Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)

Li, Shengqi; Han, Xiuzhen; Weng, Fuzhong

doi:10.3390/rs14153817

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The 6S model (the second simulation of the satellite signal in the solar spectrum), which is based on the theory of radiative transfer, can be used to analyze landforms, weather, spectra, and many other parameters synthetically [38]. Owing to its applicability in the different band ranges of various satellite sensors, and because it is resistant to influence by the characteristics of a study area and target type, the 6S model is widely used in various disciplines that include atmospheric radiation studies and remote sensing [39][40][41].…”

Section: Atmospheric Correction (Ac)mentioning

confidence: 99%

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

Xin

Zhu

et al. 2023

Water

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China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.

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Section: Atmospheric Correction (Ac)mentioning

confidence: 99%

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

Xin

Zhu

et al. 2023

Water

Self Cite

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First Look of Surface Vegetation from the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4B

Li,

Han,

Zhang

et al. 2023

J Meteorol Res

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Degree of Polarization Calculation for Laser Backscattering from Typical Geometric Rough Surfaces at Long Distance

et al. 2022

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Measurement of the degree of polarization of backscattering light from rough surfaces plays an important role in targets-detection applications. The polarization bidirectional reflectance function is the key to establish the relation between the polarization states of incidence and backscattering light. For the purpose of obtaining a polarized bidirectional reflectance distribution function (pBRDF) of a realistic, complicated target, it is decomposed as typical geometric surfaces and analytically calculated as the degree of polarization of the backscattering light, using a microfacet model, under conditions in which the scale of the target is far less than the target distance. In an experiment testing several typical geometric models, the results coincided with the theoretical calculation. The degree of polarization varied substantially as the rotation angle of the target changed, but showed little dependence on the size of target. The results have potential in applications discriminating between targets at different spatial orientations.

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Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)

Cited by 3 publications

References 21 publications

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

First Look of Surface Vegetation from the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4B

Degree of Polarization Calculation for Laser Backscattering from Typical Geometric Rough Surfaces at Long Distance

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