Hyperspectral Remote Sensing of Agriculture
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TLDR
In this article, the spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes have been discussed to reduce data dimension and minimize the information redundancy.Abstract:
Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This article describes spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes. Different methods have been discussed to reduce data dimension and minimize the information redundancy. Potential applications of hyperspectral remote sensing in agriculture, i.e. spectral discrimination of crops and their genotypes, quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling, assessing abiotic and biotic stresses as developed by different researchers in India and abroad are described.read more
Citations
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Journal ArticleDOI
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
TL;DR: This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology.
Journal ArticleDOI
Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery
TL;DR: In this article, a regression model was established for chlorophyll and nitrogen aimed indices with their corresponding crop growth variables by exploiting the ground-based measurements, regression models were established for CHs and nitrogen with their respective crop growth variable.
Journal ArticleDOI
Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges
Tobias Hank,Katja Berger,Heike Bach,Jan G. P. W. Clevers,Anatoly A. Gitelson,Pablo J. Zarco-Tejada,Wolfram Mauser +6 more
TL;DR: This paper summarizes where hyperspectral data provide additional value and information in an agricultural context and highlights the contribution of Hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectrals missions.
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An Overview of AVIRIS-NG Airborne Hyperspectral Science Campaign Over India
Bimal K. Bhattacharya,Robert O. Green,Sadasiva Rao,M.K. Saxena,Shweta Sharma,K. Ajay Kumar,P. Srinivasulu,S. K. Sharma,Dolly Wattal Dhar,S. K. Bandyopadhyay,Shantanu Bhatwadekar,Raj Kumar +11 more
TL;DR: Bimal K. Bhattacharya*, Robert O. Green, Sadasiva Rao, M. Saxena, Shweta Sharma, K. Ajay Kumar, P. Srinivasulu, Shashikant Sharma, D. Dhar, S. Bandyopadhyay, Shantanu Bhatwadekar and Raj Kumar Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India Jet Propulsion Laboratory, California Institute of Technology, CA 91109, USA National Remote Sensing Centre, India Earth Observation Science Directorate,
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Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing
Gopal Krishna,Gopal Krishna,Rabi Narayan Sahoo,Prafull Singh,Vaishangi Bajpai,Himesh Patra,Sudhir Kumar,Raju Dandapani,V. K. Gupta,C. Viswanathan,Tauqueer Ahmad,Prachi Mishra Sahoo +11 more
TL;DR: In this paper, the optimal wavebands were identified through spectral indices, multivariate techniques and neural network technique, and prediction models were developed for predicting water deficit stress levels in rice genotypes.
References
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TL;DR: The normalized difference water index (NDWI) as discussed by the authors was proposed for remote sensing of vegetation liquid water from space, which is defined as (ϱ(0.86 μm) − ϱ(1.24 μm)) where ϱ represents the radiance in reflectance units.
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