Super-resolution enhancement of Sentinel-2 image for retrieving LAI and chlorophyll content of summer corn

Mingzheng Zhang,Jianxi Huang,Jiayu Wu,Chan Yao,Wei Su,Dehai Zhu,Wei Wang,Yuting Fu,Jing-Hao Xue

doi:10.1016/j.eja.2019.125938

Abstract

Sentinel-2 satellite is a new generation of multi-spectral remote sensing technique with high spatial, temporal and spectral resolution. Especially, Sentinel-2 incorporates three red-edge bands with central wavelength at 705, 740 and 783 nm, which are very sensitive to vegetation changing, heath and variations. Unfortunately, their spatial resolution is only 20 m, which is lower comparably. This spatial resolution brings difficulties for mining the potential of Sentinel-2 image in vegetation monitoring. Therefore, we focus on enhancing the spatial resolution of Sentinel-2 red edge band images to 10m using the SupReME algorithm. Furthermore, the summer corn canopy leaf area index (LAI), leaves chlorophyll content (LCC) and canopy chlorophyll content (CCC) were retrieved by the linear and physical models for the corn growth monitoring purpose. The results showed that the spatial resolution of Sentinel-2 images had been enhanced to 10m from original 20m, and the estimation accuracy (EA) was over 97% for pixels planted by summer corn. Moreover, the accuracy of summer corn canopy LAI, LCC and CCC was improved respectively using enhanced Sentinel-2 images by SupReME method. During these three parameters retrieval, the red-edge bands or SWIR bands were introduced into optimal cost function and vegetation index which the accuracy of these models was high. The SupReME algorithm provides a valuable way for Sentinel-2 images enhancement, which is of great potential to mining Sentinel-2 images and multitude its application.

Highlights

Accurate estimation of vegetation biophysical variables with high spatial and temporal resolution plays an important role in global climate change monitoring, comprehensive monitoring of land use/cover change, estimating the total amount of ecological resources etc.(Guan et al, 2016, 2017; Houborg et al, 2007; Boegh et al, 2002; Huang et al, 2015)
The results showed that the accuracy of leaf area index (LAI), canopy chlorophyll content (CCC) and leaves chlorophyll content (LCC) retrieving using enhanced image were higher than using original image (Fig. 7 and Fig. 9), especially for multi-band combination vegetation indices including at least one red-edge band with an original spatial resolution of 20m
We found that the retrieval accuracy of CCC, LAI and LCC using whether vegetation indexes or PROSAIL model were all in line of CCC > LAI > LCC

Summary

Introduction

Accurate estimation of vegetation biophysical variables with high spatial and temporal resolution plays an important role in global climate change monitoring, comprehensive monitoring of land use/cover change, estimating the total amount of ecological resources etc.(Guan et al, 2016, 2017; Houborg et al, 2007; Boegh et al, 2002; Huang et al, 2015). Sentinel-2 is a new generation of multi-spectral imagery with 13 spectral bands, including three red-edge bands which are sensitive to the chlorophyll content of vegetation (Sibanda et al, 2015; Atzberger et al, 2012; Pu et al, 2003). It is an ideal data source for vegetation growth monitoring and is used for terrestrial observations of global high resolution and high revisiting capabilities, biophysical change mapping, monitoring of coastal and inland waters, and risk and disaster mapping to support the continuity of SPOT-5 and Landsat satellite data (Wang et al, 2017; Clevers et al, 2013). The Sentinel-2 image is in the same way, with 20m spatial resolution within three red edge bands and not balance to the 10m spatial resolution of visible bands and Near Infrared (NIR)

Methods

Results

Discussion

Conclusion