A long-term 250-m resolution Normalized Difference Vegetation Index (NDVI) product for 1982–2020 in Idaho

We developed a novel spatio-temporal fusion method to downscale the AVHRR NDVI products to the Moderate-resolution Imaging Spectroradiometer (MODIS) resolution. The algorithm effectively combines the high spatial variability of the MODIS NDVI data and the long-term temporal information of the AVHRR NDVI data. Finally, we successfully generated a monthly global long-term (since 1982) and high-resolution (250m) NDVI database.

Here we provide the downscaled NDVI dataset of Idaho from 1982 to 2020.

Datasets for other regions can be easily produced by the GEE platform with the code provided in the github (https://github.com/babyfoal/downsclaed_NDVI/tree/main).

The spatial distribution and temporal variation of this dataset have been both well validated by the simulated and real-data experiments.

我们研发了一种新型时空融合方法，用于将先进甚高分辨率辐射计（Advanced Very High Resolution Radiometer, AVHRR）的归一化植被指数（Normalized Difference Vegetation Index, NDVI）产品降尺度至中等分辨率成像光谱仪（Moderate-resolution Imaging Spectroradiometer, MODIS）的分辨率。该算法有效融合了MODIS NDVI数据的高空间变异性与AVHRR NDVI数据的长期时间序列信息。最终，我们成功构建了一套自1982年起的全球月度长期高分辨率（250米）NDVI数据库。

本次我们提供了1982年至2020年爱达荷州的降尺度NDVI数据集。

借助GitHub（https://github.com/babyfoal/downsclaed_NDVI/tree/main）中提供的代码，可通过Google地球引擎（Google Earth Engine, GEE）平台轻松生成其他区域的数据集。

该数据集的空间分布与时间变化特征已通过模拟实验与真实数据实验得到了充分验证。