# A satellite-derived dataset on vegetation phenology across Central Asia from 2001 to 2023

## TL;DR

This paper introduces a satellite-based dataset tracking vegetation growth patterns in Central Asia from 2001 to 2023, aiming to improve understanding of dryland ecosystem changes.

## Contribution

A novel satellite-derived dataset for Central Asian vegetation phenology using MODIS data and advanced smoothing techniques.

## Key findings

- The dataset includes seven LSP metrics categorized into timing, NIRv values, and vegetation growth state.
- The stationary wavelet transform with Biorthogonal 1.1 wavelet improved smoothing of NIRv time series.
- The dataset supports analysis of dryland ecosystem responses to climate and human activities.

## Abstract

Satellite-observed land surface phenology (LSP) data have helped us better understand terrestrial ecosystem dynamics at large scales. However, uncertainties remain in comprehending LSP variations in Central Asian drylands. In this article, an LSP dataset covering Central Asia (45–100°E, 33–57°N) is introduced. This LSP dataset was produced based on Moderate Resolution Imaging Spectroradiometer (MODIS) 0.05-degree daily reflectance and land cover data. The phenological dynamics of drylands were tracked using the seasonal profiles of near-infrared reflectance of vegetation (NIRv). NIRv time series processing involved the following steps: identifying low-quality observations, smoothing the NIRv time series, and retrieving LSP metrics. In the smoothing step, a median filter was first applied to reduce spikes, after which the stationary wavelet transform (SWT) was used to smooth the NIRv time series. The SWT was performed using the Biorthogonal 1.1 wavelet at a decomposition level of 5. Seven LSP metrics were provided in this dataset, and they were categorized into the following three groups: (1) timing of key phenological events, (2) NIRv values essential for the detection of the phenological events throughout the growing season, and (3) NIRv value linked to vegetation growth state during the growing season. This LSP dataset is useful for investigating dryland ecosystem dynamics in response to climate variations and human activities across Central Asia.