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Water vapor absorption coefficients in the first four bands of the MERSI sensor of the meteorological satellite FY-3D.
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NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spe...
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Context 1
... the above formula, U is the content of atmospheric water vapor, which was taken as 2.93 g/cm 2 , and A and B are water vapor absorption coefficients, as observed in Table 2. ...Context 2
... the above formula, U H is the content of atmospheric water vapor, which was taken as 2.93 g/cm 2 , and A H and B H . are water vapor absorption coefficients, as observed in Table 2. Scattered atmospheric molecules are not absorbed, and the asymmetry factor is 0. The atmospheric downward radiation transmittance, T ↓ R (µ s ) , and the uplink radiation transmittance, T ↑ R (µ v ) , can be obtained using the following formula: ...Citations
... 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. ...
... 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]. ...
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.
Medium-resolution satellites have been instrumental in monitoring global vegetation dynamics over the past decades. The Fengyun (FY) 3D satellite, a second-generation medium-resolution polar-orbiting meteorological satellite launched by the China National Meteorological Administration in 2017, plays a pivotal role in the low-orbiting group network for meteorological, oceanic, and land surface observations. MERSI-II, a key component of FY-3D designed with inspiration from MODIS, holds significant yet untapped potential for analyzing vegetation dynamics. This study embarks on a systematic analysis of FY-3D MERSI-II’s applicability in vegetation research, comparing it with Aqua MODIS. First, the spectrums of MERSI-II and MODIS are very close to each other, and compared to MODIS, MERSI-II is slightly overestimated in Red and slightly underestimated in NIR bands; both reflectance products maintain a good temporal stability when examined through desert sites, with the data being more fluctuating when the observation angle is larger; and the data availability for MERSI-II is slightly lower than that of MODIS, due to its more stringent cloud detection algorithm. Finally, the results of EVI2 (enhanced vegetation index with two bands) and the vegetation parameter GVF (green vegetation fraction) show that MERSI-II is also capable of monitoring vegetation dynamics with an optimal temporal resolution of 12 days and a spatial resolution of 2 km. Our comprehensive assessment confirms the remarkable capability of FY-3D MERSI-II in dynamic vegetation monitoring and underscores the need to make the most of its valuable observations. Our findings support the advancement of vegetation monitoring techniques and aid in adjusting the optimal spatial and temporal resolution of related products.
