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An Automatic Decision-Level Fusion Rice Mapping Method of Optical and SAR Images Based on cloud coverage

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 5018-5032页

作者： Jiang, Xueqin Gao, Song Du, Huaqiang Fang, Shenghui Gong, Yan Han, Ning Wang, Yirong Zhejiang A&F Univ State Key Lab Subtrop Silviculture Hangzhou 311300 Peoples R China Zhejiang A&F Univ Key Lab Carbon Cycling Forest Ecosyst & Carbon Seq Hangzhou 311300 Peoples R China Zhejiang A&F Univ Sch Environm & Resources Sci Hangzhou 311300 Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

Timely and accurate mapping of paddy rice cultivation is crucial for estimating rice production and optimizing land utilization. Optical images are essential data source for paddy rice mapping, but it is susceptible to cloud contamination. Existing methods struggle to effectively utilize clear-sky pixel information in optical images containing clouds, which impacts the accuracy of paddy rice mapping under cloudy conditions. To address the abovementioned problems, we propose an automatic decision-level fusion rice mapping method of optical and synthetic aperture radar (SAR) images based on cloud coverage (the Auto-OSDF method). The method effectively utilizes clear-sky pixels in images containing clouds and leverages the advantages of SAR features in heavily clouded regions. We tested and validated the Auto-OSDF method in Xiangyin County, Hunan Province, and analyzed the impact of different cloud coverage levels (10%-50%) on the accuracy of rice mapping based on this method. The results indicate that, as cloud coverage increases, the rice mapping accuracy of the Auto-OSDF method is not significantly affected, with overall accuracy and Kappa coefficients both above 93% and 0.90, respectively. To show the value of the proposed method in large-scale applications, we further mapped paddy rice in the entire Hunan Province, and the overall accuracy and Kappa coefficient were 92.47% and 0.87, respectively. The results obtained by the Auto-OSDF method show an average R-2 of 0.926 compared to municipal-level statistical planting areas. The abovementioned study demonstrates that the Auto-OSDF method is capable of achieving stable and high-precision rice mapping under cloud contamination interference.

关键词： Optical imaging clouds Optical sensors Adaptive optics Accuracy Optical distortion Radar polarimetry Contamination Integrated optics Crops cloud coverage decision-level fusion optical and synthetic aperture radar (SAR) images paddy rice mapping time series

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Luminance Simulation in CARLA under cloud coverage - Model Validation and Implications 3

Luminance Simulation in CARLA under Cloud Coverage - Model V...

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3rd IEEE International Workshop on Metrology for Automotive (IEEE MetroAutomotive)

作者： Ulreich, Fabian Moser, Elisabeth Olbrich, Florian Ebert, Martin Bierl, Rudolf Kaun, Andre TH Ingolstadt CARISSMA Inst Automated Driving Regensburg Germany OTH Regensburg Sensor Applikat Zentrum Regensburg Germany FAU Erlangen Nurnberg Multimedia Com & Signal Proc Erlangen Germany

ISBN: (纸本)9798350321876

To decrease the number of kilometers driven during the development of autonomous cars or driving assistance systems, performant simulation tools are necessary. Currently, domain distance effects between simulation and reality are limiting the successful application of rendering engines in data-driven perception tasks. In order to mitigate those domain distance effects, simulation tools have to be as close to reality as possible for the given task. For optical sensors like cameras, the luminance of the scene is essential. We provide within this paper a method to measure the luminance of rendered scenes within CARLA, an often used open-source simulation environment. Thereby, it is possible to validate the environment and weather models by taking real-world measurements with photometric sensors or with the help of open-source weather data, published e.g. by the German federal service for weather data (DWD - "Deutscher Wetterdienst"). Employing our proposed luminance measurement, the domain gap resulting from the simulation can be specified, which makes it possible to evaluate the statements about the safety of the automated driving system determined within the simulation. We show that the ratio between global and diffuse radiation modeled by the default atmosphere models within CARLA are under limited conditions similar to real-world measurements taken by the DWD. Nevertheless, we show, that the ratio's temporal variability in real-world situations is not modeled by CARLA.

关键词： simulation weather carla meteorology luminance cloud coverage

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A Fast Image Mosaicking Method Based on Iteratively Minimizing cloud coverage Areas

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第8期18卷 1371-1375页

作者： Fang, Zhonghao Yu, Xiaoyu Pan, Jun Fan, Niming Wang, Haibo Qi, Jianchao Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China China Ctr Resources Satellite Data & Applicat Remote Sensing Ground Syst Dept Beijing 100094 Peoples R China

There are many areas with cloud coverage in remote sensing images frequently, and they will shelter the ground objects under clouds. When images with clouds are used in mosaicking, cloud coverage areas may still exist in mosaics, which constrain the application of mosaics. Therefore, a simple and fast image mosaicking method based iteratively minimizing cloud coverage areas is proposed in this letter. In the proposed method, both cloud-free images and images with clouds are used to generate a mosaic with minimal cloud coverage areas. After the generation of initial effective mosaic polygons (EMPs) for images, a quadtree iterative procedure is presented to optimize each EMP by excluding the residual cloud coverage areas in each EMP. The procedure fully takes into account the spatial distribution of cloud coverage areas, and each area with cloud coverage is minimized by as few images as possible. The experimental results show that the mosaic generated by the proposed method can achieve a better radiometric continuity and higher efficiency than the pixel-based mosaic method. Besides, the proposed method can make the full use of the cloud-free areas in images, which can improve the utilization of images and the currency of mosaics.

关键词： Minimization Remote sensing Radiometry Fans Graphical models Distribution functions Support vector machines cloud coverage mosaic quadtree iterative algorithm refinement

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Scene- and pixel-level analysis of Landsat cloud coverage and image acquisition probability in South and Southeast Asia

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 123卷

作者： Yang, Yin Li, Peng Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

Landsat has provided over five-decade Earth's observations including cloud cover (CC). However, scene-wide and/or pixel-level research on CC and image acquisition probability (IAP) still lags much behind. Scene-based analysis is often reported with arbitrary thresholds, while mechanism of cloud probability (CP) dynamics remains poorly investigated. Understanding Landsat cloud climatology is critical for better utilizing historical archives especially in the tropics. Here, South and Southeast Asia (SSEA) was selected to investigate the potential of a coarse-and-fine thresholding (CAFT) method in determining appropriate threshold of CC with all available Landsat-4/5/7/8 Thematic Mapper (TM), Enhanced TM Plus (ETM+), and Operational Land Imager (OLI) scenes (a total of 691,919) during 1984-2022. Then, OLI-based pixel CP was further examined, followed by correlation analysis between CC and climatic variables (i.e., temperature and precipitation). Main conclusions include: (1) the CAFT method holds promising potential in determining the precise CC threshold (24%) in SSEA. (2) Landsat IAP (nearly 40%) and CP (about 36%) show significant monthly, regional, and sensor variations across SSEA. (3) ETM+ performs the best with the highest IAP (>45%), while OLI tends to have larger IAPs in persistently cloudy area. (4) Significant correlation between CP and precipitation or temperature indicates that the roles of climate variables on CC differ geographically in South Asia and Southeast Asia. This study contributes to developing a universal and simple method to determine optimal thresholds for global CC research and understanding the interaction between CC and climate change.

关键词： Landsat cloud coverage Image acquisition probability Coarse -and -fine thresholding Scene and pixel -level South and Southeast Asia

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Uncertainty of city-based urban heat island intensity across 1112 global cities: Background reference and cloud coverage

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REMOTE SENSING OF ENVIRONMENT 2022年 271卷 112898-112898页

作者： Li, Kangning Chen, Yunhao Gao, Shengjun Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China

As an urban heat island (UHI) is closely associated with a wide variety of environmental issues, it is necessary to monitor its spatiotemporal variations in an accurate and timely manner. However, the accuracy of surface urban heat island intensity (SUHII) estimation is greatly challenged by three issues. Namely, less attention has been given to city-based global studies, LSUHII (SUHII difference) is induced when various methods are used to the determine background reference, and uncertainty arises in the methods for coping with missing land surface temperature (LST). Thus, this paper quantitatively evaluated these different methods and their impacts on spatiotemporal SUHII variations across 1112 global cities. There are two major findings: (1) the modified equal area-rural (MEA-R) method can overcome the limitations of the other methods. The fixed buffer (FB) method is limited by the difficulty of buffer selection and the inapplicability of a single fixed buffer for various cities, particularly in large-scale studies. The simplified urban-extent (SUE) method is challenged by cluster discrepancies and data obsolescence issues, and the modified equal area-suburban (MEA-S) method underestimates SUHII resulting from strong human activities in suburban areas. The daytime LSUHII induced by different methods for the reference definition reaches 0.62 K, accounting for 42% of the average SUHII. LSUHII is 0.34 K (23%) at night. LSUHII with evident spatiotemporal variations indicates that the different methods used to define the background reference introduce a considerable uncertainty to SUHII research. (2) After comparing methods for coping with missing LST, the first spatial and after temporal aggregation with threshold removal (FSAT-T) method is demonstrated to provided improved robustness to data missing over other methods. Daytime LSUHII is over 0.1 K in 62% of cities, and nighttime LSUHII is over 0.1 K in 45% of cities. Strong latitudinal and seasonal variations of LSU

关键词： Surface urban heat island Background reference cloud coverage Google Earth Engine Quality control

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Numerical study of aerosol radiative forcing over East Asia and the impacts of cloud coverage and relative humidity

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ATMOSPHERIC RESEARCH 2022年 273卷

作者： Guo, Jun Yin, Yan Xu, Meng Wu, Jian Liu, Duanyang Fan, Wenxuan Lu, Peng Jiangsu Climate Ctr Nanjing 210008 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Key Lab Aerosol Cloud Precipitat China Meteorol A Nanjing 210044 Peoples R China Yunnan Univ Dept Atmospher Sci Key Lab Atmospher Environm & Proc Boundary Layer Kunming 650091 Yunnan Peoples R China China Meteorol Adm Key Lab Transportat Meteorol Nanjing 210008 Peoples R China

The spatiotemporal characteristics of aerosol direct radiative forcing (RF) and the relative contributions from aerosol species, as well as the impacts of cloud coverage and relative humidity on aerosol direct RF were quantified in East Asia using a regional climate model. Generally, the total aerosol produces net RFs of -12.78 W m- 2 at surface, 1.72 W m- 2 at TOA (top-of-atmosphere), and atmospheric heating of 14.50 W m- 2. It was found that dust, black carbon, and sulfate made dominant contributions to the total RF at surface and TOA, and all aerosol species induced atmospheric heating, whereas more than 96% of which was induced by dust and black carbon. The remarkably seasonally decreasing tendency of the total and the absorbing aerosol RFs was found from spring to winter at surface. Moreover, dust contributes relatively larger to the positive TOA RF and to the atmospheric heating in spring and summer, which were weakened and smaller than black carbon in other seasons. Sensitivity studies further demonstrated cloud strengthens the dust and black carbon direct RF and weakens the other species direct RF at TOA, while induces weak direct RFs of all aerosol species at surface. Particularly, cloud induced larger reduction in dust longwave RF than shortwave leads to remarkable enhanced net surface direct RF of dust, especially in JJA. The aerosol swelling effect induced by relative humidity strengthens aerosol direct RF at both TOA and surface. The percentage changes in aerosol RF and its seasonal amplitude by cloud are considered larger at TOA than surface, however, the effects of relative humidity distribute relatively uniform vertically. Meteorological factors impact on scattering aerosols direct RF is assumed larger than absorbing aerosols. The impacts of cloud on aerosol direct RF are compared to the relative humidity and are supposed to be more important at TOA and surface.

关键词： Aerosol direct radiative forcing cloud coverage Water vapor Aerosol swelling effect

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Bi-Spectral Infrared Algorithm for cloud coverage over Oceans by the JEM-EUSO Mission Program

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SENSORS 2021年第19期21卷 6506-6506页

作者： Santalices, David Briz, Susana de Castro, Antonio J. Lopez, Fernando Univ Carlos III Madrid Lab Infrared Av Univ 30 Madrid 28911 Spain

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrat

关键词： remote sensing infrared camera cloud coverage split-window algorithm JEM-EUSO

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Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China's Seas: Impacts of cloud coverage and Sediment Variations

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REMOTE SENSING 2020年第12期12卷 1945-1945页

作者： Tian, Liqiao Sun, Xianghan Li, Jian Xing, Qianguo Song, Qingjun Tong, Ruqing Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Nanjing Univ Informat Sci & Technol NUIST Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Chinese Acad Sci Yantai Inst Coastal Zone Res Key Lab Coastal Environm Proc & Ecol Remediat Yantai 264003 Peoples R China State Ocean Adm Key Lab Space Ocean Remote Sensing & Applicat Beijing 10089 Peoples R China

Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013-2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China's seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (P-bias) at 14.1% (+/- 2.6%), 7.6% (+/- 2.3%) and 12.2% (+/- 4.3%) for TSS of the ECS, BS and YS, respectively. cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly P(bias)ranging from 6.5% (+/- 7.4%) to 20% (+/- 13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant "missing trends" effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated th

关键词： ocean color remote sensing sampling uncertainty GOCI Terra Aqua MODIS cloud coverage suspended sediment

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Sky Image forecasting with Generative Adversarial Networks for cloud coverage prediction 10

Sky Image forecasting with Generative Adversarial Networks f...

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10th International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Andrianakos, George Tsourounis, Dimitrios Oikonomou, Spiros Kastaniotis, Dimitris Economou, George Kazantzidis, Andreas Univ Patras Dept Phys Patras Greece IRIDA Labs SA Comp Vis Syst Patras Greece

ISBN: (纸本)9781728149592

This work focuses on the problem of sky image prediction using Generative Adversarial Networks (GANs). Sky images observation is used for cloud coverage short-term forecasting and therefore by predicting the future frames of a sky image sequence, a better estimate of the available solar resource for renewables could be achieved. For this purpose, a Deep Convolutional Neural Network (CNN) topology is trained with an Adversarial loss in order to produce realistic future frames of the input image sequences. The prediction of future frames is incurred by two different approaches. In the first approach, a network is receiving 4 frames and predicting the next frame and in the second approach, given a sequence of 8 input images a network is predicting the next 8 frames. In order to evaluate the quality of the generated images, a criterion based on cloud coverage is proposed. This cloud coverage metric provides an objective criterion for assessing the quality of the generated sky images, especially in the presence of clouds.

关键词： All-sky image prediction Generative Adversarial Networks cloud coverage

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Ground Sky Imager Based Short Term cloud coverage Prediction 1

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4th European-Alliance-for-Innovation (EAI) International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures (FABULOUS)

作者： Hensel, Stefan Marinov, Marin B. Schwarz, Raphael Topalov, Ivan Univ Appl Sci Offenburg Dept Elect Engn Badstr 24 D-77652 Offenburg Germany Tech Univ Sofia Dept Elect 8 Kliment Ohridski Blvd Sofia 1756 Bulgaria

ISBN: (数字)9783030239763

ISBN: (纸本)9783030239763;9783030239756

The paper describes a systematic approach for a precise short-time cloud coverage prediction based on an optical system. We present a distinct preprocessing stage that uses a model based clear sky simulation to enhance the cloud segmentation in the images. The images are based on a sky imager system with fish-eye lens optic to cover a maximum area. After a calibration step, the image is rectified to enable linear prediction of cloud movement. In a subsequent step, the clear sky model is estimated on actual high dynamic range images and combined with a threshold based approach to segment clouds from sky. In the final stage, a multi hypothesis linear tracking framework estimates cloud movement, velocity and possible coverage of a given photovoltaic power station. We employ a Kalman filter framework that efficiently operates on the rectified images. The evaluation on real world data suggests high coverage prediction accuracy above 75%.

关键词： cloud coverage High dynamic range images Prediction algorithms Short term irradiance prediction

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