Monitoring the dynamics of phenological development of winter wheat using orthogonalization of multispectral satellite data

Daniela Avetisyan; Roumen Nedkov

doi:10.1117/12.2573274

20 September 2020 Monitoring the dynamics of phenological development of winter wheat using orthogonalization of multispectral satellite data

Daniela Avetisyan, Roumen Nedkov

Author Affiliations +

Proceedings Volume 11528, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII; 115280Y (2020) https://doi.org/10.1117/12.2573274
Event: SPIE Remote Sensing, 2020, Online Only

Conference Poster

Abstract

This article presents a novel methodology for monitoring and assessment of dynamics of phenological development of crop vegetation, based on orthogonal transformation of multispectral satellite images from Sentinel-2. In this work two indices using the Greenness and Wetness components of the Tasselled Cap Transformation (TCT) model are applied. The essence of the TCT model is the unitary matrix for orthogonal transformation, which is fixed for each individual sensor. The presented methodology implements a model for automated processing of Sentinel-2 images, aimed at obtaining Greenness and Wetness Tasselled Cap indices. The applied indices are defined as Normalized Differential Greenness Index (NDGI) and Normalized Differential Wetness Index (NDWNI). These indices are strongly sensitive to small changes and reflect quantitatively the vegetation dynamics in defined temporal periods. NDGI and NDWNI are especially suitable for determination of timing of different growth stages and for monitoring of the health status of the studied crops during these stages. The manifestation of the indices and their dynamics is closely related to variations of chlorophyll and moisture content, which are observed during the transition from one stage to another.

Citation Download Citation

Daniela Avetisyan and Roumen Nedkov "Monitoring the dynamics of phenological development of winter wheat using orthogonalization of multispectral satellite data", Proc. SPIE 11528, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII, 115280Y (20 September 2020); https://doi.org/10.1117/12.2573274

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available