Radiometric Characteristics of the Landsat Collection 1 Dataset

• 作者: Ramakrishna R. Nemani Sangram Ganguly Shuang Li Weile Wang
• 来源期刊: 遥感技术进展(英文)
• 学科: 医学
• 关键词: LANDSAT Collection 1 CROSS-CALIBRATION Landsat-4 (L4) THEMATIC MAPPER (TM) Landsat-5 (L5) THEMATIC MAPPER (TM) Landsat-7 (L7) Enhanced THEMATIC MAPPER Plus (ETM+) Landsat-8 (L8) Operational Land IMAGER (OLI)
• 年，卷（期）: 2018,（3）
• 页码范围: 203-217
• 页数: 15页
• 分类号: R73

摘要

This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.