soil; when the NDCI is negative the 
surface may be vegetation. 
For the 1.64um band, the repectivity of snow is less than the 
cloud. 
According to the experience a pixel is mapped as bare soil when 
NDCI<0; A pixel is mapped as water when NDCI is between 0 
and 0.1; NDCI of cloud pixel is from 0.1 to 0.5; NDCI value of 
snow is great than 0.5.So it is possible to set a suitable 
threshold to detect cloud and other land cover. 
2.2 Cloud removal methodology 
Cloud removal is different from cloud detection. The results 
from cloud detection are cloud mask. But cloud removals are 
further more. The cloud mask pixel will be replace by its 'real 
pixel', such as soil, vegetation, water, snow or other land cover 
types with a great deal repeating scenes coming from the same 
area. 
     
    
   
    
      
  
    
  
    
   
   
     
   
    
   
   
    
   
  
   
   
   
   
  
    
  
  
  
    
      
   
    
     
    
     
     
   
  
    
   
   
   
   
     
   
General 
and onc 
compos 
compos 
removal 
interval 
weather 
the time 
clear pi: 
equals ‘ 
31 Si 
Our stu 
The ge 
80?0'4: 
3274.5 
Tibet i: 
Qingha 
on the 
on the 
borders 
It has a 
average 
-Mt. Q 
In its s 
of the 
meter | 
Figure 
map. T 
data cc 
The ra 
N40. " 
MODI 
500m :
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.