From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

https://doi.org/10.1016/j.jag.2022.103006Get rights and content
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Highlights

  • Automatic method developed for mapping actively cropped fields without regional adjustments.

  • Combined use of Sentinel-1 and Sentinel-2 increases mapping accuracy.

  • High performances in correctly labelling sparse crops and separating grass from cropped fields.

  • The first 20-m national-scale map of cropped fields and abandoned cropland generated in China.

  • Around 77% cropland are cropped with field crops in China at national scale.

Abstract

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale.

Keywords

Cropped field
Smallholder agriculture
Sentinel-1
Sentinel-2
Cropland abandonment
Comparative temporal variation

Data availability

Data will be made available on request.

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