Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

Abstract

Abstract Cropping intensity has undergone dramatic changes worldwide due to the effects of climate changes and human management activities. Cropping intensity is an important factor contributing to crop production and food security at local, regional and national scales, and is a critical input data variable for many global climate, land surface, and crop models. To generate annual cropping intensity maps at large scales, Moderate Resolution Imaging Spectroradiometer (MODIS) images at 500-m or 250-m spatial resolution have problems with mixed land cover types within a pixel (mixed pixel), and Landsat images at 30-m spatial resolution suffer from low temporal resolution (16-day). To overcome these limitations, we developed a straightforward and efficient pixel- and phenology-based algorithm to generate annual cropping intensity maps over large spatial domains at high spatial resolution by integrating Landsat-8 and Sentinel-2 time series image data for 2016–2018 using the Google Earth Engine (GEE) platform. In this pilot study, we report annual cropping intensity maps for 2017 at 30-m spatial resolution over seven study areas selected according to agro-climatic zones in China. Based on field-scale sample data, the annual cropping intensity maps for the study areas had overall accuracy rates of 89–99%, with Kappa coefficients of 0.76–0.91. The overall accuracy of the annual cropping intensity maps was 93%, with a Kappa coefficient of 0.84. These cropping intensity maps can also be used to enable identification of various crop types from phenological information extracted from the growth cycle of each crop. These algorithms can be readily applied to other regions in China to generate annual cropping intensity maps and quantify inter-annual cropping intensity variations at the national scale with a greatly improved accuracy.