Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index

Abstract

Maize agriculture is experiencing substantial changes in the spatiotemporal pattern of planting areas in the most populous country-China. However, there is no spatially explicit and continuous information at national scale. Mapping maize at national scale is challenging due to intra-class variability of Vegetation Indices (VIs) temporal profile. This study coped with this challenge through combined utilizations of the EVI with two bands (EVI2) and Normalized Multi-band Drought Index (NMDI) time series datasets. A novel Maize mapping algorithm was proposed through Exploring Leaf moisture variation during flowering Stage (MELS). An indicator, the Ratio of Cumulative Positive slope to Negative slope (RCPN) during flowering stage, was developed based on NMDI and utilized as the unique metric for maize mapping. The capability of the MELS method was verified using the 8-day composite MODerate resolution Imaging Spectroradiometer (MODIS) datasets in China from 2005 to 2017. The derived maize map was consistent with the agricultural census data (r2 = 0.8875 in 2015) and 2020 ground truth observations (overall accuracy = 91.49%). Validation with Landsat-interpreted images in the test regions further confirmed its fairly good accuracy, with overall accuracy of 87.91% and kappa coefficient of 0.8577. We first generated annual maize maps from 2005 to 2017 in China. Maize planting areas increased continuously 100,130 km2 (by 33.20%) during the period 2005–2015 and decreased 10,424 km2 (by 2.60%) from 2015 to 2017. The increase of cropping intensity, replacement of paddy rice and other non-maize dryland crops areas accounted for 36.48%, 34.23% and 29.29% of the dramatic increased maize areas from 2005 to 2015, respectively.