Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert’s edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along “the Belt and Road” route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV.

Cloud detection plays a significant role in optical imagery, but snow has similar spectral characteristics to clouds in visible and near-infrared bands. The multi-feature combined (MFC) algorithm was proposed for the Gaofen-1 (GF-1) wide-field (WFV) imaging system data, with only three visible bands and one near-infrared band. It shows great accuracy but performs poorly in snow-accumulated areas. Therefore, a multi-feature combined cloud-snow differentiation (MFCSD) algorithm introducing temperature and temporal characteristics is proposed to further improve the overall accuracy of cloud detection for GF-1 WFV data. According to the different temperature, spatial, and temporal characteristics of clouds and snow, they can be distinguished by using land surface temperature (LST) data and multi-temporal data. Firstly, the cloud detection part of the MFC algorithm obtains cloud and snow mixed masks. Secondly, the Moderate-resolution Imaging Spectroradiometer (MODIS) LST product and time series of mixed masks are exploited to differentiate snow and clouds. When using the LST data to mark the potential snow cover areas, the average cloud detection accuracy of the verification data increases from 89.91% to 91.52% compared to MFC. After incorporating time series into the process, the average accuracy of cloud detection increases to 93.06%, and that of snow detection is 98.15% higher than MODIS snow cover. Thus, the MFCSD algorithm can well realize cloud detection and cloud-snow differentiation of GF-1 WFV data, and it can be applied to satellite data with similar limited band information with GF-1, such as SPOT-7, Quick Bird, GF-6, etc.

When the aircraft moves through the atmosphere at a very high speed, the aircraft will experience intense friction with the surrounding atmosphere, which causes the air to be ionized into plasma, thus producing a plasma flow field around it. Plasma is constantly generated on the windward side of the aircraft and flows backwards. The velocity of plasma is different from that of the aircraft. In addition to the dispersion characteristics of the plasma and the non-uniform distribution around the target, the plasma flow field will produce the effects of amplitude, frequency and phase modulation on the incident radar wave, which will change the electromagnetic scattering characteristics of the target. In order to reproduce the non-uniform Doppler phenomenon in the flow field under electromagnetic wave irradiation, the plasma echo spectrum is measured experimentally in the JF-10 wind tunnel of the Institute of Mechanics, the Chinese Academy of Sciences. The echo spectrum data of S band and C band are obtained by using a measurement system composed of signal source, circulator, antenna and spectrometer, and based on the measured data the one-dimensional range profile of the plasma coated target is simulated, and the results show that the plasma flow field has a spectrum modulation effect on the target. When the speed of the flow field cannot be compensated for accurately, the spectral modulation effect will further cause the one-dimensional range profile to defocus, which will affect the ability of radar to detect and track the object. The preliminary analysis shows that the effect is caused mainly by the inhomogeneous velocity distribution of plasma flow field and the sudden change of medium parameters when the shock wave arrives. This experiment can provide data support for verifying the electromagnetic characteristics modeling method of plasma flow field.

As the resolution of remote sensing quantitative inversion for albedo, clumping index, canopy height, and other biophysical parameter data is gradually refined, there is an increasing demand for higher spatial resolution bidirectional reflectance distribution function and angular reflectance data. In this study, we applied and evaluated Flexible Spatiotemporal DAta Fusion(FSDAF) to downscale angular reflectance using low-resolution bidirectional reflectance distribution function data (MCD43A1) and high-resolution Sentinel-2 data as inputs. We selected Sentinel-2 orbital overlap regions with various anisotropic flat index types and overpassing times as the study area as to obtain angular reflectance data under different anisotropic and solar-viewing geometry conditions. We found that the fusion method can downscale the angular reflectance with high accuracy (root mean square error <0.04, bias <0.02). The downscaling accuracy was higher in the cross-principal plane than in the principal plane, whereas there was no significant variation in accuracy under different anisotropic flat index conditions. Owing to the lack of angular surface reflectance at larger viewing angles, validation was performed only within a view zenith angle of 10.3°. Further validation using satellites with larger viewing angles, such as Gaofen-6 or unmanned aerial vehicles, can be used to evaluate the accuracy of this method. This study presents a strategy for downscaling angular information, preliminary validation results, and satellite-based evidence of scale effects between the 500–10 m scales of the bidirectional reflectance distribution function.