scholarly journals Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

2019 ◽  
Vol 11 (8) ◽  
pp. 962 ◽  
Author(s):  
Cendrero-Mateo ◽  
Wieneke ◽  
Damm ◽  
Alonso ◽  
Pinto ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Training Data ◽  
Proximal Sensing ◽  
Spectral Window ◽  
Spectral Fitting ◽  
Fitting Method ◽  
Remote Observation ◽  
Interpolation Strategy ◽  
Definition Of ◽  
Simulated Training

The interest of the scientific community on the remote observation of sun‐induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F687 and F760, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15%–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation.

scholarly journals Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4886
Author(s):  
Shilei Li ◽  
Maofang Gao ◽  
Zhao-Liang Li
Keyword(s):  
Chlorophyll Fluorescence ◽  
Fluorescence Spectrum ◽  
Singular Vector ◽  
Low Frequency ◽  
Information Criterion ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Remotely Sensed Data ◽  
Spectral Window ◽  
Vector Decomposition

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

An Effective Solution to Regression Problem by RBF Neuron Network

2015 ◽  
Vol 6 (4) ◽  
pp. 57-74 ◽  
Author(s):  
Dang Thi Thu Hien ◽  
Hoang Xuan Huan ◽  
Le Xuan Minh Hoang
Keyword(s):  
Regression Function ◽  
Training Data ◽  
Neuron Network ◽  
Open Problems ◽  
Regression Problem ◽  
Rbf Network ◽  
Network Training ◽  
Hidden Layer ◽  
Definition Of ◽  
Selection Of

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, the authors use k-nearest neighbour method to define the value of regression function at the center and an interpolation RBF network training algorithm with equally spaced nodes to train the network. The experiments show the outstanding efficiency of regression function when the training data has Gauss white noise.

scholarly journals Neutral temperature and atmospheric water vapour retrieval from spectral fitting of auroral and airglow emissions

2018 ◽  
Author(s):  
Joshua M. Chadney ◽  
Daniel K. Whiter
Keyword(s):  
Water Vapour ◽  
Atmospheric Parameter ◽  
Atmospheric Parameters ◽  
Atmospheric Water ◽  
Echelle Spectrograph ◽  
Atmospheric Water Vapour ◽  
Spectral Fitting ◽  
Fitting Method ◽  
Airglow Emissions ◽  
High Throughput Imaging

Abstract. We have developed a spectral fitting method to retrieve upper atmospheric parameters at multiple altitudes simultaneously during times of aurora, allowing us to measure neutral temperatures and column densities of water vapour. We use the method to separate airglow OH emissions from auroral O+ and N2 in observations between 725–740 nm using the High Throughput Imaging Echelle Spectrograph (HiTIES), located on Svalbard. In this paper, we describe our new method and show the results of Monte-Carlo simulations using synthetic spectra which demonstrate the validity of the spectral fitting method as well as provide an indication of uncertainties on the retrieval of each atmospheric parameter.

scholarly journals Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

2015 ◽  
Vol 2015 ◽  
pp. 1-29 ◽  
Author(s):  
Elena Vildjiounaite ◽  
Georgy Gimel’farb ◽  
Vesa Kyllönen ◽  
Johannes Peltola
Keyword(s):  
Domain Knowledge ◽  
Computer Applications ◽  
Training Data ◽  
Design Time ◽  
Classifier Adaptation ◽  
Application Characteristics ◽  
Definition Of ◽  
Context Specific ◽  
Application Developers ◽  
Intelligent Computer

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairlylightweightways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.

Robust polynomial fitting method for regional gravity estimation

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 80-89 ◽  
Author(s):  
J. F. Beltrão ◽  
J. B. C. Silva ◽  
J. C. Costa
Keyword(s):  
Gravity Data ◽  
Crustal Thickening ◽  
Polynomial Fitting ◽  
Shallow Subsurface ◽  
Residual Field ◽  
Fitting Method ◽  
Definition Of ◽  
Regional Gravity ◽  
Lower Crustal ◽  
Polynomial Surface

Standard polynomial fitting methods are inconsistent in their formulation. The regional field is approximated by a polynomial fitted to the observed field. As a result, in addition to the nonuniqueness in the definition of the regional field, the fitted polynomial is strongly influenced by the residual field (observed field minus regional field). We present a regional‐residual separation method for gravity data which uses a robust procedure to determine the coefficients of a polynomial fitted to the observations. Under the hypothesis that the regional can be modeled correctly by the polynomial surface, the proposed method minimizes the influence of the residual field in the fitted surface. The proposed method was applied to real gravity data from Ceará state, Brazil, and produced information on zones of possible crustal thickening and the occurrence of lower‐crustal granulitic rocks thrust into the shallow subsurface.

scholarly journals Drought exerts a greater influence than growth temperature on the temperature response of leaf day respiration in wheat (Triticum aestivum)

2021 ◽  
Author(s):  
Liang Fang ◽  
Xinyou Yin ◽  
Peter E. L. van der Putten ◽  
Pierre Martre ◽  
Paul C. Struik
Keyword(s):  
Climate Change ◽  
Triticum Aestivum ◽  
Chlorophyll Fluorescence ◽  
Gas Exchange ◽  
Growth Temperature ◽  
Temperature Response ◽  
Popular Method ◽  
Water Regimes ◽  
Fluorescence Measurements ◽  
Fitting Method

We assessed how the temperature response of leaf day respiration (Rd) in wheat responded to contrasting water regimes and growth temperatures. In Experiment 1, well-watered and drought-stressed conditions were imposed on two genotypes; in Experiment 2, the two water regimes combined with high (HT), medium (MT) and low (LT) growth temperatures were imposed on one of the genotypes. Rd was estimated from simultaneous gas exchange and chlorophyll fluorescence measurements at six leaf temperatures (Tleaf) for each treatment, using the Yin method for non-photorespiratory conditions and the non-rectangular hyperbolic fitting method for photorespiratory conditions. The two genotypes responded similarly to growth and measurement conditions. Estimates of Rd for non-photorespiratory conditions were generally higher than those for photorespiratory conditions but their responses to Tleaf were similar. Under well-watered conditions, Rd and its sensitivity to Tleaf slightly acclimated to LT but did not acclimate to HT. Temperature sensitivities of Rd were considerably suppressed by drought, and the suppression varied among growth temperatures. Thus, it is necessary to quantify interactions between drought and growth temperature for reliably modelling Rd under climate change. Our study also demonstrated that the Kok method, a currently popular method for estimating Rd, underestimated Rd significantly and should be abandoned.

scholarly journals Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun–Induced Chlorophyll Fluorescence

2018 ◽  
Vol 10 (10) ◽  
pp. 1551 ◽  
Author(s):  
Neus Sabater ◽  
Jorge Vicent ◽  
Luis Alonso ◽  
Jochem Verrelst ◽  
Elizabeth Middleton ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Spectral Resolution ◽  
Transfer Functions ◽  
Spectral Response ◽  
High Spectral Resolution ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Proximal Sensing ◽  
Field Station ◽  
Absorption Features

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O 2 ) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument’s spectral resolution. We demonstrate that for proximal–sensing scenarios compensating for atmospheric effects by simply introducing the O 2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O 2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O 2 absorption on the evaluated proximal–sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one–year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature–dependent physiological responses of vegetation.

scholarly journals Modeling generalization and specialization with Extended Conceptual Graphs

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Erika Baksa-Varga ◽  
László Kovács
Keyword(s):  
Training Data ◽  
Final Conclusion ◽  
Conceptual Graphs ◽  
Test Environment ◽  
Common Generalization ◽  
Domain Specific ◽  
Definition Of ◽  
Type Lattice ◽  
The Given ◽  
Statistical Rule

AbstractThe final goal of our research is to show that the performance of statistical rule induction can be improved by augmenting training data with semantic information. In order to prove this hypothesis, a statistical grammar induction system is to be created the knowledge base of which is represented by Extended Conceptual Graphs (ECGs). Since generalization and specialization are the basic operations of induction, they are of great significance in machine learning. As a consequence, the paper aims at investigating the least common generalization and the greatest common specialization of two ECG graphs. These operations should be traced back to the examination of ECG graph element instances. For this reason, a domain-specific ECG element instance type lattice (T′,≺) has been generated for the given test environment. Our final conclusion is that the least common generalization and the greatest common specialization of two ECG graphs always exist and can be computed. Therefore, the definition of the ≺ relation on element instances can be extended to a partial relation ⪯ on ECG diagram graphs, according to which F 1 ⪯ F 2 if graph Γ 1 is more specialized than Γ 2.

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