2022
DOI: 10.3390/rs14040934
|View full text |Cite
|
Sign up to set email alerts
|

Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data

Abstract: Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A g… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
7
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 19 publications
(7 citation statements)
references
References 58 publications
0
7
0
Order By: Relevance
“…In comparison to other non-linear prediction algorithms, such as SVM or kernel ridge regression, GP offer the possibility to automatically tune their hyper-parameters θ. GP also provide the variance of the point-wise estimation. These properties made GP for regression widely used by the remote sensing community in the last decade [61]- [64].…”
Section: A Univariate Gaussian Processesmentioning
confidence: 99%
“…In comparison to other non-linear prediction algorithms, such as SVM or kernel ridge regression, GP offer the possibility to automatically tune their hyper-parameters θ. GP also provide the variance of the point-wise estimation. These properties made GP for regression widely used by the remote sensing community in the last decade [61]- [64].…”
Section: A Univariate Gaussian Processesmentioning
confidence: 99%
“…Furthermore, the result showed that the GPR model has a lower prediction accuracy with soil and UAV imagery derived from data fusion compared with the UAV vegetation indices scenario. In contrast, previous research showed that hyperspectral UAV and soil data fusion improve GPR modelling precision while providing more accurate results with vegetation indices for estimating wheat above-ground biomass [42,115]. The improved performance of GPR can be linked to its use of kernel functions when dealing with input [46,47,82].…”
Section: Discussionmentioning
confidence: 94%
“…The covariance between any two points in the input space determines the similarity between those points and is used to make predictions about the output variable. The hyper parameters of the Gaussian process, such as the length scale and amplitude, control the smoothness and variability of the functions in the prior distribution [38]. The posterior distribution over functions is obtained by conditioning on the observed data and is also a Gaussian process with updated mean and covariance functions.…”
Section: Gaussian Process Regression (Gpr) Modelmentioning
confidence: 99%