Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters,a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed.At first,the objectives of satellite mission and optimization problems are clarified,and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit,payload,power and quality disciplines.In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm,an improved genetic algorithm is proposed and applied to system-level and discipline-level models.Finally,the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler(CSM)and MATLAB.The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible.It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

Prediction of Petroleum Prospect in the South Yellow Sea Basin Using Satellite Remote Sensing

Using satellite remote sensing to measure the sea surface temperature of the East China Sea Shelf Basin can serve the purpose of predicting the petroleum prospect in the South Yellow Sea Basin. The satellite thermal infrared temperature anomaly area always repeats in the same position as the proved oil prospect area in the East China Sea Shelf Basin, and coincides well with both the CH4 content curve at the water-atmosphere interface and the seafloor geochemical anomaly. The sea surface temperature anomaly areas of the South Yellow Sea Basin in the satellite remote sensing image go banded along 123030, in the S-N direction, and naturally follow the aeromagnetically interpreted eastern nose-type uplift that shows a S-N strike. The north and south ends of the eastern nose are considered as good oil prospect areas because temperature anomaly often occurs there.

The First Global Map of Atmospheric Ammonia(NH_(3)) as Observed by the HIRAS/FY-3D Satellite

Atmospheric ammonia(NH_(3)) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH_(3) concentration based on the absorption lines of NH_(3) in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH_(3)column from the Hyperspectral Infrared Atmospheric Sounder(HIRAS) onboard the Chinese Feng Yun(FY)-3D satellite and present the first atmospheric NH_(3) column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH_(3) hotspots around the world, e.g., India, West Africa, and East China, where large NH_(3) emissions exist. The HIRAS NH_(3) columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer(IASI)measurements, and we find that the two instruments observe a consistent NH_(3) global distribution, with correlation coefficient(R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH_(3) retrieval are discussed.

Quantifying River Bank Erosion and Accretion Patterns along the Gorai River in Kushtia, Bangladesh: A Geospatial Analysis Utilizing GIS and Remote Sensing Techniques

River bank erosion is a natural process that occurs when the water flow of a river exceeds the bank’s ability to withstand it. It is a common phenomenon that causes extensive land damage, displacement of people, loss of crops, and infrastructure damage. The Gorai River, situated on the right bank of the Ganges, is a significant branch of the river that flows into the Bay of Bengal via the Mathumati and Baleswar rivers. The erosion of the banks of the Gorai River in Kushtia district is not a recent occurrence. Local residents have been dealing with this issue for the past hundred years, and according to the elderly members of the community, the erosion has become more severe activities. Therefore, the main objective of this research is to quantify river bank erosion and accretion and bankline shifting from 2003 to 2022 using multi-temporal Landsat images data with GIS and remote sensing technique. Bank-line migration occurs as a result of the interplay and interconnectedness of various factors such as the degree of river-related processes such as erosion, transportation, and deposition, the amount of water in the river during the high season, the geological and soil makeup, and human intervention in the river. The results show that the highest eroded area was 4.6 square kilometers during the period of 2016 to 2019, while the highest accreted area was 7.12 square kilometers during the period of 2013 to 2016. However, the erosion and accretion values fluctuated from year to year.

Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Some recent advances in remote sensing-based monitoring of changes in the Greenland Ice Sheet

The mass balance of the Greenland Ice Sheet(GrIS)plays a crucial role in global sea level change.Since the 1960s,remote sensing missions have been providing extensive and continuous observation data for change monitoring of the GrIS.In this paper,we present our recent research results from remote sensing-based GrIS change monitoring.First,historical satellite data are processed and used to fill data gaps and are combined with existing partial maps,completing an ice velocity map of the GrIS from the 1960s to 1980s.This map provides valuable data for estimating the historical mass balance of Greenland.Second,the monthly gravimetry-based mass balance of the GrIS from 2002 to 2020 is estimated by combining Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow On(GRACE-FO)data.It is found that the GrIS has lost a total mass of approximately 4443±75 Gt during this period.Third,based on Global Land Ice Measurements from Space(GLIMS),an updated Greenland glacier inventory is achieved utilizing data collected between 2006 and 2020.This inventory provides more detailed and up-to-data glacier boundaries of Greenland.Overall,these advances provide essential data support for estimating the mass balance of the GrIS,contributing to the advancement of research on global sea level change.

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

Cultivated Land Changes and Their Driving Forces——A Satellite Remote Sensing Analysis in the Yellow River Delta,China

Taking Kenli County in the Yellow River Delta, China, as the study area and using digital satellite remote sensing techniques, cultivated land use changes and their corresponding driving forces were explored in this study. An interactive interpretation and a manual modification procedure were carried out to acquire cultivated land information. An overlay method based on classification results and a visual change detection method which was supported by land use maps were employed to detect the cultivated land changes. Based on the changes that were revealed and a spatial analysis between cultivated land use and related natural and socio-economic factors, the driving forces for cultivated land use changes in the study area were determined.The results showed a decrease in cultivated land in Kenli County of 5321.8 ha from 1987 to 1998, i.e.,an average annual decrement of 483.8 ha, which occurred mainly in the central paddy field region and the northeast dry land region. Adverse human activities, soil salinization and water deficiencies were the driving forces that caused these cultivated land use changes.

An operational satellite remote sensing system for ocean fishery

Ocean environmental information is very important to supporting the fishermen in fishing and satellite remote sensing technology can provide it in large scale and in near real-time. Ocean fishery locations are always far away beyond the coverage of the satellite data received by a land-based satellite receiving station. A nice idea is to install the satellite ground station on a fishing boat. When the boat moves to a fishery location, the station can receive the satellite data to cover the fishery areas. One satellite remote sensing system was once installed in a fishing boat and served fishing in the North Pacific fishery areas when the boat stayed there. The system can provide some oceanic environmental charts such as sea surface temperature (SST) and relevant derived products which are in most popular use in fishery industry. The accuracy of SST is the most important and affects the performance of the operational system, which is found to be dissatisfactory. Many factors affect the accuracy of SST and it is difficult to increase the accuracy by SST retrieval algorithms and clouds detection technology. A new technology of temperature error control is developed to detect the abnormity of satellite-measured SST. The performance of the technology is evaluated to change the temperature bias from -3.04 to 0.05 ℃ and the root mean square (RMS) from 5.71 to 1.75℃. It is suitable for employing in an operational satellite-measured SST system and improves the performance of the system in fishery applications. The system has been running for 3 a and proved to be very useful in fishing. It can help to locate the candidates of the fishery areas and monitor the typhoon which is very dangerous to the safety of fishing boats.

Satellite remote-sensing technologies used in forest fire management

Satellite remote sensing has become a primary data source for fire danger rating prediction, fuel and fire mapping, fire monitoring, and fire ecology research. This paper summarizes the research achievements in these research fields, and discusses the future trend in the use of satellite remote-sensing techniques in wildfire management. Fuel-type maps from remote-sensing data can now be produced at spatial and temporal scales quite adequate for operational fire management applications. US National Oceanic and Atmospheric Administration (NOAA) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites are being used for fire detection worldwide due to their high temporal resolution and ability to detect fires in remote regions. Results can be quickly presented on many Websites providing a valuable service readily available to fire agency. As cost-effective tools, satellite remote-sensing techniques play an important role in fire mapping. Improved remote-sensing techniques have the potential to date older fire scars and provide estimates of burn severity. Satellite remote sensing is well suited to assessing the extent of biomass burning, a prerequisite for estimating emissions at regional and global scales, which are needed for better understanding the effects of fire on climate change. The types of satellites used in fire research are also discussed in the paper. Suggestions on what remote-sensing efforts should be completed in China to modernize fire management technology in this country are given.

A Comparison of Cloud Layers from Ground and Satellite Active Remote Sensing at the Southern Great Plains ARM Site

Using the data collected over the Southern Great Plains ARM site from 2006 to 2010, the surface Active Remote Sensing of Cloud （ARSCL） and CloudSat-CALIPSO satellite （CC） retrievals of total cloud and six specified cloud types [low, midlow （ML）, high-mid-low （HML）, mid, high-mid （HM） and high] were compared in terms of cloud fraction （CF）, cloud-base height （CBH）, cloud-top height （CTH） and cloud thickness （CT）, on different temporal scales, to identify their respective advantages and limitations. Good agreement between the two methods was exhibited in the total CF. However, large discrepancies were found between the cloud distributions of the two methods at a high （240-m） vertical grid spacing. Compared to the satellites, ARSCL retrievals detected more boundary layer clouds, while they underestimated high clouds. In terms of the six specific cloud types, more low- and mid-level clouds but less HML- and high-level clouds were detected by ARSCL than by CC. In contrast, the ARSCL retrievals of ML- and HM-level clouds agreed more closely with the estimations from the CC product. Lower CBHs tended to be reported by the surface data for low-, ML- and HML-level clouds; however, higher CTHs were often recorded by the satellite product for HML-, HM- and high-level clouds. The mean CTs for low- and ML-level cloud were similar between the two products; however, the mean CTs for HML-, mid-, HM- and high-level clouds from ARSCL were smaller than those from CC.

Effects of Aerosol Vertical Inhomogeneity on the Upwelling Radiance and Satellite Remote Sensing of Surface Reflectance

There are two widely used radiative models without consideration of aerosol inhomogeneity for satellite remote sensing application, the Homogeneous Model and the Two-layer Model with aerosol in the lower layer. In this paper, effects of the aerosol vertical inhomogeneity on upwelling radiance and satellite remote sensing of surface reflectance are analyzed through numerical simulations by using two models. As shown in the simulations by using 24 representative aerosol models, there is often a considerably large error in upwel-ling radiance calculated by two models (Homogeneous and Two-layer) for the short wavelength channel with strong molecular scattering, owing to the difference between molecular and aerosol scattering proper-ties. For the long wavelength channel, the error is small if aerosol optical parameters are less variable with height, but it could also be significant if there are aerosol layers with different scattering phase functions and single scattering albedo. The radiance errors by the Homogeneous Model and the Two-layer Model can be up to 31.4% and 31.5% for the clean atmosphere, and in case of turbid atmosphere 67.8% and 59.2%, respectively. The radiance error could result in a large uncertainty of surface reflectance retrievals, especially for the short wavelength channel and the strongly absorbing aerosol. For the turbid atmosphere with strong-ly absorbing aerosol, the Homogeneous Model and the Two-layer Model are not suitable for atmospheric correction application. Key words Satellite remote sensing - Aerosol inhomogeneity - Surface reflectance - Radiance

Study on Thermal Deformation Measurement of Optical RemoteSensing Satellite Platform Based on Machine Vision

To solve the special requirements of the high orbit optical remote sensing satellite for the thermal deformation of the platform the thermal deformation test and measurement scheme of the satellite platform is designed. Through the comparative analysis of test results the rationality of the thermal deformation design of the platform structure is verified. The results of thermal deformation measurement show that the maximum deformation of A camera mounting surface is 57.5〃 and the maximum deformation of B camera mounting surface points to 79.3〃 which can be used as the basis for thermal deformation prediction of satellite during the orbit operation.

The first quantitative remote sensing of ocean internal waves by Chinese GF-3 SAR satellite

Quantitative analysis and retrieval is given by the State Key Laboratory of Satellite Ocean Environment Dynamics（SOED）,Second Institute of Oceanography（SIO）,State Oceanic Administration（SOA）,China,from the first batch of GF-3 synthetic aperture radar（SAR）data with ocean internal wave features in the Yellow Sea.

A study of the validation of atmospheric CO_2 from satellite hyper spectral remote sensing

Three total column dry-air mole fractions of CO_2(XCO_2) products from satellite retrievals, namely SCIAMACHY, NIES-GOSAT, and ACOS-GOSAT, in the Northern Hemisphere were validated by ground data from the Total Carbon Column Observing Network(TCCON). The results showed that the satellite data have the same seasonal fluctuations as in the TCCON data, with maximum in April or May and minimum in August or September. The three products all underestimate the XCO2. The ACOS-GOSAT and the NIES-GOSAT products are roughly equivalent, and their mean standard deviations are 2.26 × 10^(-6)and 2.27 × 10^(-6)respectively. The accuracy of the SCIMACHY product is slightly lower, with a mean standard deviation of 2.91 × 10^(-6).

Classification of snow cover days in western China and comparison with satellite remote sensing data

The daily snow cover data from 232 meteorological stations to the west of 105°E in China for the period 1951-2004 were used to classify the snow cover and analyze decadal variations of snow cover types in western China, and comparison was made between the observational data and those retrieved from passive microwave remote sensing data （SMMR and SSM/I） in 1980-2004. The results show that stable snow-covered areas included northern Xinjiang, the Tianshan Mountains, and the eastern Tibetan Plateau with more than 60 snow cover days; no snow cover was found in the center of the southern Xinjiang Basin, the Sichuan Basin, and southern Yunnan. In addition to the above-mentioned, there were unstable snow-covered areas in western China. Furthermore, the snow cover types in northern Xinjiang, the Tianshan Mountains, the Hexi Corridor, and the vast areas from Chengdu to Kunming were unchanged. In the 1980s, the south-north dividing line between the major snow-covered area and snow-free area advanced to its most southern position. The snow cover days calculated from satellite remote sensing were generally longer than those from observational data in western China, mainly in the higher-altitude mountains, the Hexi Corridor, and the western Sichuan Plateau.

Satellite remote sensing of sea-ice and its operational monitoring method along the coast of China

Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quantitative data of NOAA, and its contents include computer processing of AVHRR sounding data of NOAA and its program design, imagery processing of sea-ice imagery from satellite and their thematic analysis. The sea-ice satellite colour imageries processed via this software system are able to interpret sea-ice pattern, characterizing it by thickness, maximum position of ice boundary, floe concentration and dynamic process of ice changing. At the same time, analyses of the ice condition of the Bohai Sea for the two-year period (1986-1988) as monitored by satellite have been summarized.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.