The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided a large number of high-quality remote sensing data for Earth observation since its launch. Although the reflectance data is calibrated by on-board calibrators, it also needs to be verified by other independent methods in order to ensure the accuracy and reliability of the data. Thus, the Rayleigh scattering method is used to evaluate the MODIS/Aqua reflectance data in our study. In order to obtain high-precision Rayleigh scattering calculation results, the atmospheric and oceanic parameters (AOPs) corresponding to the local time and place, such as wind speed, aerosol optical depth, ozone amount, chlorophyll concentration, and seawater salinity, are put into a radiative transfer model to calculate after a series of screening. The current research selects the pixels with strong Rayleigh scattering characteristics in four days from a global ocean scene. The simulated reflectance is compared with the MYD021KM reflectance product in five visible bands, which presents the total uncertainties as, respectively, 1.39% (412 nm), 0.14% (469 nm), −0.18 % (488 nm), −0.47 % (531 nm), −0.41 % (551 nm). The verification results prove that the MODIS reflectance product remains at a high level of precision without significant deviation after having operated in orbit for 16 years, and the MODIS product has high interband self-consistency. The sensitivity analysis shows that the wind speed and chlorophyll concentration perturbed more to the simulated reflectance than other AOPs of the selected samples. It is believed that the methodology can be applicable to other visible light sensors for validating their reflectance product accurately.
This paper focuses on cross calibrating the GaoFen (GF-1) satellite wide field of view (WFV) sensor using the Landsat 8 Operational Land Imager (OLI) and HuanJing-1A (HJ-1A) hyperspectral imager (HSI) as reference sensors. Two methods are proposed to calculate the spectral band adjustment factor (SBAF). One is based on the HJ-1A HSI image and the other is based on ground-measured reflectance. However, the HSI image and ground-measured reflectance were measured at different dates, as the WFV and OLI imagers passed overhead. Three groups of regions of interest (ROIs) were chosen for cross calibration, based on different selection criteria. Cross-calibration gains with nonzero and zero offsets were both calculated. The results confirmed that the gains with zero offset were better, as they were more consistent over different groups of ROIs and SBAF calculation methods. The uncertainty of this cross calibration was analyzed, and the influence of SBAF was calculated based on different HSI images and ground reflectance spectra. The results showed that the uncertainty of SBAF was <3% for bands 1 to 3. Two other large uncertainties in this cross calibration were variation of atmosphere and low ground reflectance.
Hyper Spectral Imager (HSI) is the first Chinese space-borne hyperspectral sensor aboard the HJ-1A satellite. We have developed a data preprocess flow for HSI images, which includes destriping, atmospheric correction and spectral filtering. In this paper, the product level of HSI image was introduced in the beginning, and a destriping method for HSI level 2 images was proposed. Then an atmospheric correction method based on radiative transfer mechanism was summarized to retrieve ground reflectance from HSI image. Furthermore, a new spectral filter method for ground reflectance spectra after atmospheric correction was proposed based on reference ground spectral database. Lastly, a HSI image acquired over Lake Dali in Inner Mongolia was used to evaluate the effect of the preprocess method. The HSI image after destriping was compared with the original HSI image, which shows that the stripe noise has been removed effectively. Both un-smoothed reflectance spectra and smoothed spectra using the preprocess method proposed in this paper are compared with the reflectance spectral derived with the well-known FLAASH method. The results show that the spectra become much smoother after the application of the spectral filtered algorithm. It was also found that the spectra using this new preprocessing method have similar results as that of the FLAASH method.