Reconstruction of vegetation index time-series data for crops at a 15 m resolution after reflectance normalization of multi-satellite data
AO Yangqian1,2(), SUN Liang1()
1. Institute of Agricultural Resources and Agricultural Zoning，Chinese Academy of Agricultural Sciences/National Key Laboratory for Efficient Utilization of Arid and Semiarid Cultivated Land in the North，Beijing 100081，China 2. Jiangxi Institute of Meteorological Sciences/Key Laboratory of Climate Change Risk and Meteorological Disaster Prevention of Jiangxi Province，Nanchang 330096，China
Changes in the vegetation index can reflect variations in vegetation cover and growth in the region to some extent. Monitoring the changes in vegetation index time-series data plays a significant role in local agricultural management. However，existing methods for vegetation index time-series data reconstruction face challenges such as a single data source input and low spatial resolution of reconstruction results. In response to this，this paper proposes a reconstruction method for vegetation index time-series data that integrates the satellite data standardization method and the crop reference curve method. Consequently，it reconstructed vegetation index time-series data with high spatiotemporal resolution for winter wheat in the study area in 2021，including normalized differential vegetation index （NDVI） and enhanced vegetation index （EVI）. The results show that after reflectance normalization，the coefficient of determination （R2） for GF-1 satellite and VIIRS surface reflectance data in red，green，infrared，and near infrared bands generally increased by 0.05%，with a few exceeding 0.1%. The root mean square error （RMSE） was reduced，with the majority decreasing by 0.01. In contrast，the relative root mean square error （rRMSE） showed a reduction of about 2%. Most data from the GF-6 satellites exhibited an increase of about 0.12 in R2，a decrease of 0.03 in RMSE，and a general decline in rRMSE ranging from 3% to 4%. In contrast，the data from the Sentinel-2 satellite show an overall increase of about 0.05 in R2，as well as a decrease of around 0.001 and 2% in RMSE and rRMSE，respectively. The accuracy assessment results for the reconstructed high-resolution vegetation index time-series data indicate that the NDVI time-series reconstruction results presented high R2 values in the validation period，with five validation images reaching 0.49 and above. The RMSE was less than 0.1 in all validation periods，while the relative error （RE） was less than 15% in most cases，with only one validation image reaching 18%. Similarly，the EVI time-series reconstruction results also exhibited high R2 values，with five validation images above 0.44. Both RMSE and rRMSE values were less than 0.15 and 20%，respectively.
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AO Yangqian, SUN Liang. Reconstruction of vegetation index time-series data for crops at a 15 m resolution after reflectance normalization of multi-satellite data. Remote Sensing for Natural Resources, 2025, 37(5): 206-215.
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2025, Vol. 37 
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