Thainara M.A. Lima , Vitor S. Martins , Rejane S. Paulino , Cassia B. Caballero , Daniel A. Maciel , Claudia Giardino
{"title":"用于协调内陆和沿海水域陆地卫星哨兵的一般光谱带通调整函数","authors":"Thainara M.A. Lima , Vitor S. Martins , Rejane S. Paulino , Cassia B. Caballero , Daniel A. Maciel , Claudia Giardino","doi":"10.1016/j.srs.2025.100225","DOIUrl":null,"url":null,"abstract":"<div><div>Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) are the most relevant medium spatial resolution data sources for aquatic applications, and integrating these spectral images into a single product constellation offers significant potential for monitoring dynamic processes over coastal and inland waters. Due to water's inherently low reflectance values, small differences in the relative spectral responses (RSR) between the two sensors can result in significant discrepancies in water reflectance retrievals. To ensure compatibility and consistency in harmonized products for aquatic studies, spectral bandpass adjustment function (SBAF) for cross-calibration between OLI and MSI sensors must be carefully derived and applied. This study provides a global analysis of 4047 match-ups of atmospherically corrected Landsat-8/9 OLI and Sentinel-2 A/B MSI L1 products over inland and coastal waters to generate a new general SBAF for aquatic studies. Atmospheric correction was performed using the 6 S V radiative transfer model, a widely validated approach for aquatic remote sensing applications. The selected images were sensed ≤30 min apart on the same day, under <5 % cloud cover, ≤5° solar zenith difference across different atmospheric conditions and aquatic systems (928 coastal and inland water bodies) over the world. A robust quality-controlled protocol was developed to remove low-quality image pairs under sun/sky glint, ice/snow surface, and cirrus clouds. Following this procedure, 2,2 million quality filtered water reflectance pixels were extracted. The SBAF coefficients (i.e., slope and offset) were derived through statistical regression between Landsat-8/9 and Sentinel-2 reflectance values. Additionally, we simulated sensor band responses using a global in-situ hyperspectral water dataset and calculated the SBAF coefficients for comparison with the pixel-based results. The application of SBAF was demonstrated through comparative analyses of spectral reflectance from Landsat-8/9 and Sentinel-2 before and after the cross-calibration. Our findings underscore the effectiveness of these coefficients in reducing spectral discrepancies between Landsat-8/9 and Sentinel-2 water reflectance measurements.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"11 ","pages":"Article 100225"},"PeriodicalIF":5.7000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A general spectral bandpass adjustment function (SBAF) for harmonizing landsat-sentinel over inland and coastal waters\",\"authors\":\"Thainara M.A. Lima , Vitor S. Martins , Rejane S. Paulino , Cassia B. Caballero , Daniel A. Maciel , Claudia Giardino\",\"doi\":\"10.1016/j.srs.2025.100225\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) are the most relevant medium spatial resolution data sources for aquatic applications, and integrating these spectral images into a single product constellation offers significant potential for monitoring dynamic processes over coastal and inland waters. Due to water's inherently low reflectance values, small differences in the relative spectral responses (RSR) between the two sensors can result in significant discrepancies in water reflectance retrievals. To ensure compatibility and consistency in harmonized products for aquatic studies, spectral bandpass adjustment function (SBAF) for cross-calibration between OLI and MSI sensors must be carefully derived and applied. This study provides a global analysis of 4047 match-ups of atmospherically corrected Landsat-8/9 OLI and Sentinel-2 A/B MSI L1 products over inland and coastal waters to generate a new general SBAF for aquatic studies. Atmospheric correction was performed using the 6 S V radiative transfer model, a widely validated approach for aquatic remote sensing applications. The selected images were sensed ≤30 min apart on the same day, under <5 % cloud cover, ≤5° solar zenith difference across different atmospheric conditions and aquatic systems (928 coastal and inland water bodies) over the world. A robust quality-controlled protocol was developed to remove low-quality image pairs under sun/sky glint, ice/snow surface, and cirrus clouds. Following this procedure, 2,2 million quality filtered water reflectance pixels were extracted. The SBAF coefficients (i.e., slope and offset) were derived through statistical regression between Landsat-8/9 and Sentinel-2 reflectance values. Additionally, we simulated sensor band responses using a global in-situ hyperspectral water dataset and calculated the SBAF coefficients for comparison with the pixel-based results. The application of SBAF was demonstrated through comparative analyses of spectral reflectance from Landsat-8/9 and Sentinel-2 before and after the cross-calibration. 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引用次数: 0
摘要
Landsat 8/9作战陆地成像仪(OLI)和Sentinel-2多光谱成像仪(MSI)是水生应用中最相关的中等空间分辨率数据源,将这些光谱图像集成到单个产品星座中,为监测沿海和内陆水域的动态过程提供了巨大的潜力。由于水固有的低反射率值,两种传感器之间的相对光谱响应(RSR)的微小差异可能导致水反射率检索的显着差异。为了确保用于水产研究的协调产品的兼容性和一致性,必须仔细推导和应用OLI和MSI传感器之间交叉校准的光谱带通调整函数(SBAF)。本研究对内陆和沿海水域4047次大气校正Landsat-8/9 OLI和Sentinel-2 a /B MSI L1产品的匹配进行了全球分析,以生成用于水生研究的新的通用SBAF。大气校正使用6s V辐射传输模型进行,这是一种广泛验证的水生遥感应用方法。选取的图像在全球不同大气条件和水生系统(928个沿海和内陆水体),在5%的云量下,在同一天间隔≤30分钟,太阳天顶差≤5°的情况下进行遥感。开发了一个强大的质量控制协议,以去除太阳/天空闪烁,冰/雪表面和卷云下的低质量图像对。按照这个程序,提取了220万个高质量的过滤水反射像元。通过对Landsat-8/9和Sentinel-2反射率值的统计回归,得到SBAF系数(即斜率和偏移量)。此外,我们使用全球原位高光谱水数据集模拟传感器波段响应,并计算SBAF系数,与基于像素的结果进行比较。通过对比分析Landsat-8/9和Sentinel-2卫星交叉定标前后的光谱反射率,验证了SBAF的应用。我们的发现强调了这些系数在减少Landsat-8/9和Sentinel-2水反射率测量之间的光谱差异方面的有效性。
A general spectral bandpass adjustment function (SBAF) for harmonizing landsat-sentinel over inland and coastal waters
Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) are the most relevant medium spatial resolution data sources for aquatic applications, and integrating these spectral images into a single product constellation offers significant potential for monitoring dynamic processes over coastal and inland waters. Due to water's inherently low reflectance values, small differences in the relative spectral responses (RSR) between the two sensors can result in significant discrepancies in water reflectance retrievals. To ensure compatibility and consistency in harmonized products for aquatic studies, spectral bandpass adjustment function (SBAF) for cross-calibration between OLI and MSI sensors must be carefully derived and applied. This study provides a global analysis of 4047 match-ups of atmospherically corrected Landsat-8/9 OLI and Sentinel-2 A/B MSI L1 products over inland and coastal waters to generate a new general SBAF for aquatic studies. Atmospheric correction was performed using the 6 S V radiative transfer model, a widely validated approach for aquatic remote sensing applications. The selected images were sensed ≤30 min apart on the same day, under <5 % cloud cover, ≤5° solar zenith difference across different atmospheric conditions and aquatic systems (928 coastal and inland water bodies) over the world. A robust quality-controlled protocol was developed to remove low-quality image pairs under sun/sky glint, ice/snow surface, and cirrus clouds. Following this procedure, 2,2 million quality filtered water reflectance pixels were extracted. The SBAF coefficients (i.e., slope and offset) were derived through statistical regression between Landsat-8/9 and Sentinel-2 reflectance values. Additionally, we simulated sensor band responses using a global in-situ hyperspectral water dataset and calculated the SBAF coefficients for comparison with the pixel-based results. The application of SBAF was demonstrated through comparative analyses of spectral reflectance from Landsat-8/9 and Sentinel-2 before and after the cross-calibration. Our findings underscore the effectiveness of these coefficients in reducing spectral discrepancies between Landsat-8/9 and Sentinel-2 water reflectance measurements.