Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces

Wang, Han; Sun, Xiaobing; Yang, Leiku; Zhao, Meiru; Lui, Pei; Du, Wentao

doi:10.1016/j.jqsrt.2018.08.011

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Meteorological data represent important inputs for urban flood models. With the development of remote sensing technology, many meteorological-related data, such as rainfall [81], evapotranspiration [82], and soil moisture [83,84], can be rapidly quantified using retrieval algorithms [85,86].…”

Section: Meteorological Informationmentioning

confidence: 99%

Applications of Advanced Technologies in the Development of Urban Flood Models

Yan

Zhang

2023

Water

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Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system.

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Section: Meteorological Informationmentioning

confidence: 99%

Applications of Advanced Technologies in the Development of Urban Flood Models

Yan

Zhang

2023

Water

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“…The adaptive land-atmospheric decoupling (ALAD) based aerosol retrieval algorithm (Wang et al, 2018) separates the surface and atmosphere reflected radiance based on the spectral relationship for the PRNS. The adaptive method uses iteration to approach the real values for the aerosol and surface polarized reflectance, where an initial estimate is necessary.…”

Section: Retrieval Algorithmmentioning

confidence: 99%

The Normalized Difference Vegetation Index and Angular Variation of Surface Spectral Polarized Reflectance Relationships: Improvements on Aerosol Remote Sensing Over Land

Wang

Yang

Zhao

et al. 2019

Earth and Space Science

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Surface polarized reflectance is related to its optical properties and observation geometry. It disturbs remote sensing of aerosol. In this study, we differentiated the polarized reflectance of a natural surface based on the normalized difference vegetation index and scattering angle, and the interband correlation coefficients were generally higher than that without differentiation. The spectral relationship was determined based on normalized difference vegetation index and scattering angle and then employed for aerosol retrieval from polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a lidar measurements. The retrieved aerosol optical depths (AODs) were contrasted with Aerosol Robotic Network results. The correlation coefficient between the retrieved and Aerosol Robotic Network AODs increased from 0.723 to 0.836, and the root‐mean‐square error reduced from 0.348 to 0.245 after considering the differentiated relationship. Thus, the accuracy of the retrieved AOD values was improved.

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