Abstract
Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.
Highlights
Global population growth leads to increasing demand for agricultural crops, which is expected to continue in the coming decades [1]
To characterize the spatio-temporal patterns of winter wheat planting areas in the North China Plain (NCP), we examined their dynamics for 1999–2019 using a linear trend analysis for the entire area and at the province level (Beijing and Tianjin are excluded from this analysis as they occupy a small proportion of the winter wheat planted area across the NCP (~0.62%)
Our study demonstrated the possibility of mapping winter wheat planting areas and tracking its dynamics using Landsat data in China, showing the potential to be applied at a global scale
Summary
Global population growth leads to increasing demand for agricultural crops, which is expected to continue in the coming decades [1]. Global- and regional-scale agricultural monitoring systems are needed to provide accurate information about agricultural. Climate change-induced droughts, extreme rainfall and heatwaves may force farmers to adjust crop systems in adaptation to climate change [4]. Wheat is one of the major grain crops with a projected global production of ~765 million metric tons in 2019 [5] and feeds 2.5 billion people across the globe [6]. Winter wheat planting areas are often managed by smallholder farmers and are patchy in nature, thereby hampering quantification and continuous monitoring of yield estimates needed to ensure food security [7,8]. About two-thirds of the developing world’s 3 billion rural people live in about 475 million small farm households, working on land plots smaller than 2 hectares and in China, nearly
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