Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

Fuyou Tian,Jiaming Xu,Xin Zhang,Bingfang Wu,Hongwei Zeng

doi:10.3390/rs11060629

Abstract

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

Highlights

Food security a major challenge to the sustainable development of human beings [1,2], and the eradication of hunger is the second target of the United Nation (UN)’s Sustainable Development Goals (SDGs) [3]
The urban values in three VI bands and the red and SWIR2 bands were separated from other classes, while the forest values in SWIR1, SWIR2, and normalized differenced vegetation index (NDVI) show a clear distinction from the crop values
The results showed that the corn area estimated from the optical and synthetic aperture radar (SAR) images correlated well with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2

Summary

Introduction

Food security a major challenge to the sustainable development of human beings [1,2], and the eradication of hunger is the second target of the United Nation (UN)’s Sustainable Development Goals (SDGs) [3]. The use of coarse spatial resolution images as original data at scales of hundreds of meters, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR), is predominant in previous studies because of the fine temporal resolution of the Aqua and Terra satellites. These two satellites revisit the same location at least four times per day, and provide the composition products for free [24,25,26,27,28]. The revisiting period of Landsat was half a month, and few images were available during the crop-growing season [9,21]

Objectives

Results

Conclusion