1,248 research outputs found
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
In this contribution, we investigate the potential of hyperspectral data
combined with either simulated ground penetrating radar (GPR) or simulated
(sensor-like) soil-moisture data to estimate soil moisture. We propose two
simulation approaches to extend a given multi-sensor dataset which contains
sparse GPR data. In the first approach, simulated GPR data is generated either
by an interpolation along the time axis or by a machine learning model. The
second approach includes the simulation of soil-moisture along the GPR profile.
The soil-moisture estimation is improved significantly by the fusion of
hyperspectral and GPR data. In contrast, the combination of simulated,
sensor-like soil-moisture values and hyperspectral data achieves the worst
regression performance. In conclusion, the estimation of soil moisture with
hyperspectral and GPR data engages further investigations.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C)
2018 IEE
Raffinose in Chloroplasts is Synthesized in the Cytosol and Transported across the Chloroplast Envelope
In chloroplasts, several water-soluble carbohydrates have been suggested to act as stress protectants. The trisaccharide raffinose (α-1,6-galactosyl sucrose) is such a carbohydrate but has received little attention. We here demonstrate by compartmentation analysis of leaf mesophyll protoplasts that raffinose is clearly (to about 20%) present in chloroplasts of cold-treated common bugle (Ajuga reptans L.), spinach (Spinacia oleracea L.) and Arabidopsis [Arabidopsis thaliana (L.) Heynh.] plants. The two dedicated enzymes needed for raffinose synthesis, galactinol synthase and raffinose synthase, were found to be extra-chloroplastic (probably cytosolic) in location, suggesting that the chloroplast envelope contains a raffinose transporter. Uptake experiments with isolated Ajuga and Arabidopsis chloroplasts clearly demonstrated that raffinose is indeed transported across the chloroplast envelope by a raffinose transporter, probably actively. Raffinose uptake into Ajuga chloroplasts was a saturable process with apparent Km and vmax values of 27.8 mM and 3.3 μmol mg−1 Chl min−1, respectivel
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
Soil texture is important for many environmental processes. In this paper, we
study the classification of soil texture based on hyperspectral data. We
develop and implement three 1-dimensional (1D) convolutional neural networks
(CNN): the LucasCNN, the LucasResNet which contains an identity block as
residual network, and the LucasCoordConv with an additional coordinates layer.
Furthermore, we modify two existing 1D CNN approaches for the presented
classification task. The code of all five CNN approaches is available on GitHub
(Riese, 2019). We evaluate the performance of the CNN approaches and compare
them to a random forest classifier. Thereby, we rely on the freely available
LUCAS topsoil dataset. The CNN approach with the least depth turns out to be
the best performing classifier. The LucasCoordConv achieves the best
performance regarding the average accuracy. In future work, we can further
enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include
additional variables of the rich LUCAS dataset.Comment: Accepted to the ISPRS Geospatial Week 2019 in Enschede (NL
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset
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