Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-11-01/2022-12-31): Spectral indices

Data information

This dataset includes widely used indices for 2022-11-01/2022-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).

Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).

Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).

Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).

Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).

Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).

As a part of a Data Cube

This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.

To cite this dataset, refer to the DOI available on the landing page.

To access other data layers in the data cube, use the navigation catalog on the landing page as well.

Support

If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!

数据集信息
本数据集涵盖2022年11月1日至2022年12月31日期间的多款常用遥感光谱指数，覆盖植被、作物、土壤与水体等关键研究方向，数据以双月为更新周期（即每两个月提供一组数值）。

归一化差分植被指数（Normalized Difference Vegetation Index, NDVI）用于评估植被健康状态与生物量，计算公式为(nir - red) / (nir + red)（Tucker, 1979）。
归一化差分耕作指数（Normalized Difference Tillage Index, NDTI），又称归一化燃烧比2（Normalized Burn Ratio 2, NBR2），可应用于耕作检测、火灾后恢复研究以及土壤封闭性识别，计算公式为(swir1 - swir2) / (swir1 + swir2)（Van Deventer et al., 1997）。
土壤调节植被指数（Soil Adjusted Vegetation Index, SAVI）通过最小化土壤亮度对植被遥感探测的干扰，在植被稀疏区域的应用效果更优，计算公式为((nir - red) / (nir + red + 0.5)) * 1.5（Huete, 1988）。
吸收光合有效辐射比例（Fraction of Absorbed Photosynthetically Active Radiation, FAPAR）可直接表征植物生产力，计算公式为((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001（Robinson et al., 2018）。
归一化差分水体指数（Normalized Difference Water Index, NDWI）可用于解析水体动态变化与气候特征，计算公式为(nir - swir1) / (nir + swir1)（Gao, 1996）。
归一化差分积雪指数（Normalized Difference Snow Index, NDSI）可辅助识别积雪覆盖区域，计算公式为(green - swir1) / (green + swir1)（Salomonson and Appel, 2006）。

作为数据立方体的子集
本数据属于基于陆地卫星的光谱指数时间序列数据集（欧盟，30米分辨率）数据立方体的一个子集。如需获取完整数据集的概览与详细信息，请通过指定链接访问该数据立方体的登陆页面。

如需引用本数据集，请查阅登陆页面上提供的DOI编号。
亦可通过登陆页面的导航目录，访问数据立方体中的其他数据图层。

问题反馈
若您发现数据存在错误、异常或不一致之处，或有相关疑问，请提交GitHub Issue！