Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

被引:3
作者
Sun, Yuanheng [1 ]
Wang, Binyu [1 ]
Teng, Senlin [1 ]
Liu, Bingxin [1 ]
Zhang, Zhaoxu [2 ]
Li, Ying [1 ]
机构
[1] Dalian Maritime Univ, Environm Informat Inst, Nav Coll, Dalian 116026, Peoples R China
[2] Tiangong Univ, Sch Environm Sci & Engn, Tianjin 300387, Peoples R China
基金
中国国家自然科学基金;
关键词
Landsat-8; Landsat-9; reflectance; NDVI; continuity; time series; THEMATIC MAPPER PLUS; LAND IMAGER OLI; VEGETATION INDEXES; GENERAL-METHOD; ETM PLUS; VALIDATION; NORMALIZATION; PERFORMANCE; RETRIEVAL; ALGORITHM;
D O I
10.3390/rs15204948
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat's calibrated Earth observations. This study comprehensively assesses the continuity of reflectance and the Normalized Difference Vegetation Index (NDVI) between Landsat-8 and Landsat-9 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact reflectance and NDVI consistency. Although Landsat-9's top-of-atmosphere (TOA) reflectance is slightly lower than that of Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR and SWIR bands (and larger RMSE for NDVI at 0.0422) fall within acceptable ranges for Earth observation applications. Applying atmospheric corrections markedly enhances reflectance uniformity and brings regression slopes closer to unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, ensuring measurement reliability, and the NDVI maintains robust consistency across various reflectance types, time series, and land cover classes. These findings affirm Landsat-9's success in achieving data continuity within the Landsat program, allowing interchangeable use of Landsat-8 and Landsat-9 OLI data for diverse Earth observation purposes. Future research may explore specific sensor correlations across different vegetation types and seasons while integrating data from complementary platforms, such as Sentinel-2, to enhance the understanding of data continuity factors.
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页数:17
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