2021
DOI: 10.1515/geo-2020-0241
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Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

Abstract: The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indi… Show more

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Cited by 22 publications
(15 citation statements)
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“…GF-6 has high resolution, large coverage, high quality and multiband. For instance, the red edge data of GF-6 can be used to extract fractional vegetation coverage [48] and its wide image can realize tree species classification [49]. Moreover, Li et al pointed out that GF-6 data could play an important role in the field of fire monitoring and post-disaster assessment [50].…”
Section: Satellite Datamentioning
confidence: 99%
“…GF-6 has high resolution, large coverage, high quality and multiband. For instance, the red edge data of GF-6 can be used to extract fractional vegetation coverage [48] and its wide image can realize tree species classification [49]. Moreover, Li et al pointed out that GF-6 data could play an important role in the field of fire monitoring and post-disaster assessment [50].…”
Section: Satellite Datamentioning
confidence: 99%
“…The satellite adds the "red edge" band and ultraviolet band to the WFV sensor, and the observation width reaches 800 km. This has played an important role in effectively reflecting the specific spectral characteristics of crops, natural resource investigation and monitoring, disaster prevention and mitigation (Deng et al, 2021;Xia et al, 2022). GF-6/PMS images of L1A from 2019 to 2021 used in this study are from the China Ocean Satellite Data Service (https://osdds.nsoas.org.cn/).…”
Section: Satellite Datamentioning
confidence: 99%
“…FVC was calculated by pixel bisection model (Li, 2003;Deng et al, 2021). using 5% confidence interval, NDVIs is the NDVI value with 5% cumulative percentage, and NDVIv is the NDVI value with 95% cumulative percentage.…”
Section: Leaf Area Index (Lai) and Fractional Vegetation Coverage (Fvc) Estimation Based On Thermal Imagesmentioning
confidence: 99%