The 1km / 5day vegetation index (NDVI / EVI) data set in Heihe River basin provides a 5-day resolution NDVI / EVI synthetic product from 2011 to 2014. The data uses the characteristics of domestic satellite FY-3 data with high temporal resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set, Based on the analysis of multi-source data sets and existing synthetic vegetation index products and algorithms, a global synthetic vegetation index product algorithm system with 1km resolution and 5-day cycle based on multi-source data sets is proposed
The vegetation index synthesis algorithm basically adopts the vegetation index synthesis algorithm of MODIS, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on semi empirical waltall model. Using the algorithm system, the synthetic vegetation index is calculated for the primary data and secondary data respectively, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, the observation quality of multi-source data sets is uneven due to the differences in on orbit running time and performance of sensors. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which are divided into primary data, secondary data and tertiary data according to the observation rationality. Level III data are observations polluted by thin clouds and are not used for calculation
The verification results in farmland and forest areas in the middle reaches of Heihe River show that the NDVI / EVI synthesis results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 products, it fully shows that when the time resolution is increased from 16 days to 5 days, the stable and high-precision vegetation index can describe the details of vegetation growth in detail
In short, the 1km / 5day synthetic vegetation index (NDVI / EVI) data set in Heihe River basin makes comprehensive use of multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serve the application of remote sensing data products
The 1km / 5day vegetation index (NDVI / EVI) data set in Heihe River basin provides a 5-day resolution NDVI / EVI synthetic product from 2011 to 2014. The data uses the characteristics of domestic satellite FY-3 data with high temporal resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set, Based on the analysis of multi-source data sets and existing synthetic vegetation index products and algorithms, a global synthetic vegetation index product algorithm system with 1km resolution and 5-day cycle based on multi-source data sets is proposed
The vegetation index synthesis algorithm basically adopts the vegetation index synthesis algorithm of MODIS, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on semi empirical waltall model. Using the algorithm system, the synthetic vegetation index is calculated for the primary data and secondary data respectively, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, the observation quality of multi-source data sets is uneven due to the differences in on orbit running time and performance of sensors. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which are divided into primary data, secondary data and tertiary data according to the observation rationality
Good data quality
| # | number | name | type |
| 1 | 2013AA12A301 | National High-tech R&D Program of China (863 Program) | |
| 2 | 2012AA12A304 | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.ncdc.NIEER.2021.36
10.12072/ncdc.nieer.db3556.2023
