Simulating the directional, spectral and textural properties of a large-scale scene at high resolution using a MODIS BRDF product

Rengarajan, Rajagopalan; Goodenough, Adam A.; Schott, John R.

doi:10.1117/12.2240527

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…MODIS BRDF/albedo, a land level 3 standard data product, was also used in this study. MODIS BRDF products have the characteristics of high time resolution, high spatial resolution, full spectrum coverage, and global imaging [22][23][24]. The core data set of the product is MCD43A1, which implements the RossThick-LiSparseR semi-empirical BRDF model (RTLSR), and provides information on MODIS 1-7 bands and the other three bands.…”

Section: Modis L1b/modis Brdf Datamentioning

confidence: 99%

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Chen

Wang

et al. 2021

Remote Sensing

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High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI.

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Section: Modis L1b/modis Brdf Datamentioning

confidence: 99%

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Chen

Wang

et al. 2021

Remote Sensing

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“…The spectral characteristics are determined by the optical characteristics of the underlying surface type, atmosphere, sunlight, and terminal sensors [5]. The traditional methods based on radiation transfer models [6][7][8] require pre-building a large database of ground features and environmental characteristics. However, it is still difficult to model the complex and random atmosphere and clouds.…”

Section: Introductionmentioning

confidence: 99%

SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain

Wang

Zhu²,

Guo

et al. 2022

Remote Sensing

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The synthesis of spectral remote sensing images of the Earth’s background is affected by various factors such as the atmosphere, illumination and terrain, which makes it difficult to simulate random disturbance and real textures. Based on the shared latent domain hypothesis and generation adversarial network, this paper proposes the SDTGAN method to mine the correlation between the spectrum and directly generate target spectral remote sensing images of the Earth’s background according to the source spectral images. The introduction of shared latent domain allows multi-spectral domains connect to each other without the need to build a one-to-one model. Meanwhile, additional feature maps are introduced to fill in the lack of information in the spectrum and improve the geographic accuracy. Through supervised training with a paired dataset, cycle consistency loss, and perceptual loss, the uniqueness of the output result is guaranteed. Finally, the experiments on the Fengyun satellite observation data show that the proposed SDTGAN method performs better than the baseline models in remote sensing image spectrum translation.

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Simulating the directional, spectral and textural properties of a large-scale scene at high resolution using a MODIS BRDF product

Cited by 2 publications

References 4 publications

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain

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