Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

The radial basis function （RBF） emerged as a variant of artificial neural network. Generalized regression neural network （GRNN） is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance （350 to 2500 nm） data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density （45 plants m^-2）. Stepwise multivariable regression model （SMR） and RBF were used to compare their predictability for the leaf area index （LAI） and green leaf chlorophyll density （GLCD） of rice based on reflectance （R） and its three different transformations, the first derivative reflectance （D1）, the second derivative reflectance （D2） and the log-transformed reflectance （LOG）. GRNN based on D1 was the best model for the prediction of rice LAI and CLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.

Sensitivity of X-Band (σ0, γ) and Optical (NDVI) Satellite Data to Corn Biophysical Parameters

The objective of this work was to evaluate the sensitivity of three different satellite signals (interferometric coherence (γ), backscattering coefficient (σ0) and NDVI) to corn biophysical parameters (leaf area index, height, biomass and water content) throughout its entire vegetation cycle. All of the satellite and in situ data were collected during the Multi-spectral Crop Monitoring (MCM’10) experiment conducted in 2010 by the CESBIO Laboratory over eight different agricultural sites located in southwestern France. The results demonstrated that the NDVI is well adapted for leaf area index monitoring, whereas γ27.3° is much more suited to the estimation of the three other Biophysical Parameters throughout the entire crop cycle, with a coefficient of determination ranging from 0.83 to 0.99, using non-linear relationships. Moreover, contrary to the use of the NDVI or backscattering coefficients, the use of coherence exhibited a low sensitivity to the changes in vegetation and soil moisture occurring during senescence, offering interesting perspectives in the domain of applied remote sensing

Study of the Impact of the Patient Rhythm during Cryoballoon Ablation on the Acute Biophysical Parameters

Background: Pulmonary vein isolation by means of cryoballoon is a well-established way of treatment of atrial fibrillation. The aim of the study was to compare the acute cryoballoon biophysical parameters attained during energy applications to the individual pulmonary vein during sinus rhythm versus atrial fibrillation. Methods: 100 Patients who underwent their first-time PVI using second-generation cryoballoon for symptomatic and drug-refractory AF, between the beginning of March to end of August 2016, were initially screened. 61 patients with paroxysmal AF were included in the present study. 39 patients with persistent AF were excluded. No pre-procedural anatomical imaging was reported. Results: A total of 61 patients (male 80%, age 59.3 ± 13.4 years) were included in the present analysis. A total of 243 pulmonary veins were isolated with an average of 1.87 ± 1.14 cryo energy applications per individual vein. During cryo application, there were no significant differences between applications delivered during sinus rhythm or ongoing AF in the rate of temperature drop at 5 and 30 s, rate of warming at 5 s after freezing stop or achieved balloon nadir temperature. The same also was observed for both the balloon cooling rate and warming times. Conclusions: The present analysis shows no impact of the patient baseline rhythm at the time of energy application upon the acute balloon biophysical parameters in patients with normal sinus rhythm and those with ongoing atrial fibrillation using the second-generation cryoballoon.

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.

Identification of Optimal Hyperspectral Bands for Estimation of Rice Biophysical Parameters

The present study aims to identify the narrow spectral bands that are most suitable for characterizing rice biophysical parameters. The data used for this study come from ground-level hyperspectral reflectance measurements for five rice species at three levels of nitrogen fertilization during the growing period. Reflectance was measured in discrete narrow bands between 350 and 2 500 nm. Observed rice biophysical parameters included leaf area index （LAI）, wet biomass and dry biomass. The stepwise regression method was applied to identify the optimal bands for rice biophysical parameter estimation. This research indicated that combinations of four narrow bands in stepwise regression models explained 69% to 83% variability for LAI, 56% to 73% for aboveground wet biomass and 70% to 83% for leaf wet biomass. An overwhelming proportion of rice information was in a particular portion of near infrared （NIR） （1 100-1 150 nm）, red-edge （700-750 nm）, and a longer portion of green （550-600 nm）. These were followed by the moisture-sensitive NIR （950-1 000 nm）, the intermediate portion of shortwave infrared （SWlR） （1 650-1 700 nm）, and another portion of NIR （1 000-1 050 nm）.

Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service

Currently,many soil erosion studies at local,regional,national or continental scale use models based on the USLE-family approaches.Applications of these models pay little attention to seasonal changes,despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology.This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing.The latter,together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000.This potential service has led to the establishment of a new modelling approach(called the G2 model)based on the inheritance of USLE-family models.The G2 model proposes innovative techniques for the estimation of vegetation and protection factors.The model has been applied in a 14,500 km 2 study area in SE Europe covering a major part of the basin of the cross-border river,Strymonas.Model results were verified with erosion and sedimentation figures from previous research.The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.

Evolutionary history shapes variation of wood density of tree species across the world

The effect of evolutionary history on wood density variation may play an important role in shaping variation in wood density,but this has largely not been tested.Using a comprehensive global dataset including 27,297 measurements of wood density from 2621 tree species worldwide,we test the hypothesis that the legacy of evolutionary history plays an important role in driving the variation of wood density among tree species.We assessed phylogenetic signal in different taxonomic(e.g.,angiosperms and gymnosperms)and ecological(e.g.,tropical,temperate,and boreal)groups of tree species,explored the biogeographical and phylogenetic patterns of wood density,and quantified the relative importance of current environmental factors(e.g.,climatic and soil variables)and evolutionary history(i.e.,phylogenetic relatedness among species and lineages)in driving global wood density variation.We found that wood density displayed a significant phylogenetic signal.Wood density differed among different biomes and climatic zones,with higher mean values of wood density in relatively drier regions(highest in subtropical desert).Our study revealed that at a global scale,for angiosperms and gymnosperms combined,phylogeny and species(representing the variance explained by taxonomy and not direct explained by long-term evolution process)explained 84.3%and 7.7%of total wood density variation,respectively,whereas current environment explained 2.7%of total wood density variation when phylogeny and species were taken into account.When angiosperms and gymnosperms were considered separately,the three proportions of explained variation are,respectively,84.2%,7.5%and 6.7%for angiosperms,and 45.7%,21.3%and 18.6%for gymnosperms.Our study shows that evolutionary history outpaced current environmental factors in shaping global variation in wood density.

Evaluation and Exploitation of Retrieval Algorithms for Estimating Biophysical Crop Variables Using Sentinel-2,Venus,and PRISMA Satellite Data

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite(e.g.,PRISMA,EnMAP,and GF-5).Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index(LAI)and Leaf chlorophyll content(Cab)were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms(MLRA);Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season.Results show that for Sentinel-2 data,Gaussian Process Regression(GPR)was the best performing algorithm for both LAI(R 2=0.89 and RMSE=0.59)and Cab(R 2=0.70 and RMSE=8.31).Whereas,for PRISMA simulated data the Kernel Ridge Regression(KRR)was the best performing algorithm among all the other MLRA(R 2=0.91 and RMSE=0.51)for LAI and(R 2=0.83 and RMSE=6.09)for Cab,respectively.Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands,which are used as tie-points in the PROSAIL inversion,are extremely useful for an accurate retrieving of crop biophysical parameters.