A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data

Xiao, Fengjin; Liu, Qiufeng; Li, Shuai; Yun, Qin; Huang, Dapeng; Wang, Yanjiao; Wang, Lei

doi:10.3390/rs15020491

Cited by 2 publications

(2 citation statements)

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“…The FY-3 series satellites, including the FY-3A, FY-3B, FY-3C, FY-3D, FY-3E, FY-3G, FY-3F and additional satellites to be launched in the next few years, are China's secondgeneration polar-orbiting meteorological satellites. These satellites are equipped with more than ten sets of advanced remote sensing instruments, such as the visible and infrared radiometer (VIRR), global navigation occultation sounder (GNOS), microwave radiometric imager (MWRI), high spectral infrared atmospheric Sounder (HIRAS), microwave temperature sounder (MWTS) and microwave humidity sounder (MWHS), thus enabling them to collect the quantitative, multispectral, three-dimensional and all-weather earth surface characteristic parameters on a global scale [11,12]. Among these instruments, the MERSI and MERSI-II sensors were designed as key visible and infrared spectral imaging instruments for detecting the atmospheric, terrestrial and oceanic features, and providing important operational products such as the outgoing longwave radiation (OLR), precipitable water vapor (PWV) and NDVI.…”

Section: Introductionmentioning

confidence: 99%

“…Cross-comparisons among vegetation indices (VIs) derived from different satellites and sensors is of great significance for their application, calibration and cooperative inversion using multisource remote sensing data [11]. Many previous studies have utilized methods such as regression analysis to recognize differences between VIs extracted from different satellite systems and sensors [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

Wang,

Han,

Fang

et al. 2024

Remote Sensing

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NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

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Section: Introductionmentioning

confidence: 99%