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
Remote sensing of vegetation water content from equivalent water thickness using satellite imagery
TL;DR: In this article, the authors used a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery to estimate EWT and VWC.
241
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat
TL;DR: In this paper, the authors evaluated three different chemometric techniques specifically designed to deal with redundant (and small) data sets, including principal component regression (PCR), partial least square regression (PLSR), and a widely used 2-band vegetation index (NDVI) as a baseline approach.
241
Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy
TL;DR: In this article, the contents of thermolabile organic carbon (C 375°C ), the inert organic C fraction (C inert ) and the sum of both (total soil organic carbon, OC tot ) were estimated with three different methods: partial least squares regression (PLSR), a combination of PLSR with a genetic algorithm (GA-PLSR) for spectral feature selection, and support vector machine regression (SVMR) with nonlinear fitting capacities.
221
Estimating canopy water content using hyperspectral remote sensing data
TL;DR: In this paper, the spectral information provided by the canopy water absorption feature at 970nm for estimating and predicting canopy water content was studied using a modelling approach and in situ spectroradiometric measurements.
205
A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆
TL;DR: In this article, the spectral vegetation index (SVI) was used to estimate the leaf area index (LAI) of corn and soybeans in the Walnut Creek watershed south of Ames, IA.
191