TY  - Data
T1  - Heihe River eco hydrological remote sensing test: 30m / month synthetic vegetation index (NDVI / EVI) data set of Heihe River Basin (2011-2014)
A1  - li jing
A1  - Liu qinghuo
A1  - Zhong bo
DO  - 10.12072/ncdc.nieer.db3487.2023
PY  - 2021
DA  - 2021-09-14
PB  - National Cryosphere Desert Data Center
AB  - The 30m / month vegetation index (NDVI / EVI) data set of Heihe River basin provides the monthly NDVI / EVI synthesis products from 2011 to 2014. The data uses the characteristics of domestic satellite HJ / CCD data with high temporal resolution (2 days after Networking) and spatial resolution (30M) to construct a multi angle observation data set. The average synthesis MC method is used as the main algorithm for synthesis, and the VI method is used as the standby algorithm. At the same time, the main observation angle of multi-source data set is taken as a part of quality descriptor to assist in analyzing the angle effect of synthetic vegetation index residue. The monthly remote sensing data can provide more angles and more observations than the single day sensor data, but the quality of multi temporal and multi angle observation data is uneven due to the differences in the on orbit operation time and performance of the sensor. Therefore, in order to make effective use of multi temporal and multi angle observation data, before using multi-source data sets for vegetation index synthesis, this algorithm designs the data quality inspection of multi-source data sets to remove large error observations and inconsistent observationsThe verification results in the farmland area 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 (R2 = 0.89, RMSE = 0.092). In short, the 30m / month synthetic vegetation index (NDVI / EVI) data set in Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to realize the stable standardized products from scratch and better serve the application of remote sensing data products
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/4359c938-13bc-4cb0-bcb7-fac75f0022fe
ER  -