Large-Scale and High-Resolution Crop Mapping in China Using Sentinel-2 Satellite Imagery

Abstract

Large-scale, high-resolution mapping of crop patterns is useful for the assessment of food security and agricultural sustainability but is still limited. This study attempted to establish remote sensing-based crop classification models for specific cropping systems using the decision trees method and monitored the distribution of the major crop species using Sentinel-2 satellites (10 m) in 2017. The results showed that the cropping areas of maize, rice, and soybean on the Northeast China Plain were approximately 12.1, 6.2, and 7.4 million ha, respectively. The cropping areas of winter wheat and summer maize on the North China Plain were 13.4 and 16.9 million ha, respectively. The cropping areas of wheat, rice, and rape on the middle-lower Yangtze River plain were 2.2, 6.4 and 1.3 million ha, respectively. Estimated images agreed well with field survey data (average overall accuracy = 94%) and the national agricultural census data (R2 = 0.78). This indicated the applicability of the Sentinel-2 satellite data for large-scale, high-resolution crop mapping in China. We intend to update the crop mapping datasets annually and hope to guide the adjustment and optimization of the national agricultural structure.

Impacts of global warming on the cropping systems of China under technical improvements from 1961 to 2016

“…The increased cumulative temperature demand of the winter wheat growing season is one factor (Xiao et al., 2013). The national rotation and fallow policy in the North China Plain might be another factor leading to the reduction of the winter wheat planting area (Jiang et al., 2020a, 2020b). On the other hand, the northern limit of the TCS moved Northward during the study period, while the double‐cropping rice sown area was gradually replaced by single‐cropping rice.…”

Soil Organic Carbon MRV Sourcebook for Agricultural Landscapes

“…Hyperion hyperspectral imaging at 30 m resolution has been used to map crop types across different regions of the US, with accuracies of 75% to 95% in classifying crop type. 124 Similarly, high accuracy (up to 95%) in crop type classification have been demonstrated using 10 m resolution satellite imagery in north-eastern China. 125…”

Land Use/Cover Changes in Al-Jouf, KSA in Response to Water Management Strategies Using Multi-Sensor/-Temporal Data in Google Earth Engine

“…Since 2017, the European Space Agency (ESA) has provided high-resolution images (having 10, 20, and 60 m), with Sentinel-2A and 2B satellites, worldwide coverage, and revisit interval of 5-day. Such data enables to study and monitor the crop composition at no cost, especially in vegetation mapping, with the aid of enhanced definition in the red-edge spectral reflectance (Jiang et al 2020). On the other hand, the synthetic aperture radar (SAR) data has the advantage that the SAR signal interaction with the surface depends mainly on the studied object characteristics (i.e., surface roughness, shape, and dielectric properties), also on the sensor characteristics, i.e., polarization, wavelength, and incidence angle (Paluba et al 2021).…”