Impacts of Extreme-High-Temperature Events on Vegetation in North China

Yang, Qingran; Jiang, Chao; Ding, Ting

doi:10.3390/rs15184542

Cited by 4 publications

(3 citation statements)

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“…The Qinghai Tibet Plateau, known as the roof of the world, is located in the southwest of the study area, with an average elevation of 4000 m (Figure 1a). There are mainly six land cover types across China, including forest, grassland, farmland, barren, urban lands and water bodies, as shown in Figure 1b Over the past several decades, China has experienced a rapid increase in extreme climate event occurrences in the context of global climate change, which have led to spatio-temporal changes in vegetation coverage and other vegetation activities [21,22]. Therefore, it is urgent and of great importance to achieve accurate and continuous NDVI dataset on a regional scale for China's vegetation monitoring and carbon sequestration detection.…”

Section: Study Areamentioning

confidence: 99%

“…To produce a highly homogenous and long time series NDVI product for vegetation applications in fields such as climate change, the National Satellite Meteorological Center (NSMC) adopted the unified calibration method to achieve the Retrospective Calibration of Historical Chinese Earth Observation Satellites (RCH-CEOS) NDVI dataset. The RCH-CEOS NDVI product was derived from the calibrated Level 1 reflectance of FY-3A, FY-3B MERSI and FY-3D MERSI-II, with spatial resolutions of 1 km and 250 m. The maximum value composite (MVC) approach was applied to generate the composite NDVI maps Over the past several decades, China has experienced a rapid increase in extreme climate event occurrences in the context of global climate change, which have led to spatiotemporal changes in vegetation coverage and other vegetation activities [21,22]. Therefore, it is urgent and of great importance to achieve accurate and continuous NDVI dataset on a regional scale for China's vegetation monitoring and carbon sequestration detection.…”

Section: Rch-ceos Ndvi Product Derived From Fengyun Satellitesmentioning

confidence: 99%

“…The generated FY-3 long-term NDVI dataset could contribute to research in fields such as climate change, ecological environment detection, crop growth monitoring, phenological extraction and carbon sequestration trend analysis. For example, long-term NDVI time series data, together with other climatic data, can be applied to detect the effect of extreme events on vegetation activities [21]. This information is quite crucial for local ecosystem conservation, especially in climate-sensitive regions.…”

Section: Practical Applicationsmentioning

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: Study Areamentioning

confidence: 99%

Section: Rch-ceos Ndvi Product Derived From Fengyun Satellitesmentioning

confidence: 99%

Section: Practical Applicationsmentioning

confidence: 99%

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