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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Meissner, Thomas Ricciardulli, Lucrezia Manaster, Andrew Wentz, Frank Remote Sensing Syst Santa Rosa CA 95401 USA

ISBN: (纸本)9798350320107

Our presentation describes the creations of a match-up set between the SMAP (Soil Moisture Active Passive) and WindSat sensors. The combined set is used for developing and testing ocean retrieval algorithms for sea surface temperature (ST), sea surface salinity (SSS) and ocean surface vector winds (OSVW) that will be measured by the ESA Copernicus Imaging Microwave Radiometer (CIMR).

关键词： Copernicus Imaging Microwave Radiometer Soil Moisture Active Passive WindSat retrieval algorithms sea surface temperature sea surface salinity ocean surface vector winds radiative transfer model

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A study of multiyear ice concentration retrieval algorithms using AMSR-E data

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Acta Oceanologica Sinica 2015年第9期34卷 102-109页

作者： HAO Guanghua SU Jie Key Laboratory of Physical Oceanography Ocean University of China

In recent years, the rapid decline of Arctic sea ice area （SIA） and sea ice extent （SIE）, especially for the multiyear （MY） ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0 GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square （rms） difference of MY SIA derived from the algorithm of this study are 0.65×10^6 km^2 and 0.69×10^6 km^2 during January to March, -0.06×10^6 km^2 and 0.14×10^6 km^2during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×10^6 km^2 and 0.84×10^6 km^2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang＇s, Lomax＇ and NASA TEAM algorithms.

关键词： multiyear ice concentration retrieval algorithms AMSR-E data

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Evaluation of chlorophyll-a retrieval algorithms based on MERIS bands for optically varying eutrophic inland lakes

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SCIENCE OF THE TOTAL ENVIRONMENT 2015年 530卷 373-382页

作者： Lyu, Heng Li, Xiaojun Wang, Yannan Jin, Qi Cao, Kai Wang, Qiao Li, Yunmei Nanjing Normal Univ Coll Geog Sci Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China Chongqing Inst Surveying & Planning Land Resource Chongqing 400020 Peoples R China Natl Univ Singapore Dept Geog Singapore 117570 Singapore Minist Environm Protect Satellite Environm Applicat Ctr Beijing 100029 Peoples R China

Fourteen field campaigns were conducted in five inland lakes during different seasons between 2006 and 2013, and a total of 398 water samples with varying optical characteristics were collected. The characteristics were analyzed based on remote sensing reflectance, and an automatic cluster two-step method was applied for water classification. The inland waters could be clustered into three types, which we labeled water types I, II and III. From water types I to III, the effect of the phytoplankton on the optical characteristics gradually decreased. Four chlorophyll-a retrieval algorithms for Case II water, a two-band, three-band, four-band and SCI (Synthetic Chlorophyll Index) algorithm were evaluated for three water types based on the MERIS bands. Different MERIS bands were used for the three water types in each of the four algorithms. The four algorithms had different levels of retrieval accuracy for each water type, and no single algorithm could be successfully applied to all water types. For water types I and III, the three-band algorithm performed the best, while the four-band algorithm had the highest retrieval accuracy for water type II. However, the three-band algorithm is preferable to the two-band algorithm for turbid eutrophic inland waters. The SCI algorithm is recommended for highly turbid water with a higher concentration of total suspended solids. Our research indicates that the chlorophyll-a concentration retrieval by remote sensing for optically contrasted inland water requires a specific algorithm that is based on the optical characteristics of inland water bodies to obtain higher estimation accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Chlorophyll-a concentration retrieval algorithms MERIS bands Optical clustering Accuracy evaluation

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Lens less shadow imaging via fusion of phase retrieval and image reconstruction from correlations algorithms

Lens less shadow imaging via fusion of phase retrieval and i...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Cain, Stephen C. Air Force Institute of Technology United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The recent global pandemic has served to intensify interest in analyzing microdroplets suspended in the air that can possibly carry pathogens thus demonstrating an airborne transmission mechanism with potentially devastating consequences. Although COVID-19 turned out to be not as virulent as some other diseases encountered in the past, the possibility of airborne transmission of diseases remains a substantial concern. In this paper the potential for an optical sensor to image airborne particles is investigated. The proposed sensor would use optical radiation to probe the atmosphere through a pinhole aperture. Simulations conducted as part of this research reveal that droplets of the diameter in the range of micrometers can be imaged using the proposed technique, thus potentially allowing for the content of the air to be characterized with regard to its droplet size distribution and density. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Optical Sensor Optical Radiation Transmittance retrieval algorithms Iterative Algorithm Image Formation Computing

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A Novel Land Surface Temperature retrieval Algorithm for SDGSAT-1 Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Li, Na Xu, Jianhui Li, Xu Qin, Boxiong Wang, Yunpeng Fu, Dongjie Zhong, Kaiwen Qin, Zhihao Chinese Acad Sci Guangzhou Inst Geochem State Key Lab Organ Geochem Guangzhou 510640 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing 100049 Peoples R China Guangdong Acad Sci Guangdong Engn Technol Res Ctr Remote Sensing Big Guangzhou Inst Geog Guangdong Open Lab Geospatial Informat Technol & A Guangzhou 510070 Peoples R China Chinese Acad Sci Guangdong Prov Key Lab Appl Bot South China Bot Garden Guangzhou 510650 Peoples R China Chinese Acad Sci Key Lab Vegetat Restorat & Management Degraded Eco South China Bot Garden Guangzhou 510650 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Chinese Acad Agr Sci Inst Agroresources & Reg Planning Lab Agr Ecol Beijing 100081 Peoples R China

Land surface temperature (LST) is a crucial parameter influencing Earth-atmosphere interactions and energy balance processes. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) was recently launched to support the realization of the United Nations Sustainable Development Goals (SDGs), which provides worldwide three-spectrum wide-swath, high-resolution, and high-sensitivity thermal infrared (TIR) images. The objective of this study is to develop a modified three-channel split-window algorithm incorporating atmospheric water vapor content (W-TCSW) for LST retrieval from SDGSAT-1 images. This algorithm was developed from the existing split-window (SW) form. The parameters of the algorithm were determined based on the MODerate resolution atmospheric TRANsmission (MODTRAN) simulation results of 946 Thermodynamic Initial-Guess retrieval (TIGR) atmospheric profiles. The W-TCSW algorithm was comprehensively compared with the SW and three-channel SW (TCSW) algorithms. The retrieval results of the three algorithms were validated with simulated datasets and in situ measurements from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites in China and the Surface Radiation Budget Network (SURFRAD) sites in USA. The SDGSAT-1 data retrieved by the W-TCSW algorithm was also intercompared with Landsat and ECOSTRESS LST products. The W-TCSW algorithm demonstrated the highest accuracy among the three retrieval algorithms (SW, TCSW, and W-TCSW). The influences of atmospheric water vapor content (AWVC) and land surface emissivity (LSE) as well as land use and land cover (LULC) on retrieval algorithms were discussed in a long-term time series. This study introduces a novel LST retrieval algorithm considering AWVC for SDGSAT-1 images and elucidates comprehensive validation and comparative assessment, expanding the application of high-spatial resolution TIR remote sensing data.

关键词： Land surface temperature Spatial resolution Land surface Grasslands Sensors Remote sensing MODIS Atmospheric measurements Landsat Vegetation mapping Land surface temperature (LST) retrieval algorithms Sustainable Development Goals Science Satellite 1 (SDGSAT-1) validation

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Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

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REMOTE SENSING OF ENVIRONMENT 2024年 300卷

作者： Eckert, Regina Mauceri, Steffen Thompson, David R. Fahlen, Jay E. Brodrick, Philip G. CALTECH Jet Prop Lab Pasadena CA 91125 USA

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for neighboring pixels, resulting in surface-related biases in the retrieved surface and atmospheric parameters. In this work, we present a mathematical framework to more accurately retrieve surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. This framework retrieves the full spatial- spectral reflectance data cube in a computationally efficient manner and is to our knowledge the first approach capable of accurately finding this global solution for large imaging spectroscopy datasets. We implement this mathematical framework in an example proposed algorithm which we call spatially constrained optimal estimation (SCOE). We show that SCOE improves the overall surface reflectance retrieval error, reduces surface -related biases in the retrieved surface reflectance and atmospheric parameters, and is more robust to noise than a traditional pixel-by-pixel approach and an operational empirical line approach in simulated and experimental results. This reduction in surface reflectance error will impact science data products across application areas, making imaging spectroscopy algorithms more robust for global acquisitions.

关键词： Imaging spectroscopy VSWIR retrieval algorithms Computational imaging Remote sensing Earth science

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THE retrieval OF LAND AEROSOL OPTICAL DEPTH BASED ON MACHINE LEARNING APPROACH OVER SOUTH ASIA

THE RETRIEVAL OF LAND AEROSOL OPTICAL DEPTH BASED ON MACHINE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Yuan, Min Wu, Xiaolin Wang, Zhaobin Li, Jun Zhang, Yaonan Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Natl Cryosphere Desert Data Ctr Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China

ISBN: (纸本)9798350360332;9798350360325

Aerosol optical depth is a crucial parameter that characterizes the impact of aerosols on light attenuation. This parameter serves as the fundamental prerequisite for quantitative remote sensing studies and holds significant importance in climate change research, atmospheric pollution monitoring, and environmental governance. In this article, four machine learning models, namely, LSTM, RF, XGBoost, and LightGBM, were used to achieve aerosol optical depth retrieval using MODIS data as input features. Spatiotemporal cross-validation was used for model training and evaluation, and the results showed that the LSTM model had the best performance among the four models. In independent sites validation, both the LSTM and LightGBM models exhibited superior performance compared to the MODIS Deep Blue algorithm, indicating that the machine learning methods will contribute to enhancing the capability of satellite remote sensing for aerosol optical depth retrieval.

关键词： Aerosol optical depth machine learning satellite remote sensing retrieval algorithms

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Comparison of retrieval algorithms for the Wet Tropospheric Path Delay

Comparison of Retrieval Algorithms for the Wet Tropospheric ...

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13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)

作者： Thao, S. Obligis, E. Picard, B. Frery, M-L. Eymard, L. CLS Space Oceanog Div Ramonville St Agne France LOCEAN CNRS IPSL Paris France

ISBN: (纸本)9781479946440

This paper provides a comparative analysis of statistical algorithms for the retrieval of the wet tropospheric correction from microwave radiometers in the context of altimetry missions. The algorithms are based on the algorithms used in Envisat and Jason-1 missions. The objective of this comparison is two-folds: 1) To find which regression method is better suited for the retrieval between the neural network algorithm and the log linear regression. 2) To tackle the problem of variable selection, i.e. to find the best set of variables to include as inputs in order to reduce the retrieval error. In particular, we want to determine whether the lack of a radiometer third channel at 18GHz can be compensated by the altimeter backscattering coefficient. Several configurations of algorithms, including those used in the operational processing of altimetry missions such as JASON-1 or ENVISAT, are built and compared on the same learning and test databases to determine which retrieval strategy is more appropriate. The database is composed of atmospheric and oceanographic conditions taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and the brightness temperatures are simulated using a radiative transfer model. The importance of each input for the different algorithms is analyzed and the performances of the different algorithms are assessed in terms of error (bias and standard deviation) but also in terms of geographical distribution of the errors and correlation with other environmental variables. The results are then validated on Jason-2 radiometer measurements. Our results show that, in terms of variable selection, better results were obtained when the brightness temperature at 18 GHz was used instead of the backscattering coefficient. Moreover, better estimations of the wet tropospheric path delay were obtained with neural networks.

关键词： wet tropospheric path delay water vapor retrieval algorithms artificial neural networks

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Shadow Imaging Reconstruction from Partial Diffraction Patterns

Shadow Imaging Reconstruction from Partial Diffraction Patte...

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AIAA SciTech Forum

作者： Ruyle, Douglas B. Curtis, David H. McMahon-Crabtree, Peter N. Air Force Inst Technol Dept Aeronaut & Astronaut 2950 Hobson Way Wright Patterson AFB OH 45433 USA USAF Res Lab 3550 Aberdeen Av SE Kirtland AFB NM 87117 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Shadow imaging is an established astronomical observation technique that reconstructs silhouettes of distant space objects (like asteroids) from the object's diffraction pattern which is present in the shadow of the object as the object passes between the observer and a star. The application of this technique as a low-cost method to obtain fine resolution silhouette images of satellites in the geosynchronous belt using an array of hobby telescopes is referred to as Synthetic Aperture Silhouette Imaging (SASI). This research shows that a SASI array may be constructed with up to three spaces between telescopes where each space is the same size as the telescopes while maintaining sub-meter resolution.

关键词： Diffraction Telescopes Satellites Asteroids MATLAB Space Situational Awareness retrieval algorithms GPS satellites Resident Space Object Image Resolution

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Advancing Soil Moisture Estimation with Enhanced SMAP Active/Passive Algorithm for SMAP/NISAR Combined Framework

Advancing Soil Moisture Estimation with Enhanced SMAP Active...

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2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

作者： Xu, Xiaolan Das, Narendra Yueh, Simon Entekhabi, Dara Colliander, Andreas California Institute of Technology Jet Propulsion Laboratory Pasadena United States Michigan State University Civil and Environmental Engineering LansingMI United States Massachusetts Institute of Technology Civil and Environmental Engineering CambridgeMA United States

ISBN: (纸本)9798350360325

This paper presents a refined Active and Passive (AP) algorithm from the Soil Moisture Active Passive (SMAP) mission, highlighting the progressive enhancements made to the passive algorithm over the years. The primary focus centers on the process of disaggregating coarse brightness temperature (TB) directly measured from the radiometer to attain fine-resolution TB, subsequently enabling the retrieval of soil moisture and vegetation optical depth. Throughout the operational phase of the SMAP SAR instrument, approximately 2.5 months of global SAR backscattering data were acquired simultaneously with TB data. With the imminent launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, the availability of continuous L-band SAR data will see a significant boost. The original SMAP SAR data encompassed four polarizations (VV, HH, HV, and VH), which prompted an examination of three disaggregation combinations: 1) The original SMAP AP algorithm, which utilizes HH, VV, and cross-polarization (X-pol) data (averaged from cross-polarizations). 2) Sole reliance on HH and X-pol data, a configuration that aligns with the capabilities of the NISAR mission, offering global coverage. 3) VV and X-pol data, aiming to provide a more comprehensive analysis. Across these three combinations, similar accuracy was observed at the core study sites, affirming the feasibility of utilizing NISAR HH/HV data exclusively for the AP algorithm. Additionally, this paper also demonstrates both the snapshot method and time-series method for parameter determination and engages in the discussion of their respective advantages and disadvantages. © 2024 IEEE.

关键词： Active and Passive NISAR retrieval algorithms SMAP soil moisture vegetation optical depth

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