The application of neural network algorithms for the quantification of Volatile Organic Compounds (VOCs) concentrations, derived from infrared absorption spectral data, has been shown to achieve superior accuracy compared to conventional least-squares regression techniques. The current neural network models in use generally have issues with low precision and stability, which affect the consistency and credibility of the inversion results. In this study, in order to resolve the aforementioned challenges, we present the pioneering application of the MiniRocket (minimally random convolutional kernel transform) model for the quantitative analysis of VOCs concentrations utilizing hyperspectral data derived from satellite platforms. This model surpasses traditional neural network methodologies by virtue of its automated feature extraction, enhanced computational efficiency, near-deterministic processing, and superior predictive accuracy. The near-deterministic nature of MiniRocket's transformation process ensures reproducibility, as it guarantees identical outcomes given the same input data across diverse computational settings. We employed a training dataset consisting of 120 infrared hyperspectral data with a spectral resolution of 1 cm-1 and a spectral range of 2.8 to 14.3 μm. Additionally, we utilized a validation dataset comprising 80 sets of test data with randomly assigned concentrations. Experimental results indicate that the MiniRocket model achieves a mean error of prediction (MES) of 6.2×10-3 parts per million (ppm) for the estimation of pollutant gas concentrations, with a processing time reduced to 0.02 seconds. These outcomes not only underscore the model's superior predictive accuracy but also highlight its unparalleled computational efficiency when compared to other existing models in the field.
Differential Absorption Lidar (DIAL) is an effective approach for measuring atmospheric ozone with high precision. To solve the problem of wavelength pair selection for DIAL, numerical simulation experiments of DIAL were carried out. Within the ultraviolet band of 220nm to 360nm, tests were conducted for each wavelength with an interval of 1nm, and wavelength combinations with differences of 5nm, 10nm, 20nm, 30nm, 40nm, and 50nm were tested. The feasibility and of different wavelength combinations in the band for ozone measurement were synthesisly evaluated. The experimental results show that for DIAL systems with wavelength differences of 5 nm, 10 nm, and 20 nm, the combined error of ozone measurements in the troposphere from 0 to 6 KM below 15% if the strong absorption wavelengths (λon) are selected in the range of 260 nm to 283 nm, and the synthesis error of ozone measurements in the stratosphere from 15 to 30 KM below 15% if λon are selected in the range of 305 nm to 315 nm. For DIAL systems with wavelength differences of 30nm and 40nm, the synthesis error of ozone measurements in the troposphere from 0 to 6 KM below 15% if λon are selected in the range of 254 nm to 270 nm, and the synthesis error of ozone measurements in the stratosphere from 15 to 30 KM below 15% if λon are selected in the range of 305nm to 312nm. For DIAL systems with a wavelength difference of 50nm, the synthesis error of ozone measurement in the troposphere at 0 to 6KM is higher than 15%, and the synthesis error of ozone measurements in the stratosphere from 15 to 30 KM below 15% if λon are selected in the range of 305nm to 311nm. This result serves as a reference for the selection of wavelength pairs for DIAL measurements of atmospheric ozone.
Through the combination of active and passive detection, the marine science mission will realize the integrated remote sensing of marine dynamic and ecological parameters, fill the gap of sub-mesoscale perspective observation, and take a key step toward three-dimensional remote sensing of "transparent ocean". The satellite will operate in a sun-synchronous orbit and will be equipped with lidar and a light-weighted multispectral camera. Among them, the camera has 8 multispectral bands with a spatial resolution of 20m and a width of 160km. In this paper, the simulation calculation of the sun glints area is carried out for the two installation methods, which are formal and oblique, and the four typical simulation time nodes are spring equinox, summer solstice, autumn equinox, and winter solstice. The results show that the proportion of single-track sun glints area on the summer solstice is about 37.5% and 34.0%, respectively, which is the highest time point of the year, and the equator, tropic of Capricorn and tropic of cancer regions will produce 100% of the sun glints area at different time points. Compared with the formal form, the total proportion of sun glints area is reduced by about 10% for the oblique form, which will improve the effective data rate in orbit and improve the efficiency of satellite imaging. At the same time, this paper briefly describes the satellite calibration mode according to the combination of active and passive imaging. It will guide the subsequent satellite design and application.
The existing space-based remote sensing has problems such as weak collaboration, slow response, and long links, which cannot meet the application requirements of real-time anomaly detection, recognition, and transmission. This article studies the characteristics of existing surface anomaly classification, establishes a demand matrix for anomaly remote sensing, establishes a new surface anomaly real-time detection system, and proposes a working mode for anomaly real-time recognition, using the constellation system task and information flow design combined with the on-board intelligent processing unit, improve the anomaly recognition and service capability of the space-based system, design the mission flow and information flow of the constellation system, and finally analyze the communication link and timeliness of the system. The simulation analysis results show that the system can achieve minute level efficient anomaly recognition and early warning, effectively improving the service capability to users, this provides an overall idea and architectural reference for the construction of future space-based surface anomaly real-time detection systems.
The lunar is a stable radiation source, which can be used as an ideal source for in-orbit calibration of remote sensors and evaluation of detector degradation performance. A passive lunar calibration method is proposed for Chinese ocean color satellite, which reuses field of view of cryogenic-deep-space calibration, periodically achieves monthly lunar calibration tasks. This work enriches ocean color satellite in-orbit calibration methods and improves data accuracy of products. The start and stop angle vector of cryogenic-deep-space, satellite-lunar pointing vector and imaging observation model are established in simulation software. The satellite and payload parameters are used as input conditions to carry out the simulation of the lunar calibration timing. The lunar calibration timing of payload COCTS (Chinese Ocean Color and Temperature Scanner) is simulated 00:00:00~24:00:00UTC on June 28, 2020. The result shows that lunar calibration was carried out for twice. The starting UTC (Universal Time Coordinated) time was 15:16:44 and 16:56:37, respectively. The duration was two seconds. Through analyzing the 0-level products of in-orbit satellite received by the application system, the cryogenic-deep-space data showed abnormal changes at 15:16:45 and 16:56:38 on June 28, 2020, and the DN (Digital Number) values dropped from 300 to 62 and 73, respectively. The in-orbit calibration timing is basically the same as the simulation results, and the numerical anomalies of cryogenic-deep-space data are consistent with the principle design, indicating that the simulation model can be used to predict the in-orbit lunar calibration timing of ocean color satellite. The analysis method can be used for cryogenic-deep-space imaging mission mode and payload design of the follow-up ocean color satellites.
Global Navigation Satellite System reflectometry (GNSS-R) technology uses the signal receiver to receive the reflected signal of navigation satellite for ground feature inversion. It has the advantages of wide dynamic range, all day, all weather, light weight and low cost. It has a broad application prospect in the field of remote sensing. On June 5, 2019, China's first group of test satellites carrying GNSS-R payload, BF-1 A/B satellites, was successfully launched on the sea by using CZ-11 carrier rocket. GNSS-R data with high spatial and temporal resolution were obtained during the operation of the satellite in orbit. In order to solve the problem of low accuracy and few methods of GNSS-R inversion of sea surface wind speed, this paper proposes a sea surface wind speed inversion method based on the delay doppler map average (DDMA) of BF-1 satellite. Firstly, the GNSS-R sea surface scattering model is established by using Z-V model and Elfouhaily wave spectrum to verify the relationship between the observation of BF-1 satellite and the change of wind speed, Then, the principle of GNSS-R sea surface wind speed inversion is studied. Through the correction and normalization of observations, the inversion observation DDMA is obtained. Finally, the geophysical model function (GMF) is established by using L1 level satellite data to realize the high-precision inversion of sea surface wind speed. The root mean square (RMS) accuracy of wind speed inversion is 1.81m/s, which is slightly higher than that of CYGNSS when compared with that of CYGNSS at the same time. The experimental results show that the inversion trend of the same region and time is the same, which proves the accuracy and effectiveness of the data processing results, it will also provide support for the follow-up GNSS-R satellite development and the development and optimization of surface wind speed inversion algorithm.
In recent years, the emerging global navigation satellite system reflectometry (GNSS-R) technology has become a research hotspot for its lightweight, high sensitivity and rich technology application scenarios. It has broad application prospects in the field of remote sensing detection and navigation technology. The role of GNSS-R remote sensing satellite in the field of marine remote sensing is becoming increasingly prominent. The acquisition of data and information and the observation performance of GNSS-R remote sensing satellite are not only constrained by the remote sensing equipment itself, but also affected by the satellite orbit. Based on the technical characteristics of GNSS-R remote sensing satellite, this paper proposes a grid based coverage efficiency statistical method, analyzes some influencing factors of GNSS-R remote sensing satellite efficiency based on the remote sensing task, and analyzes the influence of each factor on GNSS-R remote sensing satellite efficiency by modeling -It can provide theoretical reference for GNSS-R satellite orbit selection and optimization, onboard load design optimization and large-scale system construction.
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