Assessing Rice Chlorophyll Content with Vegetation Indices from Hyperspectral Data - Computer and Computing Technologies in Agriculture IV - Part I
Conference Papers Year : 2011

Assessing Rice Chlorophyll Content with Vegetation Indices from Hyperspectral Data

Abstract

Leaf chlorophyll content is not only an important biochemical parameter for determinating the capacity of rice photosynthesis, but also a good indicator of crop stress, nutritional state. Due to the reliable, operational and non-destructive advantages, hyperspectral remote sensing plays a significant role for assessing and monitoring chlorophyll content. In the study, a few of typical vegetation indices (VI) with the combination of 670nm and 800nm band reflectance, Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio index (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) are modified by using 705nm and 750nm band reflectance so as to reduce the effect of spectral saturation in 660-680nm absorptive band region, and then used to assess the rice chlorophyll content. The result shows that the five mentioned VIs have better correlation with rice chlorophyll content while using 705nm and 750nm. In addition, in the study the Weight optimization combination (WOC) principle is utilized to further assess the capacity of the five modified VIs for estimating rice chlorophyll content, it is proved that OSAVI and MSR display the better performance.
Fichier principal
Vignette du fichier
978-3-642-18333-1_35_Chapter.pdf (129.18 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01559620 , version 1 (10-07-2017)

Licence

Identifiers

Cite

Xingang Xu, Xiaohe Gu, Xiaoyu Song, Cunjun Li, Wenjiang Huang. Assessing Rice Chlorophyll Content with Vegetation Indices from Hyperspectral Data. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.296-303, ⟨10.1007/978-3-642-18333-1_35⟩. ⟨hal-01559620⟩
170 View
254 Download

Altmetric

Share

More