Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data

Abstract

Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000–2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.

Forest Phenology under Differing Topographic Conditions: A Case Study of Changbai Mountain in Northeast China

“…When the latitude increased by 1 • , SOS increased at a rate of 0.29 d/a and the EOS was delayed by 0.47 d/a. Sarchil et al [15] observed the gradual change trend of vegetation phenology from low latitude to high latitude in the Middle East. For each degree increase in latitude, SOS was delayed by 4.83 d and EOS was delayed by 6.54 d. Yang et al [16] studied the radial growth characteristics of Pinus pumila (dwarf Siberian pine) along a latitude gradient of 44 • -52 • N in Northeast China and the relationship between growth and climate change.…”

Spatiotemporal Patterns of Land Surface Phenology from 2001 to 2021 in the Agricultural Pastoral Ecotone of Northern China

“…These datasets are used to analyze vegetation phenology dynamics at regional and global scales [20,21]. Landsat and Sentinel data are used for LSP research at a local scale [22]. Phenological metrics, including the start of the growing season (SOS), end of the growing season (EOS) and length of growing season (LOS), can be identified based on LSP extraction methods and various time-series remote sensing data [23].…”