Literature DB >> 24347746

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass.

Loris Vescovo1, Georg Wohlfahrt2, Manuela Balzarolo3, Sebastian Pilloni2, Matteo Sottocornola1, Mirco Rodeghiero1, Damiano Gianelle1.   

Abstract

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863-881 nm) and the H18 (745-751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.

Entities:  

Year:  2012        PMID: 24347746      PMCID: PMC3859895          DOI: 10.1080/01431161.2011.607195

Source DB:  PubMed          Journal:  Int J Remote Sens        ISSN: 0143-1161            Impact factor:   3.151


  3 in total

1.  Estimating near-infrared leaf reflectance from leaf structural characteristics.

Authors:  M R Slaton; E Raymond Hunt; W K Smith
Journal:  Am J Bot       Date:  2001-02       Impact factor: 3.844

2.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation.

Authors:  Anatoly A Gitelson
Journal:  J Plant Physiol       Date:  2004-02       Impact factor: 3.549

3.  Asymptotic nature of grass canopy spectral reflectance.

Authors:  C J Tucker
Journal:  Appl Opt       Date:  1977-05-01       Impact factor: 1.980

  3 in total
  7 in total

1.  WhiteRef: a new tower-based hyperspectral system for continuous reflectance measurements.

Authors:  Karolina Sakowska; Damiano Gianelle; Alessandro Zaldei; Alasdair MacArthur; Federico Carotenuto; Franco Miglietta; Roberto Zampedri; Mauro Cavagna; Loris Vescovo
Journal:  Sensors (Basel)       Date:  2015-01-08       Impact factor: 3.576

2.  Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging.

Authors:  Jian Wang; Chu Zhang; Ying Shi; Meijuan Long; Faisal Islam; Chong Yang; Su Yang; Yong He; Weijun Zhou
Journal:  Plant Methods       Date:  2020-03-05       Impact factor: 4.993

3.  Mapping the in situ microspatial distribution of ice algal biomass through hyperspectral imaging of sea-ice cores.

Authors:  Emiliano Cimoli; Vanessa Lucieer; Klaus M Meiners; Arjun Chennu; Katerina Castrisios; Ken G Ryan; Lars Chresten Lund-Hansen; Andrew Martin; Fraser Kennedy; Arko Lucieer
Journal:  Sci Rep       Date:  2020-12-14       Impact factor: 4.379

4.  Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation.

Authors:  Gustavo Togeiro de Alckmin; Lammert Kooistra; Richard Rawnsley; Sytze de Bruin; Arko Lucieer
Journal:  Sensors (Basel)       Date:  2020-12-15       Impact factor: 3.576

5.  Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.

Authors:  Juanjuan Zhang; Wen Zhang; Shuping Xiong; Zhaoxiang Song; Wenzhong Tian; Lei Shi; Xinming Ma
Journal:  Plant Methods       Date:  2021-03-31       Impact factor: 4.993

6.  Improving remote estimation of winter crops gross ecosystem production by inclusion of leaf area index in a spectral model.

Authors:  Radosław Juszczak; Bogna Uździcka; Marcin Stróżecki; Karolina Sakowska
Journal:  PeerJ       Date:  2018-09-21       Impact factor: 2.984

7.  Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions.

Authors:  Adel H Elmetwalli; Salah El-Hendawy; Nasser Al-Suhaibani; Majed Alotaibi; Muhammad Usman Tahir; Muhammad Mubushar; Wael M Hassan; Salah Elsayed
Journal:  Sensors (Basel)       Date:  2020-11-17       Impact factor: 3.576

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.