Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

Snapshot Compressive Imaging is an emerging technology that is based on compressive sensing theory to achieve high-efficiency hyperspectral data acquisition. The core problem of this technology is how to reconstruct 3D hyperspectral data from the 2D snapshot measurement in a fast and high-quality manner. In this paper, we propose a novel deep network, which consists of the symmetric residual module and the non-local spatial-spectral attention module, to learn the reconstruction mapping in a data-driven way. The symmetric residual module uses symmetric residual connections to improve the potential of interaction between convolution operations and further promotes the fusion of local features. The non-local spatial-spectral attention module is designed to capture the non-local spatial-spectral correlation in the hyperspectral image. Specifically, this module calculates the channel attention matrix to capture the global correlations between all of the spectral channels, and it fuses the channel attention attained feature maps and the spatial attention weighted features as the module output, thus both of the spatial-spectral correlations of hyperspectral images can be fully utilized for reconstruction. In addition, a compound loss, including the reconstruction loss, the measurement loss, and the cosine loss, is designed to guide the end-to-end network learning. We experimentally evaluate the proposed method on simulation and real datasets. The experimental results show that the proposed network outperforms the competing methods in terms of the reconstruction quality and running time.

As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program.

An in-depth exploration of fractional vegetation coverage (FVC) changes and driving mechanisms in the Yangtze River Delta (YRD) is crucial for maintaining regional ecological health and achieving sustainable development. We therefore calculate FVC in the YRD from 2012 to 2021 based on MODIS NDVI data, and analyze its spatiotemporal evolution. Multiple regression residual analysis and geographic detector model were used along with various auxiliary data to further explore its driving mechanisms in a hierarchical manner. Finally, the Hurst index was used to forecast future trends in FVC. The results show that: (1) Significant changes in FVC occurred around 2016, rapidly declining at a rate of 8.9×10 ^-3 a ^-1 from 2012 to 2016, followed by fluctuating growth at a rate of 5.2×10 ^-3 a ^-1 thereafter. (2) Although the overall development of FVC in the YRD tends toward stability, the fluctuation was pronounced in the Taihu Lake Basin. Improvement areas were mainly concentrated on both sides of the Yangtze River in the central part of the YRD and in the southern mountainous region. Degraded areas were concentrated in cities north of the Huai River. (3) Dynamic changes in FVC were primarily driven by a combination of climate and human activities, whereas spatial heterogeneity was mainly driven by factors such as elevation, slope, and landform type. The improvement in the explanatory power of anthropogenic factors for spatial heterogeneity when combined with natural factors was significant.

Herein, in situ water reflectance and TSM data obtained from several experiments on the Nanyi Lake from 2018 to 2022 and the Sentinel-3 OLCI satellite synchronization data were used to compare four atmospheric correction methods (FLAASH, 6S, ACOLITE, and C2RCC) and construct an empirical model for TSM inversion in the Nanyi Lake to analyze the spatial and temporal changes in the water quality in the Nanyi Lake from 2018 to 2023. On the Sentinel-3 OLCI data, the C2RCC algorithm showed the highest accuracy and overall performance stability (RMSE: 0.0014–0.0051 Sr ⁻¹ , MAPE: 18.44%–68.47%, and BIAS: from −3.68% to 23.63%). The highest correlation was observed between the three-band ratio (B9 + B18)/B10 and the in situ TSM; the TSM inversion model constructed based on this inversion factor showed the best accuracy for the Nanyi Lake (R ² : 0.76, RMSE: 5.01 mg/L, and MAPE: 28.46%). The spatial and temporal changes in TSM in the Nanyi Lake exhibited significant regularity. Specifically, the TSM was higher in 2018–2019, significantly decreased in 2020, and stabilized in 2021–2023. Owing to the effects of human activities, precipitation, and illumination, seasonal variation in the TSM in the Nanyi Lake was detected, with TSM decreasing in the following order: summer > autumn > spring > winter. Concerning spatial variations, high TSM was observed in the northwest, northeast, and southeast of the Nanyi Lake reclamation area and its surrounding lake area. River confluence and human activities affected the area, leading to significant fluctuations in TSM in 2018–2023.