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Mapping abandoned cropland using Within-Year Sentinel-2 time series.

Bo Liu^1,2, Wei Song^1,3

¹Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China.

Catena

|January 16, 2023

Summary

Mapping abandoned cropland using Sentinel-2 data can boost food security. This new method accurately identifies abandoned farmland, even in challenging terrains, aiding reclamation efforts.

Keywords:

Change detection within-year Food security GEOBIA Land use mapping Linxia County Time series

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Area of Science:

Environmental Science
Remote Sensing
Agricultural Science

Background:

Global food crisis exacerbated by pandemics and conflicts necessitates increased food production.
Reclaiming abandoned cropland offers a rapid solution for enhancing global food supply and security.
Existing remote sensing methods struggle with fragmented, mountainous terrains and distinguishing abandoned from fallow land.

Purpose of the Study:

To develop and validate a novel change-detection method for extracting abandoned cropland using Sentinel-2 time-series data.
To differentiate between spontaneously abandoned, induced abandoned, fallow, and lost cropland.
To assess the potential for grain production increase through reclamation in Linxia County, China.

Main Methods:

Utilized within-year Sentinel-2 time-series data for change detection from 2018-2021.
Employed multi-resolution segmentation and random forest classification for object-based land use mapping.
Analyzed annual land use trajectories and within-year Normalized Difference Vegetation Index (NDVI) time series for cropland classification and change extraction.

Main Results:

Achieved high overall mapping accuracy (93% ± 5%) and abandoned cropland extraction accuracy (81% ± 2%) at the object level.
Identified abandoned cropland primarily in medium-quality areas, moderately distant from rural settlements.
Quantified a potential grain production increase of at least 3.6% in Linxia County through reclamation.

Conclusions:

The developed Sentinel-2 time-series method robustly extracts abandoned cropland, including fragmented areas in mountainous regions.
The findings highlight the potential of abandoned cropland reclamation for food security, emphasizing the need for localized strategies.
This methodology is transferable for land use change research and monitoring globally.