A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data. Issue 18 (16th September 2020)

• Record Type: Journal Article
• Authors: Dong, Jie; Liu, Wei; Han, Wei; Xiang, Kunlun; Lei, Tianjie; Yuan, Wenping
• Is Part Of: International journal of remote sensing. Volume 41:Issue 18(2020)
• Page Start: 6892
• Page End: 6913
• Publication Date: 2020-09-16
• Subjects: Planting fraction -- Wheat -- Vegetation index -- Phenology -- Aerial images -- MODIS; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1755738
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.