On the Radiative Properties of Ice Clouds:Light Scattering, Remote Sensing,and Radiation Parameterization

Presented is a review of the radiative properties of ice clouds from three perspectives： light scattering simulations, remote sensing applications, and broadband radiation parameterizations appropriate for numerical models. On the subject of light scattering simulations, several classical computational approaches are reviewed, including the conventional geometric-optics method and its improved forms, the finite-difference time domain technique, the pseudo-spectral time domain technique, the discrete dipole approximation method, and the T-matrix method, with specific applications to the computation of the singlescattering properties of individual ice crystals. The strengths and weaknesses associated with each approach are discussed.With reference to remote sensing, operational retrieval algorithms are reviewed for retrieving cloud optical depth and effective particle size based on solar or thermal infrared（IR） bands. To illustrate the performance of the current solar- and IR-based retrievals, two case studies are presented based on spaceborne observations. The need for a more realistic ice cloud optical model to obtain spectrally consistent retrievals is demonstrated. Furthermore, to complement ice cloud property studies based on passive radiometric measurements, the advantage of incorporating lidar and/or polarimetric measurements is discussed.The performance of ice cloud models based on the use of different ice habits to represent ice particles is illustrated by comparing model results with satellite observations. A summary is provided of a number of parameterization schemes for ice cloud radiative properties that were developed for application to broadband radiative transfer submodels within general circulation models（GCMs）. The availability of the single-scattering properties of complex ice habits has led to more accurate radiation parameterizations. In conclusion, the importance of using nonspherical ice particle models in GCM simulations for climate studies is proven.

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.

Retrieval algorithm of quantitative analysis of passive Fourier transform infrared (FTRD) remote sensing measurements of chemical gas cloud from measuring the transmissivity by passive remote Fourier transform infrared

Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.

Macrophysical properties of specific cloud types from radiosonde and surface active remote sensing measurements over the ARM Southern Great Plains site

Accurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences.By using the long-term cloud data collected during the ARM program at the Southern Great Plains central facility during 2001-2010,the consistencies and differences in the macrophysical properties of clouds between radiosonde and ground-based active remote sensing are quantitatively evaluated according to six cloud types：low;mid-low（ML）;high-midlow;mid;high-mid（HM）;and high.A similar variability trend is exhibited by the radiosonde and surface observations for the cloud fractions of the six cloud types.However,the magnitudes of the differences between the two methods are different among the six cloud types,with the largest difference seen in the high clouds.The distribution of the cloud-base height of the ML,mid,and HM clouds agrees in both methods,whereas large differences are seen in the cloud-top height for the ML and high clouds.The cloud thickness variations generally agree between the two datasets for the six cloud types.

The actuality and progress of whole sky infrared cloud remote sensing techniques

Clouds are crucial regulators of both weather and climate. Properties such as the amount,type,height,distribution and movement of them have an impact on the earth's radiation budget and the hydrological cycle,thus cloud observation is very important. The disadvantages of zenith pointing measuring instruments and whole sky visible imagers limit the application of them.A summary of the actuality and application of ground-based whole sky infrared cloud measuring instruments and analyses of the techniques of radiometric calibrations,removal of atmospheric emission and calculation of cloud cover,amount,type are conducted to promote the automatically observation of the whole sky. Fully considering whole sky infrared cloud sounding theories,techniques and applications,there are still a lot of studies on improving the properties of instruments,enhancing the techniques of cloud base height measurements and establishing instrumental cloud classification criterion before actual operations.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

Cloud removal of remote sensing image based on multi-output support vector regression

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

Study on Microwave Remote Sensing of Atmosphere,Cloud and Rain

In this paper, recent research of microwave remote sensing of atmosphere, cloud and rain in China is presented. It includes the following aspects:(1) Progress in the development of multifrequency radiometer and its characteristics and parameters;(2) Application of microwave remote sensing in prediction of atmospheric boundary layer. The atmospheric temperature profiles are derived with 5 mm (54.5 GHz) radiometer angle-scanning observations. Due to the fact that microwave radiometer could monitor the atmospheric temperature profile continuously and make the initialization of numerical model any time, it is helpful for improving the accuracy in prediction of the evolution of atmospheric boundary layer;(3) Theory and application of microwave radiometers in monitoring atmospheric temperature, humidity and water content in cloud. The field experiment of International Satellite Cloud Climatology Project (ISCCP) at Shionomisaki and Amami Oshima of Japan for studies of cloud and weather has been described;(4) Satellite remote sensing of atmosphere and colud. The TIROS-N TOYS satellite data are used to obtain atmospheric temperature profile. The results are compared with those of radiosonde, with rms deviation smaller than that of the current operational TOVS processing;(5) Microwave remote sensing and communication. The atmospheric attenuations are derived with microwave remote sensing methods such as solar radiation method etc., in order to obtain the local value instantaneously. The characteristics of Beijing's rainfall have been analysed and the probability of microwave attenuation of rain is predicted;(6) For improvement of the accuracy of rainfall measurement, a radiometer-radar system (λ= 3.2 cm) has been developed The variation of rainfall distribution and area-rainfall may be obtained by its measurements, which mav be helpful for hydrological prediction.The prospect of microwave remote sensing in meteorology is also discussed.

Cloud Top Height Retrieval Using Polarizing Remote Sensing Data of POLDER

A retrieval method of cloud top heights using polarizing remote sensing is proposed in this paper. Using the vector radiative transfer model in a coupled atmosphere-ocean system, the factors influencing the upwelling linear polarizing radiance at top-of-atmosphere are analyzed, which show that the upwelling linear polarizing radiance varies remarkably with the cloud top height, but has negligible sensitivity with cloud albedo and aerosol scattering above the cloud layer. Based on this property, a cloud top height retrieval algorithm using polarizing remote sensing was developed. The algorithm has been applied to the polarizing remote sensing data of Polarization and Directionality of the Earth's Reflectances-2 (POLDER-2). The retrieved cloud top height from POLDER-2 compares well with the Moderate Resolution Imaging Spectroradiometer (MODIS) operational product with a bias of 0.83 km and standard deviation of 1.56 km.

Technical foundation research on high resolution remote sensing system of China's coastal zone

China's coastal zone is a region with a highly developed economy that contrasts clearly with the slow paced regular investigation on its natural environment,which cannot keep pace with the requirement of economic development and modern management.Laying a theoretical foundation for the modern management of China's costal zone is aimed at. This research focuses on the following processing and analyzing technologies for coastal zone high-resolution remote sensing data: organization and management of large amounts of high-resolution remote sensing data, quick and precise spatial positioning system,algorithms for image fusion in feature level and coastal zone feature extraction. They will form a technical foundation of the system. And, if combined with other research results such as coastal zone remote sensing classification system and its mapping subsystem, an advanced technical frame for remote sensing investigation of coastal zone resource will be constructed.

Changes in Global Cloud Cover Based on Remote Sensing Data from 2003 to 2012

As is well known,clouds impact the radiative budget,climate change,hydrological processes,and the global carbon,nitrogen and sulfur cycles.To understand the wide-ranging effects of clouds,it is necessary to assess changes in cloud cover at high spatial and temporal resolution.In this study,we calculate global cloud cover during the day and at night using cloud products estimated from Moderate Resolution Imaging Spectroradiometer(MODIS)data.Results indicate that the global mean cloud cover from 2003 to 2012 was 66%.Moreover,global cloud cover increased over this recent decade.Specifically,cloud cover over land areas(especially North America,Antarctica,and Europe)decreased(slope=–0.001,R^2=0.5254),whereas cloud cover over ocean areas(especially the Indian and Pacific Oceans)increased(slope=0.0011,R^2=0.4955).Cloud cover is relatively high between the latitudes of 36°S and 68°S compared to other regions,and cloud cover is lowest over Oceania and Antarctica.The highest rates of increase occurred over Southeast Asia and Oceania,whereas the highest rates of decrease occurred over Antarctica and North America.The global distribution of cloud cover regulates global temperature change,and the trends of these two variables over the 10-year period examined in this study(2003–2012)oppose one another in some regions.These findings are very important for studies of global climate change.

A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space,cost savings.However,the openness of cloud brings challenges for image data security.In this paper,we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment,which takes the weights of participants(i.e.,cloud service providers)into consideration.An extended Mignotte sequence is constructed according to the weights of participants,and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images.Then we store the shadows in every cloud service provider,respectively.At last,we restore the remote sensing image based on the Chinese Remainder Theorem.Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud.The experiment also shows that no matter weight values,each service providers only needs to save one share,which simplifies the management and usage,it also reduces the transmission of secret information,strengthens the security and practicality of this scheme.

Framework of SAGI Agriculture Remote Sensing and Its Perspectives in Supporting National Food Security

Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.

Remote Sensing Data Processing Process Scheduling Based on Reinforcement Learning in Cloud Environment

Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance.To solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem.Then,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method.Finally,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from experience.The experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing data.The results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.

Land cover change and carbon stores in a tropical montane cloud forest in the Sierra Madre Oriental,Mexico

Tropical montane cloud forest is one of the ecosystems with the highest biomass worldwide, representing an important carbon store. Globally its deforestation index is –1.1%, but in Mexico it is higher than –3%. Carbon estimates are scarce globally, particularly in Mexico. The objective of this study was to simulate future land-cover scenarios for the Sierra Madre Oriental in Mexico, by analyzing past forest cover changes. Another objective was to estimate stored carbon in the two study areas. These objectives involve the generation of information that could be useful inputs to anti-deforestation public policy such as the REDD+ strategy. Remote sensing was used to measure land cover change and estimate carbon stocks. Satellite images from 2015, 2000 and 1986 were used, and Dinamica EGO freeware generatedmodels of future projections. Between 1986 and 2015, 5171 ha of forest were converted to pasture. The annual deforestation rates were –1.5% for Tlanchinol and –1.3% for the San Bartolo Tutotepec sites. Distance to roads and marginalization were highly correlated with deforestation. By 2030, an estimated 3608 ha of forest in these sites will have been converted to pasture. Stored carbon was estimated at 16.35 Mg C ha-1 for the Tlanchinol site and 12.7 Mg C ha-1 for the San Bartolo site. In the Sierra Madre Oriental deforestation due to land cover change(–1.4%) is higher than levels reported worldwide. Besides having high values of stored carbon(14.5 Mg C ha-1), these forests have high biodiversity. The models' outputs show that the deforestation process will continue if action is not taken to avoid the expansion of livestock pasturing. This can be done by paying incentives for forest conservation to the owners of the land. The results suggest that REDD+ is currently the most viable strategy for reducing deforestation rates in tropical montane cloud forests in Sierra Madre Oriental.

Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review

Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.

New Cloud Detection Index (CDI) for Forecasting the Extreme Rain Events

The water vapor content and clouds play a very significant role in atmospheric conditions. This paper is derived from the new cloud detection index (CDI) which is useful to forecast extreme weather events like heavy or extreme rain. The CDI is retrieved using two crucial elements of cloud formation, the critical humidity and critical water vapor. The cloud thickness is determined by using CDI for a radiosonde site (VABB) located in Mumbai, the western part of India. The obtained results are compared with the cloud thickness required for extreme rain. The outcome of the comparison is discussed in this paper. The cloud detection index is also useful in establishing the atmospheric stability along with another four atmospheric stability indices.

Improving Satellite-Retrieved Cloud Base Height with Ground-Based Cloud Radar Measurements

Cloud base height(CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a lookup table(LUT) of effective cloud water content(ECWC), representing the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness(CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparative analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar(MMCR) measurements, and results show that the mean bias(correlation coefficient) is0.18±1.79 km(0.73), which is lower(higher) than 0.23±2.11 km(0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar(i.e., Cloud Sat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing networks, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.

Design of Remote Monitoring System for Ceramic Electric Kiln

In order to improve the management and control requirements of small electric kilns in ceramic production field.According to the excessive dispersion of electric kilns in porcelain production field,a remote monitoring system based on RS485 bus and Modbus protocol is designed.The system consists of data acquisition,field control,cloud monitoring and communication protocol.MCGS is used as the master station to communicate with the temperature controller,collect and transmit the internal data to Siemens PLC.Through the communication between Siemens PLC and IOT gateway,the kiln working data is uploaded to the cloud platform to realize the remote monitoring for ceramic electric kiln.The experimental results showed this system can accurately collect the working temperature and parameters of the kiln in real time,and can remotely cortrol the kiln.

Reduced-order design of Robust H_∞ Controller for an Inertial Stabilized Aerial Platform

The uncertainty disturbance is one of the main disturbances that seriously influences the stabilization precision of an aerial inertially stabilized platform(ISP)system.In this paper,to improve the stabilization precision of the ISP under disturbance uncertainty,a robust H∞controller is designed in this paper.Then,the reduction order is carried out for high-order controllers generated by the robust H∞loop shaping control method.The application of the minimum implementation and balanced truncation algorithm in controller reduction is discussed.First,the principle of reduced order of minimum implementation and balanced truncation are analyzed.Then,the method is used to reduce the order of the high-order robust H∞loop shaping controller.Finally,the method is analyzed and verified by the simulations and experiments.The results show that by the reduced-order method of minimum implementation and balanced truncation,the stabilization precision of the robust H∞loop shaping controller is increased by about 10%.