China News Service, Beijing, October 27th (Reporter Sun Zifa) The Institute of Aerospace Information Innovation of the Chinese Academy of Sciences (Academy of Aerospace Sciences) announced on the 27th that the CropWatch team has released the world’s first set of 30 Data product of multiple cropping index of rice arable land.

Through verification, its overall accuracy is as high as 92.9%, not only the accuracy is better than the existing multiple index data products, but the characterization ability of the spatial details of the multiple index has also been significantly improved.

  The latest data products show that 81.6% of the world’s arable land is single-season planting, multiple cropping is mainly concentrated in East Asia, Southeast Asia, South Asia, South America and the Nile Delta, and 75% of the three-season crop planting models are distributed in tropical regions.

The average multiple cropping index in China has declined slightly by about 6% in the past 20 years. The multiple cropping index in the south has declined and the north multiple cropping index has increased overall, reflecting the positive role of agricultural policies such as farmland protection, encouragement of fallow and rotation in improving the sustainable use of farmland.

  The Institute of Space and Space of the Chinese Academy of Sciences stated that the data product fills the gap in the 30-meter resolution global multiple cropping index remote sensing data product, and is useful for assessing the potential of global cropland multiple cropping enhancement, food production increase potential, food security prediction and early warning, zero hunger and other United Nations sustainable development goals. Etc. are of great significance.

  The Global Agricultural Situation Remote Sensing Quick Report team said that the multiple cropping index of cultivated land is an indicator that describes the successive planting of several crops within a year in the same farmland. It is an important indicator to measure the degree of intensive utilization of cultivated land resources. Its accurate monitoring helps to grasp the global Food security status.

However, due to the significant differences in the degree of arable land fragmentation in different regions, the diverse utilization patterns, and the influence of clouds and rain, the high-precision global multiple cropping index extraction is facing huge challenges.

The existing multi-species index data products have low resolution and high uncertainty, making it difficult to accurately portray the true conditions of highly heterogeneous and fragmented regions.

  The research team uses Google Earth Engine as the main data processing and algorithm development platform, and uses massive multi-source remote sensing data to normalize the high-quality pixels of high-resolution multi-source optical satellite data in various years to achieve multi-source data normalization. The maximum use of effective observation data from source satellites, supplemented by microwave remote sensing data, solves the problem of missing data in cloudy rain areas.

At the same time, through the effective detection of the sowing-growth turning point and the growth-mature harvest turning point during the growth period of the crop, the adaptability and accuracy of the algorithm under different planting modes are improved.

Finally, the 5-degree grid was used as the data processing unit to extract the multiple cropping index grid by grid, and the world's first 30-meter resolution cropland multiple cropping index data product was developed.

  It is understood that the related research paper "Global 30-meter Multiple Cropping Index Data Based on Multi-source Remote Sensing Data" has been published in the top academic journal "Earth System Science Data".

(over)

Keywords: