照射角对于一个瓦片被设置为在Landsat-8和Sentinel-2在该瓦片中心的纬度上空过时的太阳天顶角的平均值;这个角度是使用Li等人(2018年)描述的代码导出的。 bandpass调整 MSI和OLI在等效光谱波段上具有略有不同的波段通量,这些差异需要在HLS产品中移除。OLI光谱波段被用作参考,MSI光谱波段被调整到与之相匹配。波段通量的...
小视频揭示了Landsat 8的Optical Land Imager (OLI)与Sentinel-2的Multi-Spectral Imager (MSI)之间的光谱相似性，这促使了Harmonized Landsat Sentinel-2 (HLS)项目的诞生。该项目旨在整合两种传感器的数据，生成更高频率的陆地表面监测图像，每2-3天就能提供全球30米分辨率的观察。HLSS30和HLSL30产品是...
Spectral similarities between the Landsat 8 Optical Land Imager (OLI) and the Sentinel-2 Multi-Spectral Imager (MSI) present an opportunity to harmonize data from both sensors to generate higher-frequency imagery products for land surface monitoring and applications. HLS 重访时间(橙色线)和计划 Landsa...
This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) ...
Harmonized Landsat Sentinel-2 (HLS) 是 NASA 的一项举措,输入产品是 Landsat 8-9 L1 和 Sentinel-2 L1C 大气层顶部反射率。该数据自2013年4月起在全球范围内提供,分辨率为30米,重访时间为2-3天。 它被广泛用于监测植被、土地利用和土地覆盖随时间的变化。
geometrysentinelvar可视化数据 有些同学发现在2022年的1C级影像中获取NDVI时,DN值基本上大了1000左右。原因是在 2022-01-25 之后,PROCESSING_BASELINE ‘04.00’ 或更高版本的 Sentinel-2 场景的 DN(值)范围移动了 1000。HARMONIZED 集合将新场景中的数据移动到与旧场景中相同的范围内。由此GEE中Sentinel-2 1C级...
The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, ...
Satellite imagery from the Landsat 8 and Sentinel-2 satellites, aligned to a common grid and processed to compatible color spaces. The Harmonized Landsat Sentinel-2 (HLS) product includes data from the Landsat-8 and Sentinel-2 satellites, aligned to a common tiling system at 30m resolution, fro...
This study's objective was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India, using multitemporal visible and near﹊nfrared (VNIR) bands from the SentinelMultiSpectral Instruments (MSIs). Conversion to cropland, coupled with secondary ...
Inspired by the recent advances in deep learning, this study developed an extended super-resolution convolutional neural network (ESRCNN) to a data fusion framework, specifically for blending Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) data. Results demonstrated...