Himawari-8 (H8), as a geostationary satellite, observing the full disk image of the earth per 10 min, is a very powerful tool to investigate atmospheric aerosol temporal variations. The advanced himawari imager (AHI) onboard H8 has several spectral bands similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosol optical depth (AOD) is thus possible to be retrieved from AHI data using the dark target (DT) method of MODIS. Based on a statistics study of infrared bands of these two satellite instruments, we found that the difference of shortwave infrared bands between AHI (2.3 μm) and MODIS (2.12 μm) in DT area can be ignored for our purpose. Meanwhile, for AHI sensor, the normalized difference vegetation index (NDVI) calculated from 0.86 μm (instead of traditional 1.24 μm) and 2.3 μm can also be a good indicator of surface spectral reflectance. Therefore, first, a new NDVI of AHI sensor is proposed in this paper to improve the estimation of surface reflectance based on DT retrieval method. Then, AOD over DT area was retrieved using a lookup table strategy and tested based on AHI observation over China region from August 2015 to July 2016. All retrieved results are validated against ground-based measurements of the aerosol robotic network and the sun-sky radiometer observation network. The coincident AOD results between AHI retrieval- and ground-based network measurements show good agreements with R² of 0.81. The intercomparison between AHI and MODIS DT AOD products also shows a good agreement with R² of 0.92.

The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%–5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day.

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

China is planning to launch more and more optical remote-sensing satellites with high spatial resolution and multistep gains. Field calibration, the current operational method of satellite in-flight radiometric calibration, still does not have enough capacity to meet these demands. Gaofen-1 (GF-1), as the first satellite of the Chinese High-resolution Earth Observation System, has been specially arranged to obtain 22 images over clean ocean areas using the Wide Field Viewing camera. Following this, Rayleigh scattering calibration was carried out for the visible channels with these images after the appropriate data processing steps. To guarantee a high calibration precision, uncertainty was analyzed in advance taking into account ozone, aerosol optical depth (AOD), seawater salinity, chlorophyll concentration, wind speed and solar zenith angle. AOD and wind speed were found to be the biggest error sources, which were also closely coupled to the solar zenith angle. Therefore, the best sample data for Rayleigh scattering calibration were selected at the following solar zenith angle of 19–22° and wind speed of 5–13 m/s to reduce the reflection contributed by the water surface. The total Rayleigh scattering calibration uncertainties of visible bands are 2.44% (blue), 3.86% (green), and 4.63% (red) respectively. Compared with the recent field calibration results, the errors are −1.69% (blue), 1.83% (green), and −0.79% (red). Therefore, the Rayleigh scattering calibration can become an operational in-flight calibration method for the high spatial resolution satellites.

GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R = 0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R = 0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.