Improving Land Surface Temperature and Emissivity Retrieval From the Chinese Gaofen-5 Satellite Using a Hybrid Algorithm

Ren, Huazhong; Ye, Xin; Liu, Rongyuan; Jian, Dong; Qin, Qiming

doi:10.1109/tgrs.2017.2758804

Cited by 60 publications

(37 citation statements)

References 48 publications

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“…For OI system, the best horizontal correlation length was found to be 50 km [58]. For Nudging data assimilation system, the best depth of influence and time scale were found to be 4.0 m and 1800 s, respectively [54]. For indirect data assimilation via correcting wind stress, the best assimilation coefficient is −1 [41].…”

Section: Resultsmentioning

confidence: 94%

“…Other data assimilation cycle lengths less than 1 h were examined as well, but the differences were not significant. The main reasons for assimilating data over shorter temporal intervals into models are presented by Ren and Hartnett [54]. The best data assimilation cycle length was found to be at each model computational time step for DI, OI and Nudging; the best data assimilation cycle length was one minute for indirect data assimilation via correcting wind stress.…”

Section: Resultsmentioning

confidence: 99%

“…For details of the procedure using with IDA and OI data assimilation algorithm, see Ren et al [41] and Ren et al [58], respectively. The same procedure was used to determine data assimilation cycle length for assimilation models using the Nudging algorithm by Ren and Hartnett [54].…”

Section: Resultsmentioning

confidence: 99%

“…For simplicity, the following notation is used in this paper: (a) DI, data assimilation using the Direct Insertion algorithm [52]; (b) OI, data assimilation using the Optimal Interpolation algorithm [53]; (c) NDA, data assimilation using Nudging algorithm [54]; (d) IDA, data assimilation using indirect data assimilation algorithm through correcting wind stress [41]; and (e) FR, a "Free Run", a benchmark model without data assimilation implemented. All the initial and boundary conditions are the same in all the above models.…”

Section: Data Assimilation Algorithmsmentioning

confidence: 99%

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Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

Ren

Hartnett

2017

Remote Sensing

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A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the ≥6 h forecasting period from the best model NDA attained 26% and 31% for east-west and north-south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Data Assimilation Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

Ren

Hartnett

2017

Remote Sensing

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“…With development of earth observation, satellite remote sensing data have been extensively used for numerous studies [1,2], such as land surface temperature retrieval [3][4][5][6], agro-drought monitoring [7], soil moisture estimation [8], evapotranspiration modelling [9] and radiation flux estimation [10]. Since the first Landsat sensor TM (Thematic Mapper) was launched in 1982, Landsat data are referred to as one of the most widely and frequently used imagery sources in land cover study for their availability, relatively high spatial resolution and wide spectral range [11,12].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach to Remove Thick Cloud in VNIR Bands of Multi-Temporal Remote Sensing Images

Du¹,

Qin²,

Fan³

et al. 2019

Remote Sensing

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Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images.

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RETRACTED ARTICLE: Multi-model fusion based satellite image classification using versatile unsupervised vector zone (VUVZ) fusion and intensive pragmatic blossoms (IPB) technique

Swathika¹,

Sharmila²

2019

Multimed Tools Appl

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Improving Land Surface Temperature and Emissivity Retrieval From the Chinese Gaofen-5 Satellite Using a Hybrid Algorithm

Cited by 60 publications

References 48 publications

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

Comparative Study on Assimilating Remote Sensing High Frequency Radar Surface Currents at an Atlantic Marine Renewable Energy Test Site

An Efficient Approach to Remove Thick Cloud in VNIR Bands of Multi-Temporal Remote Sensing Images

RETRACTED ARTICLE: Multi-model fusion based satellite image classification using versatile unsupervised vector zone (VUVZ) fusion and intensive pragmatic blossoms (IPB) technique

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