Search

Double cropping is an important cropping system in China, with more than half of China's cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]