Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Changchun Li,Yacong Li,Jingbo Li,Chunyan Ma,Yu Wang,Weiguang Zhai,Weinan Chen,Yilin Wang

doi:10.3390/rs14020284

Abstract

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

Highlights

Published: 8 January 2022Agricultural production is the basis of a country’s socio-economic development and is the key to land resource management and food security [1]
The results show that: (1) When only Sentinel-1A images were used, the extraction accuracy was improved with the aggregation of the images in the growth period
This is consistent with the result that the winter wheat acreage was extracted by integrating the SAR images of multiple growth periods

Summary

Introduction

Agricultural production is the basis of a country’s socio-economic development and is the key to land resource management and food security [1]. With 30 m resolution produced by the National Geomatics Center of China (NGCC). These remote sensing products provide strong data support for studying the spatial distribution of farmland, but few can provide information of specific crops. The traditional acquisition and updating of crop acreage and distribution information generally require managers to conduct field visits or consult local agricultural statistical reports [6]. This process is tedious and consumes a lot of human and material

Objectives

Results

Conclusion