Journal Article10.1016/J.JAG.2013.04.004
Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements
M. Mirzaie,Roshanak Darvishzadeh,Alireza Shakiba,A.A. Matkan,Clement Atzberger,Andrew K. Skidmore +5 more
119
TL;DR: The cross-validated results identified PLSR as the regression model providing the most accurate estimates of VWC among the various methods, and revealed that this model is highly recommended for use with multi-collinear datasets.
read more
About: This article is published in International Journal of Applied Earth Observation and Geoinformation. The article was published on 01 Feb 2014. The article focuses on the topics: Enhanced vegetation index & Partial least squares regression.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models
TL;DR: The results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications.
319
An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement
TL;DR: The results represent that the machine learning techniques in contrast to SVIs are not sensitive to different disease symptoms and their results are reliable.
182
A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy
TL;DR: The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples, and RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data.
172
Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy
Clement Atzberger,Roshanak Darvishzadeh,Markus Immitzer,Martin Schlerf,Andrew K. Skidmore,Guerric Le Maire +5 more
TL;DR: Four different retrieval methods for estimating leaf area index (LAI) in grassland are assessed in a comparative way using a jackknife approach, demonstrating that the LUT-based RTM inversion yields higher accuracies for LAI estimation and the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples.
165
Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize.
TL;DR: It is demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques and obtained the best predictive power of crop water status using VIs.
References
Estimating the foliar biochemical concentration of leaves with reflectance spectrometry
TL;DR: In this paper, the authors used stepwise regression and either of the following: (i) standard first derivative reflectance spectra (FDS), (ii) absorption band depths, following continuum removal and normalisation against band depth at the centre of the absorption feature (BNC) or (iii) absorption bands depths, followed continuum removal, normalisation, and normalization against the area of the BNA.
493
Estimating soil moisture using remote sensing data: A machine learning approach
TL;DR: In this paper, a Support Vector Machine (SVM) is applied to soil moisture estimation using remote sensing data, which is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach.
490
An improved strategy for regression of biophysical variables and Landsat ETM+ data.
TL;DR: In this article, the authors compared three different regression models to predict the leaf area index (LAI) for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest.
487
Hyperspectral Imaging for Food Quality Analysis and Control
01 Jan 2010
TL;DR: Hyperspectral imaging is a novel technology for food quality analysis and control, offering non-destructive assessment and control of food products.
474
Global land cover classification by remote sensing: present capabilities and future possibilities
TL;DR: In this article, the authors examined the adequacy of traditional approaches to the derivation of global information on land cover, and the contribution of coarse resolution satellite data from the NOAA series of satellites is discussed.
468