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Reseach Article

Hyperspectal Remote Sensing for Agriculture: A Review

by Pooja Vinod Janse, Ratnadeep R. Deshmukh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 7
Year of Publication: 2017
Authors: Pooja Vinod Janse, Ratnadeep R. Deshmukh

Pooja Vinod Janse, Ratnadeep R. Deshmukh . Hyperspectal Remote Sensing for Agriculture: A Review. International Journal of Computer Applications. 172, 7 ( Aug 2017), 30-34. DOI=10.5120/ijca2017915185

@article{ 10.5120/ijca2017915185,
author = { Pooja Vinod Janse, Ratnadeep R. Deshmukh },
title = { Hyperspectal Remote Sensing for Agriculture: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 7 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017915185 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:19:44.344216+05:30
%A Pooja Vinod Janse
%A Ratnadeep R. Deshmukh
%T Hyperspectal Remote Sensing for Agriculture: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 7
%P 30-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Hyperspectral remote sensing is used for wide range of application. Hyperspectral data provides more than 200 narrow wavelength bands which provide significant information about all biological and chemical properties of material. Hyperspectral remote sensing is widely used in various applications like agricultural and soil research, mining application, crop management application, drought condition assessment, plant species classification, water body analysis, mineral analysis etc. This paper mainly reviews the concept of hyperspectral remote sensing; processing of hyperspectral data; different vegetation indices defined by researcher; the applications of hyperspectral data for agricultural.

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Index Terms

Computer Science
Information Sciences


Hyperspectral data Remote sensing Spectral reflectance Agriculture Crop classification Vegetation Index.