Abstract
Cropping intensity is a key indicator for evaluating grain production and intensive use of cropland. Timely and accurately monitoring of cropping intensity is of great significance for ensuring national food security and improving the level of national land management. In this study, we used all Sentinel-2 images on the Google Earth Engine cloud platform, and constructed an improved peak point detection method to extract the cropping intensity of a heterogeneous planting area combined with crop phenology. The crop growth cycle profiles were extracted from the multi-temporal normalized difference vegetation index (NDVI) and land surface water index (LSWI) datasets. Results show that by 2020, the area of single cropping, double cropping, and triple cropping in the Henan Province are 52,236.9 km2, 74,334.1 km2, and 1927.1 km2, respectively; the corresponding producer accuracies are 86.12%, 93.72%, and 91.41%, respectively; the corresponding user accuracies are 88.99%, 92.29%, and 71.26%, respectively. The overall accuracy is 90.95%, and the Kappa coefficient is 0.81. Using the sown area in the statistical yearbook data of cities in the Henan Province to verify the extraction results of this paper, the R2 is 0.9717, and the root mean square error is 1715.9 km2. This study shows that using all the Sentinel-2 data, the phenology algorithm, and cloud computing technology has great potential in producing a high spatio-temporal resolution dataset for crop remote sensing monitoring and agricultural policymaking in complex planting areas.
Highlights
The “17 Sustainable Development Goals” (SDGs) issued by the United Nations in 2015 clearly took eliminating hunger and achieving food security as one of its goals and tasks
Considering the limitations of using remote sensing to identify cropping intensity in complex planting areas, we developed a phenological-based algorithm on the Google
The planting systems in the Henan Province are dominated by single cropping (52,236.9 km2 ) and double cropping (74,334.1 km2 ), and the distribution of triple cropping is very small (1927.1 km2 )
Summary
The “17 Sustainable Development Goals” (SDGs) issued by the United Nations in 2015 clearly took eliminating hunger and achieving food security as one of its goals and tasks. Under the background of the decline of cropland area in the Peoples’ Republic of China (PRC) [5,6] and the “ceiling effect” of grain yields [7], increasing the degree of cropland intensification has become a key way to increase food production and ensure food security [8]. Sentinel-2A/B data have high temporal resolution (5-d to 10-d), and high spatial resolution (10-m to 60-m), which provides more detailed phenological information for monitoring cropping intensity [26]. This provides the possibility to describe many small-scale farmland areas accurately [27]. It is necessary to evaluate the potential of Sentinel-2A/B data in cropping intensity monitoring and provide new ideas for the fine mapping of cropping intensity in complex planting areas
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