Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

Jiali Shang,David Kroetsch,Elizabeth Pattey,Jinfei Wang,Jiangui Liu,Taifeng Dong,Nicholas Lantz,Ting Zhao,Xiaoyuan Geng,Budong Qian,Ted Huffman

doi:10.1117/1.jrs.8.085196

Abstract

This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R 2 )o f 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.085196)

Highlights

High-spatial resolution optical remote sensing observations can provide crop information at a spatial scale suitable for field to subfield level studies
Samples of normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and optimized soil adjusted vegetation index (OSAVI) [Figs. 3(a), 3(b), and 3(e)] were distributed more parallel along the 1:1 line than the other indices, with a negative intercept showing an overestimate of the three indices by operational land imager (OLI) data
The results from this study showed that, following proper radiometric calibration and atmospheric correction, vegetation indices derived from the data acquired by the two sensors were in very good agreement

Summary

Introduction

High-spatial resolution optical remote sensing observations can provide crop information at a spatial scale suitable for field to subfield level studies. The capability for simultaneous acquisition over a large area allows for capturing spatial variability due to underlying soil properties and management practices. It can greatly alleviate the workload for conducting crop surveys or field measurements. Multiple optical remote sensing products over a growing season have been used for crop biomass and yield estimation with a radiation use efficiency model (RUE)[1] and have proven to be useful in reducing the uncertainty of several input descriptors of crop models using the data assimilation approach.[2,3] Unlike the moderate-resolution satellite sensors such as the MODIS and AVHRR, the relatively longer revisiting cycle of a high-resolution satellite sensor is largely affected by cloud contamination and leads to missed acquisitions during part of the key growth stages. For continuous monitoring of crop seasonal development trends, it is advantageous to be able to use data available from different sensors to shorten the revisit cycle

Methods

Results

Conclusion