## 3 Methodology  
### Data Pre-Processing  
Radiometric calibration converts Digital Numbers (DN) recorded in multi-spectral images into radiance and Top Of Atmosphere (TOA) reflectance. It is a fundamental process to eliminate the influence of the attenuation of the sensor photoelectric system [15]. It is especially important for images acquired from the constellation, because of the different attenuation rates of sensors [28]. The radiometric calibration of HJ-1A/B images was conducted by using Equation (1) to Equation (3) [34].  
\[L_{\lambda}=\frac{DN}{G}+B \tag{1}\]  
\[R_{\lambda}=\frac{\pi\times L_{\lambda}\times d_{r}^{2}}{ESUN_{\lambda}\times \text{cos}\theta} \tag{2}\]  
\[d_{r}=1+0.033\text{cos}\left(DOY\cdot\frac{2\pi}{365}\right) \tag{3}\]  
where, \(L_{\lambda}\) is the upward radiance (\(W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}\)), G (\(W^{-1}\cdot m^{2}\cdot sr\cdot\mu m\)) is the gain of the absolute calibration coefficient, B (\(W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}\)) is the calibration offset, \(ESUN_{\lambda}\) (\(W\cdot m^{-2}\cdot\mu m^{-1}\)) is the solar exo-atmospheric spectral irradiance, \(\theta\) is the solar zenith angle, \(d_{r}\) is the solar-terrestrial astronomical unit distance, and the Day of the Year (DOY). The annual calibration coefficients for Esun, G and B can be obtained from the website of CRESDA.  
Sub-pixel geolocation accuracy is required to generate composites [35]. Using the auto-registration and orthorectification algorithm for HJ-1A/B images [26], geometric registration and orthorectification were carried out for all the acquired HJ-1A/B images. Using the Global Land Survey (GLS) Landsat images from the year 2005 with geolocation error less than 30 m as base images [36], the HJ-1A/B geometric registration algorithm employed the area-based matching method to automatically select tie points between the base and HJ-1A/B images. The elevation data from the Shuttle Radar Topography Mission (SRTM) with spatial resolution of 90 m was used to correct the parallax error due to the local topographic relief. For the images with the cloud cover, the distribution of control points selected by the auto-algorithm will be uneven, and therefore the geometric residuals might still be bigger than ones in the cloudless images. More control points in these images were selected by hand so that the final geometric residuals of all the images met the requirement for sub-pixel geometric accuracy.  
Cloud masking for all of the images has been implemented according to [28,37]. The cloud detection algorithm was designed to take full advantage of the temporal and spatial contextual information to overcome the shortcomings of the limited spectral bands. The algorithm consisted of three major stages, which were the initial spectral text, the multi-temporal difference and the synthetic texture information test. The initial spectral test was used for detecting both clouds and bright pixels (such as snow) together based on the whiteness and haze optimized transformation spectral tests [38,39]. Then, the spectral differences between the HJ-1A/B images and a cloud-free reference image were calculated to discriminate most cloud from those bright pixels. In the third stage, the synthetic spatial texture information calculated from the distance of regional covariance matrix (RCM) was used to correct the commission and omission errors. Cloud shadow was detected using a geometry-based cloud shadow projection method proposed in [13]. In this method, cloud shadow was detected based on the shadow physical characteristics (scattering differences between the short wavelength and NIR) combined with the geometry. Since there was no well-accepted atmospheric correction method available for HJ-1A/B images, no atmospheric correction was performed.