DUSTFALL EFFECT ON HYPERSPECTRAL INVERSION OF CHLOROPHYLL CONTENT – A LABORATORY EXPERIMENT

Yuteng Chen,Baodong Ma,Lixin Wu,Xuexin Li,Song Zhang

doi:10.5194/isprs-annals-iv-3-65-2018

Abstract

Abstract. Dust pollution is serious in many areas of China. It is of great significance to estimate chlorophyll content of vegetation accurately by hyperspectral remote sensing for assessing the vegetation growth status and monitoring the ecological environment in dusty areas. By using selected vegetation indices including Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI) ，Double Difference Index (DD) and Red Edge Position Index (REP), chlorophyll inversion models were built to study the accuracy of hyperspectral inversion of chlorophyll content based on a laboratory experiment. The results show that: (1) REP exponential model has the most stable accuracy for inversion of chlorophyll content in dusty environment. When dustfall amount is less than 80 g/m2, the inversion accuracy based on REP is stable with the variation of dustfall amount. When dustfall amount is greater than 80 g/m2, the inversion accuracy is slightly fluctuation. (2) Inversion accuracy of DD is worst among three models. (3) MTCI logarithm model has high inversion accuracy when dustfall amount is less than 80 g/m2; When dustfall amount is greater than 80 g/m2, inversion accuracy decreases regularly and inversion accuracy of modified MTCI (mMTCI) increases significantly. The results provide experimental basis and theoretical reference for hyperspectral remote sensing inversion of chlorophyll content.

Highlights

Due to environmental disruption, dust has become one of the main pollutants that affect the quality of air in China (Qiao et al, 2011)
3 Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI) logarithm model has high inversion accuracy when dustfall amount is less than 80 g/m2; When dustfall amount is greater than 80 g/m2, inversion accuracy decreases regularly and inversion accuracy of modified MTCI increases significantly
Chlorophyll is an important index to evaluate the growth of vegetation, and monitoring of chlorophyll content is of great significance to the construction and protection of ecological environment

Summary

INTRODUCTION

Dust has become one of the main pollutants that affect the quality of air in China (Qiao et al, 2011). Monitoring vegetation and its related biological systems can provide decision-making basis for protecting the ecological environment (Koppnen et al, 2002).The change of chlorophyll content will affect the spectral curve of vegetation, which make it possible to use remote sensing data to estimate chlorophyll content. Variation of spectral characteristics of plant leaves in dusty environment was analyzed and an inversion model for the dustfall of iron tail mineral powder was established by Xu et al (Xu et al.,2017). The effect of dustfall on remote sensing inversion accuracy of vegetation chlorophyll content has been not studied. Dustfall coverage leads to changes in spectral characteristics of leaves, which would reduce hyperspectral inversion accuracy of chlorophyll content. The final aim is to improve the inversion accuracy of chlorophyll content by hyperspectral remote sensing in dusty environment

Sample collection

Measurement of chlorophyll content and spectra

Dustfall effect on inversion accuracy of chlorophyll content

Effect of dustfall on REP

The correction of the MTCI index

CONCLUSION