The current study evaluates consistency among three Normalized Difference Vegetation Index (NDVI) datasets, namely GIMMS, MODIS and SPOT, to characterize alpine vegetation dynamics (greening and browning) across High Mountain Asia (HMA) in 2001–2015. The utility of these datasets is explored to evaluate the vegetation's variability at different spatial-temporal scales and, elevation, and to compare their spatial trends and distribution patterns. In addition to the Pearson correlation coefficients performed to quantitatively analyze the consistency and inconsistency of each dataset, an NDVI quality control (QC) layer and Landsat NDVI are also used to evaluate the findings. The results indicate that the GIMMS has the highest NDVI mean, while SPOT has the lowest. However, GIMMS also showed a browning trend for both Tianshan (TS) and the Qinghai Tibet Plateau (TP) at a rate of −0.3 × 10⁻³ per year, whereas MODIS and SPOT exhibit a greening trend (TS_MODIS = 0.5 × 10⁻³ per year, TS_SPOT = 0.6 × 10⁻³ per year, TP_MODIS = 0.9 × 10⁻³ per year, TP_SPOT = 1.6 × 10⁻³ per year). Furthermore, MODIS-SPOT shows the highest correlation (R_GREEN = 0.73; R_BROWN = 0.47), followed by MODIS-GIMMS, and GIMMS-SPOT. The overall, NDVI trend consistency appears to be higher in TS. Finally, the consistent greening pixels mainly distributed in central TP stretching to the northeastern part, and in western stretching to eastern TS, account for 32.14%, while 8.32% of consistent browning pixels are concentrated in southwestern TP and central TS. The inconsistent pixels account for 59.54%, with 39.21% of inconsistent greening pixels being widely distributed across HMA, and 20.58% of inconsistent browning pixels being relatively pronounced in central TS and southern TP. This study provides baseline inferences for the selection and reconstruction of data in follow-up studies on vegetation dynamics.

目前的研究评估了三个归一化差异植被指数 (NDVI) 数据集之间的一致性，即 GIMMS、MODIS 和 SPOT，以表征 2001-2015 年亚洲高山 (HMA) 的高山植被动态（绿化和褐化）。探索这些数据集的效用，以评估不同时空尺度和高程下植被的变异性，并比较它们的空间趋势和分布模式。除了使用 Pearson 相关系数对每个数据集的一致性和不一致性进行定量分析外，还使用 ​​NDVI 质量控制 (QC) 层和 Landsat NDVI 来评估结果。结果表明，GIMMS 的 NDVI 均值最高，而 SPOT 的 NDVI 均值最低。然而，⁻³年，而 MODIS 和 SPOT 呈现绿化趋势（TS _MODIS  = 0.5 × 10 ^-3年，TS _SPOT  = 0.6 × 10 ^-3年，TP _MODIS  = 0.9 × 10 ^-3年，TP _SPOT  = 1.6 × 10 ^-3每年）。此外，MODIS-SPOT 显示出最高的相关性（R _GREEN  = 0.73；R _BROWN = 0.47)，然后是 MODIS-GIMMS 和 GIMMS-SPOT。总体而言，TS 中的 NDVI 趋势一致性似乎更高。最后，一致绿化像素主要分布在青藏高原中部向东北延伸，西部向青藏高原东部延伸，占32.14%，而8.32%的一致褐化像素集中在高原西南部和青藏高原中部。不一致像素占 59.54%，其中 39.21% 不一致绿化像素广泛分布在 HMA，20.58% 不一致褐化像素在 TS 中部和 TP 南部相对明显。本研究为后续植被动态研究中数据的选择和重建提供了基线推断。