The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), provide inconsistent data. Hence, the application of NTL for long-term analysis is hampered. This study constructed a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light to solve this problem. First, NTL data were processed to obtain vicarious site across China. By comparing different candidate models, it is discovered the Biphasic Dose Response (BiDoseResp) model, which is a weighted combination of sigmoid functions, can best perform the regression between DMSP-OLS and logarithmically transformed NPP-VIIRS. The coefficient of determination of BiDoseResp model reaches 0.967. It’s residual sum of squares is $6.136\times {10}^5$ , which is less than $6.199\times {10}^5$ of Logistic function. After obtaining the BiDoseResp-calibrated VIIRS (BDRVIIRS), we smoothed it by a filter with optimal parameters to maximize the consistency. The result shows that the consistency of NTL data is greatly enhanced after calibration. In 2013, the correlation coefficient between DMSP-OLS and original NPP-VIIRS data in the China region is only 0.621, while that reaches to 0.949 after calibration. Finally, a consistent NTL dataset of China from 1992 to 2018 was produced. When compared with the existing methods, our method is applicable to the full dynamic range of DMSP-OLS. Besides, it is more suitable for country or larger scale areas. It is expected that this method can greatly facilitate the development of research that is based on the historical NTL archive.

Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie’s landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing’s landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui‘s landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters.

The Polarization CrossFire (PCF) suite onboard the Chinese GaoFen-5(02) satellite has been sophisticatedly composed by the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC). Among them, DPC is a multi-angle sequential measurement polarization imager, while POSP is a cross-track scanning simultaneous polarimeter with corresponding radiometric and polarimetric calibrators, which can theoretically be used for cross comparison and calibration with DPC. After the data preprocessing of these two sensors, we first select local homogeneous cluster scenes by calculating the local variance-to-mean ratio in DPC’s Level 1 product projection grids to reduce the influence of scale differences and geometry misalignment between DPC and POSP. Then, taking the observation results after POSP data quality assurance as the abscissa and taking the DPC observation results under the same wavelength band and geometric conditions as the same ordinate, a two-dimensional radiation/polarization feature space is established. Results show that the normalized top of the atmosphere (TOA) radiances of DPC and POSP processed data at the nadir are linearly correlated. The normalized TOA radiance root mean square errors (RMSEs) look reasonable in all common bands. The DPC and POSP normalized radiance ratios in different viewing zenith angle ranges at different times reveal the temporal drift of the DPC relative radiation response. The RMSEs, mean absolute errors (MAEs), relative errors (REs), and scatter percentage of DPC degree of linear polarization (DoLP) falling within the expected error (EE = ±0.02) of POSP measured DoLP are better than 0.012, 0.009, 0.066, and 91%, respectively.

The directional polarimetric camera (DPC) is a Chinese satellite sensor with a large field of view (FOV) (±50° both along-track and cross-track) and a high spatial resolution (about 3.3 km at nadir) that operates in a sun-synchronous orbit. It is a difficult task to calibrate the in-flight relative radiometric variation of the sensors with such a wide FOV. In this study, a new method based on Rayleigh scattering over the ocean is developed to estimate the radiometric sensitivity variation over the whole FOV of DPC. Firstly, the theoretical uncertainty of the method is analyzed to calibrate the relative radiometric response. The calibration uncertainties are about 2–6.9% (depending on the wavelength) when the view zenith angle (VZA) is 0° and decrease to about 1–3.8% when VZA increases to 70°. Then, the method is applied to evaluate the long-term radiometric drift of the DPC. It is found that the radiometric response of DPC/GaoFen-5 over the whole FOV is progressively drifting over time. The sensitivity at shorter bands decreases more strongly than longer bands, and at the central part of the optics decreases more strongly than the marginal part. During the 14 months (from March 2019 to April 2020) of operational running in-orbit, the DPC radiometric responses of 443 nm, 490 nm, 565 nm, and 670 nm bands drifted by about 4.44–23.08%, 4.75–16.22%, 3.86–9.81%, and 4.7–16.86%, respectively, from the marginal to the central part of the FOV. The radiometric sensitivity has become more stable since January 2020. The monthly radiometric drift is separated into the relative radiometric part and the absolute radiometric part. The relative radiometric drift of DPC is found to be smoothly varying with VZA, which can be parameterized as a polynomial function via VZA. At last, the temporal radiometric drift of DPC/GaoFen-5 is corrected by combining the relative and absolute radiometric coefficients. The correction is convincing by cross calibration with MODIS/Aqua observation over the desert sites and improving the aerosol retrievals. The Rayleigh method in this study is efficient for the radiometric sensitivity calibration of wide FOV satellite sensors.

The directional polarimetric camera (DPC) is a polarization sensor with the characteristics of ultra-wide-angle and low-distortion imaging. The multi-angle polarization information is helpful to obtain the spatial distribution of target radiation, and multiple data fusion relies on the non-uniformity calibration of image plane. The non-uniformity consists of many factors such as lens, detector assembly, spatial stray light, etc. The single correction method can not distinguish the error source effectively. In consideration of the in-flight operation mode of DPC based on the adjustment of exposure time, the non-uniformity correction method of the detector based on multi parameters is proposed. Through the electro-optical performance measurement system of the CCD detector, the sensitive factors such as temperature, dark current, exposure time and spectral response are obtained. After a series of preprocessing of the image including removal of dark signal, removal of smearing effect and temperature compensation, the non-uniformity calibration based on multi-parameters is imposed on the detector. The low-frequency unbalanced response difference of the image surface is eliminated, and the high-frequency difference is effectively suppressed. The experimental results show that the photo response non-uniformity of 95% full well single frame data is reduced from 2.86% to 0.36%. After correction, the data noise is shown as shot noise, and the detector has good ability of dynamic range adjustment. The non-uniformity calibration by the proposed method can offer data support for the instrumental calibration and in-flight fast calculation, and provide effective reference for the subsequent polarization remote sensing instruments.

More than 15 nm of continuous wavelength tuning range has been obtained by means of temperature tuning and current setting in three-section grating-cascaded strongly gain-coupled distributed-feedback lasers. Within the entire wavelength tuning range, a sidemode-suppression ratio beyond 50 dB, a linewidth of less than 10 MHz, and a fiber-coupled output power of more than 5 dBm were achieved.